
@inproceedings{dufumier_integrating_2023,
	title = {Integrating {Prior} {Knowledge} in {Contrastive} {Learning} with {Kernel}},
	booktitle = {International {Conference} on {Machine} {Learning} ({ICML})},
	author = {Dufumier, Benoit and Barbano, Carlo Alberto and Louiset, Robin and Duchesnay, Edouard and Gori, Pietro},
	year = {2023}
}


@inproceedings{dufumier_manuscript_2023,
	title = {Representation learning in neuroimaging               Transferring from big healthy data to small clinical cohorts},
	booktitle = {Thesis Manuscript},
	author = {Dufumier, Benoit},
	year = {2023}
}

@inproceedings{barbano_unbiased_2023,
	title = {Unbiased {Supervised} {Contrastive} {Learning}},
	booktitle = {International {Conference} on {Learning} {Representations} ({ICLR})},
	author = {Barbano, Carlo Alberto and Dufumier, Benoit and Tartaglione, Enzo and Grangetto, Marco and Gori, Pietro},
	year = {2023}
}


@inproceedings{dufumier_contrastive_2021,
	series = {Lecture {Notes} in {Computer} {Science}},
	title = {Contrastive {Learning} with {Continuous} {Proxy} {Meta}-data for {3D} {MRI} {Classification}},
	booktitle = {Medical {Image} {Computing} and {Computer} {Assisted} {Intervention} – {MICCAI}},
	author = {Dufumier, Benoit and Gori, Pietro and Victor, Julie and Grigis, Antoine and Duchesnay, Edouard},
	year = {2021},
	pages = {58--68}
}


@inproceedings{von_kugelgen_self-supervised_2021,
	title = {Self-{Supervised} {Learning} with {Data} {Augmentations} {Provably} {Isolates} {Content} from {Style}},
	abstract = {Self-supervised representation learning has shown remarkable success in a number of domains. A common practice is to perform data augmentation via hand-crafted transformations intended to leave the semantics of the data invariant. We seek to understand the empirical success of this approach from a theoretical perspective. We formulate the augmentation process as a latent variable model by postulating a partition of the latent representation into a content component, which is assumed invariant to augmentation, and a style component, which is allowed to change. Unlike prior work on disentanglement and independent component analysis, we allow for both nontrivial statistical and causal dependencies in the latent space. We study the identifiability of the latent representation based on pairs of views of the observations and prove sufficient conditions that allow us to identify the invariant content partition up to an invertible mapping in both generative and discriminative settings. We find numerical simulations with dependent latent variables are consistent with our theory. Lastly, we introduce Causal3DIdent, a dataset of high-dimensional, visually complex images with rich causal dependencies, which we use to study the effect of data augmentations performed in practice.},
	booktitle = {{NeurIPS}},
	author = {von Kügelgen, Julius and Sharma, Yash and Gresele, Luigi and Brendel, Wieland and Schölkopf, Bernhard and Besserve, Michel and Locatello, Francesco},
	year = {2021}
}

@inproceedings{khemakhem_variational_2020,
	title = {Variational {Autoencoders} and {Nonlinear} {ICA}: {A} {Unifying} {Framework}},
	shorttitle = {Variational {Autoencoders} and {Nonlinear} {ICA}},
	abstract = {The framework of variational autoencoders allows us to efficiently learn deep latent-variable models, such that the model’s marginal distribution over observed variables fits the data. Often, we’re interested in going a step further, and want to approximate the true joint distribution over observed and latent variables, including the true prior and posterior distributions over latent variables. This is known to be generally impossible due to unidentifiability of the model. We address this issue by showing that for a broad family of deep latent-variable models, identification of the true joint distribution over observed and latent variables is actually possible up to  very simple transformations, thus achieving a principled and powerful form of disentanglement. Our result requires a factorized prior distribution over the latent variables that is conditioned on an additionally observed variable, such as a class label or almost any other observation. We build on recent developments in nonlinear ICA, which we extend to the case with noisy, undercomplete or discrete observations, integrated in a maximum likelihood framework. The result also trivially contains identifiable flow-based generative models as a special case.},
	booktitle = { International Conference on Artificial Intelligence and Statistics (AISTATS)},
	author = {Khemakhem, Ilyes and Kingma, Diederik and Monti, Ricardo and Hyvarinen, Aapo},
	year = {2020},
	file = {Full Text PDF:/Users/pgori/Zotero/storage/YLGBTPVC/Khemakhem et al. - 2020 - Variational Autoencoders and Nonlinear ICA A Unif.pdf:application/pdf},
}

@article{zheng_disentangling_2019,
	title = {Disentangling {Latent} {Space} for {VAE} by {Label} {Relevant}/{Irrelevant} {Dimensions}},
	abstract = {VAE requires the standard Gaussian distribution as a prior in the latent space. Since all codes tend to follow the same prior, it often suffers the so-called ”posterior collapse”. To avoid this, this paper introduces the class speciﬁc distribution for the latent code. But different from cVAE, we present a method for disentangling the latent space into the label relevant and irrelevant dimensions, zs and zu, for a single input. We apply two separated encoders to map the input into zs and zu respectively, and then give the concatenated code to the decoder to reconstruct the input. The label irrelevant code zu represent the common characteristics of all inputs, hence they are constrained by the standard Gaussian, and their encoder is trained in amortized variational inference way, like VAE. While zs is assumed to follow the Gaussian mixture distribution in which each component corresponds to a particular class. The parameters for the Gaussian components in zs encoder are optimized by the label supervision in a global stochastic way. In theory, we show that our method is actually equivalent to adding a KL divergence term on the joint distribution of zs and the class label c, and it can directly increase the mutual information between zs and the label c. Our model can also be extended to GAN by adding a discriminator in the pixel domain so that it produces high quality and diverse images.},
	language = {en},
	urldate = {2020-11-04},
	journal = {Computer Vision and Pattern Recognition - CVPR 2019},
	author = {Zheng, Zhilin and Sun, Li},
	month = mar,
	year = {2019},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
	annote = {Comment: Accepted by CVPR 2019},
	file = {Zheng et Sun - 2019 - Disentangling Latent Space for VAE by Label Releva.pdf:/home/robin/snap/zotero-snap/common/Zotero/storage/IBVRQYSG/Zheng et Sun - 2019 - Disentangling Latent Space for VAE by Label Releva.pdf:application/pdf},
}

@article{chen_isolating_2019,
	title = {Isolating {Sources} of {Disentanglement} in {Variational} {Autoencoders}},
	abstract = {We decompose the evidence lower bound to show the existence of a term measuring the total correlation between latent variables. We use this to motivate the β-TCVAE (Total Correlation Variational Autoencoder) algorithm, a reﬁnement and plug-in replacement of the β-VAE for learning disentangled representations, requiring no additional hyperparameters during training. We further propose a principled classiﬁer-free measure of disentanglement called the mutual information gap (MIG). We perform extensive quantitative and qualitative experiments, in both restricted and non-restricted settings, and show a strong relation between total correlation and disentanglement, when the model is trained using our framework.},
	language = {en},
	urldate = {2020-11-03},
	journal = {32nd Conference on Neural Information Processing Systems (NeurIPS 2018)},
	author = {Chen, Ricky T. Q. and Li, Xuechen and Grosse, Roger and Duvenaud, David},
	month = apr,
	year = {2019},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning, Computer Science - Artificial Intelligence},
	file = {Chen et al. - 2019 - Isolating Sources of Disentanglement in Variationa.pdf:/home/robin/snap/zotero-snap/common/Zotero/storage/D3GG2NHR/Chen et al. - 2019 - Isolating Sources of Disentanglement in Variationa.pdf:application/pdf},
}

@inproceedings{higgins_beta-vae_2017,
	title = {beta-{VAE}: {Learning} {Basic} {Visual} {Concepts} with a {Constrained} {Variational} {Framework}},
	shorttitle = {beta-{VAE}},
	abstract = {Learning an interpretable factorised representation of the independent data generative factors of the world without supervision is an important precursor for the development of artificial intelligence that is able to learn and reason in the same way that humans do. We introduce beta-VAE, a new state-of-the-art framework for automated discovery of interpretable factorised latent representations from raw image data in a completely unsupervised manner. Our approach is a modification of the variational autoencoder (VAE) framework. We introduce an adjustable hyperparameter beta that balances latent channel capacity and independence constraints with reconstruction accuracy. We demonstrate that beta-VAE with appropriately tuned beta {\textgreater} 1 qualitatively outperforms VAE (beta = 1), as well as state of the art unsupervised (InfoGAN) and semi-supervised (DC-IGN) approaches to disentangled factor learning on a variety of datasets (celebA, faces and chairs). Furthermore, we devise a protocol to quantitatively compare the degree of disentanglement learnt by different models, and show that our approach also significantly outperforms all baselines quantitatively. Unlike InfoGAN, beta-VAE is stable to train, makes few assumptions about the data and relies on tuning a single hyperparameter, which can be directly optimised through a hyper parameter search using weakly labelled data or through heuristic visual inspection for purely unsupervised data.},
	booktitle = {{International Conference on Learning Representations}},
	author = {Higgins, I. and Matthey, Loïc and Pal, A. and Burgess, C. and Glorot, Xavier and Botvinick, M. and Mohamed, S. and Lerchner, Alexander},
	year = {2017},
	keywords = {Variational Auto Encoder},
}

@article{burgess_understanding_2018,
	title = {Understanding disentangling in $\beta$-{VAE}},
	abstract = {We present new intuitions and theoretical assessments of the emergence of disentangled representation in variational autoencoders. Taking a rate-distortion theory perspective, we show the circumstances under which representations aligned with the underlying generative factors of variation of data emerge when optimising the modiﬁed ELBO bound in β-VAE, as training progresses. From these insights, we propose a modiﬁcation to the training regime of β-VAE, that progressively increases the information capacity of the latent code during training. This modiﬁcation facilitates the robust learning of disentangled representations in β-VAE, without the previous trade-off in reconstruction accuracy.},
	language = {en},
	urldate = {2020-11-03},
	journal = {Neural Information Processing Systems 2017: Workshop on Learning Disentangled Representations},
	author = {Burgess, Christopher P. and Higgins, Irina and Pal, Arka and Matthey, Loic and Watters, Nick and Desjardins, Guillaume and Lerchner, Alexander},
	month = apr,
	year = {2018},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning, Computer Science - Artificial Intelligence},
	file = {Burgess et al. - 2018 - Understanding disentangling in \$beta\$-VAE.pdf:/home/robin/snap/zotero-snap/common/Zotero/storage/JSDXBLB6/Burgess et al. - 2018 - Understanding disentangling in \$beta\$-VAE.pdf:application/pdf},
}

@article{shu_rethinking_2018,
	title = {Rethinking Style and Content Disentanglement in Variational Auto Encoders},
	abstract = {A common test for whether a generative model learns disentangled representations is its ability to learn style and content as independent factors of variation on digit datasets. To achieve such disentanglement with variational autoencoders, the label information is often provided in either a fully-supervised or semi-supervised fashion. We show, however, that the variational objective is insufﬁcient in explaining the observed style and content disentanglement. Furthermore, we present an empirical framework to systematically evaluate the disentanglement behavior of our models. We show that the encoder and decoder independently favor disentangled representations and that this tendency depends on the implicit regularization by stochastic gradient descent.},
	language = {en},
	author = {Shu, Rui and Zhao, Shengjia and Kochenderfer, Mykel J},
	year = {2018},
    journal = {International Conference on Learning Representations - ICLR (Workshop)},
	file = {Shu et al. - 2018 - RETHINKING STYLE AND CONTENT DISENTANGLE- MENT IN .pdf:/home/robin/snap/zotero-snap/common/Zotero/storage/D586ZLHZ/Shu et al. - 2018 - RETHINKING STYLE AND CONTENT DISENTANGLE- MENT IN .pdf:application/pdf},
}

@article{li_disentangled_2018,
	title = {Disentangled {Variational} {Auto}-{Encoder} for {Semi}-supervised {Learning}},
	abstract = {Semi-supervised learning is attracting increasing attention due to the fact that datasets of many domains lack enough labeled data. Variational Auto-Encoder (VAE), in particular, has demonstrated the beneﬁts of semi-supervised learning. The majority of existing semi-supervised VAEs utilize a classiﬁer to exploit label information, where the parameters of the classiﬁer are introduced to the VAE. Given the limited labeled data, learning the parameters for the classiﬁers may not be an optimal solution for exploiting label information. Therefore, in this paper, we develop a novel approach for semi-supervised VAE without classiﬁer. Speciﬁcally, we propose a new model called Semi-supervised Disentangled VAE (SDVAE), which encodes the input data into disentangled representation and non-interpretable representation, then the category information is directly utilized to regularize the disentangled representation via the equality constraint. To further enhance the feature learning ability of the proposed VAE, we incorporate reinforcement learning to relieve the lack of data. The dynamic framework is capable of dealing with both image and text data with its corresponding encoder and decoder networks. Extensive experiments on image and text datasets demonstrate the eﬀectiveness of the proposed framework.},
	language = {en},
	urldate = {2022-03-01},
	journal = {arXiv:1709.05047 [cs]},
	author = {Li, Yang and Pan, Quan and Wang, Suhang and Peng, Haiyun and Yang, Tao and Cambria, Erik},
	month = dec,
	year = {2018},
	keywords = {Computer Science - Machine Learning, Computer Science - Artificial Intelligence},
	annote = {Comment: 6 figures, 10 pages, Information Sciences 2018},
	file = {Li et al. - 2018 - Disentangled Variational Auto-Encoder for Semi-sup.pdf:/home/robin/snap/zotero-snap/common/Zotero/storage/QP8RMLMG/Li et al. - 2018 - Disentangled Variational Auto-Encoder for Semi-sup.pdf:application/pdf},
}

@article{joy_capturing_2021,
	title = {Capturing {Label} {Characteristics} in {VAEs}},
	abstract = {We present a principled approach to incorporating labels in variational autoencoders (VAEs) that captures the rich characteristic information associated with those labels. While prior work has typically conﬂated these by learning latent variables that directly correspond to label values, we argue this is contrary to the intended effect of supervision in VAEs—capturing rich label characteristics with the latents. For example, we may want to capture the characteristics of a face that make it look young, rather than just the age of the person. To this end, we develop the characteristic capturing VAE (CCVAE), a novel VAE model and concomitant variational objective which captures label characteristics explicitly in the latent space, eschewing direct correspondences between label values and latents. Through judicious structuring of mappings between such characteristic latents and labels, we show that the CCVAE can effectively learn meaningful representations of the characteristics of interest across a variety of supervision schemes. In particular, we show that the CCVAE allows for more effective and more general interventions to be performed, such as smooth traversals within the characteristics for a given label, diverse conditional generation, and transferring characteristics across datapoints.},
	language = {en},
	urldate = {2021-09-02},
	journal = {ICLR 2021},
	author = {Joy, Tom and Schmon, Sebastian M. and Torr, Philip H. S. and Siddharth, N. and Rainforth, Tom},
	month = jun,
	year = {2021},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
	file = {Joy et al. - 2021 - Capturing Label Characteristics in VAEs.pdf:/home/robin/snap/zotero-snap/common/Zotero/storage/NS6S9W3Y/Joy et al. - 2021 - Capturing Label Characteristics in VAEs.pdf:application/pdf},
}

@misc{kim_disentangling_2019,
	title = {Disentangling by {Factorising}},
	abstract = {We deﬁne and address the problem of unsupervised learning of disentangled representations on data generated from independent factors of variation. We propose FactorVAE, a method that disentangles by encouraging the distribution of representations to be factorial and hence independent across the dimensions. We show that it improves upon β-VAE by providing a better trade-off between disentanglement and reconstruction quality. Moreover, we highlight the problems of a commonly used disentanglement metric and introduce a new metric that does not suffer from them.},
	language = {en},
	urldate = {2023-01-05},
	author = {Kim, Hyunjik and Mnih, Andriy},
        note = {Proceedings of the 35th International Conference on Machine Learning, PMLR 80:2649-2658, 2018},
	month = jul,
	year = {2019},
	journal = {Advances on Neural Information Processing Signals - NeurIPS},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
}

@article{kingma_semi-supervised_nodate,
	title = {Semi-supervised {Learning} with {Deep} {Generative} {Models}},
	abstract = {The ever-increasing size of modern data sets combined with the difﬁculty of obtaining label information has made semi-supervised learning one of the problems of signiﬁcant practical importance in modern data analysis. We revisit the approach to semi-supervised learning with generative models and develop new models that allow for effective generalisation from small labelled data sets to large unlabelled ones. Generative approaches have thus far been either inﬂexible, inefﬁcient or non-scalable. We show that deep generative models and approximate Bayesian inference exploiting recent advances in variational methods can be used to provide signiﬁcant improvements, making generative approaches highly competitive for semi-supervised learning.},
	language = {en},
	author = {Kingma, Durk P and Mohamed, Shakir and Rezende, Danilo Jimenez and Welling, Max},
	file = {Kingma et al. - Semi-supervised Learning with Deep Generative Mode.pdf:/home/robin/snap/zotero-snap/common/Zotero/storage/B9LF8GEB/Kingma et al. - Semi-supervised Learning with Deep Generative Mode.pdf:application/pdf},
}

@inproceedings{maaloe_auxiliary_2016,
	title = {Auxiliary {Deep} {Generative} {Models}},
	abstract = {Deep generative models parameterized by neural networks have recently achieved state-of-the-art performance in unsupervised and semi-supervised learning. We extend deep generative models with auxiliary variables which improves the variational approximation. The auxiliary variables leave the generative model unchanged but make the variational distribution more expressive. Inspired by the structure of the auxiliary variable we also propose a model with two stochastic layers and skip connections. Our findings suggest that more expressive and properly specified deep generative models converge faster with better results. We show state-of-the-art performance within semi-supervised learning on MNIST, SVHN and NORB datasets.},
	language = {en},
	urldate = {2023-01-05},
	booktitle = {Proceedings of {The} 33rd {International} {Conference} on {Machine} {Learning}},
	publisher = {PMLR},
	author = {Maaløe, Lars and Sønderby, Casper Kaae and Sønderby, Søren Kaae and Winther, Ole},
	month = jun,
	year = {2016},
	pages = {1445--1453},
	file = {Full Text PDF:/home/robin/snap/zotero-snap/common/Zotero/storage/7GGGAHD9/Maaløe et al. - 2016 - Auxiliary Deep Generative Models.pdf:application/pdf},
}

@inproceedings{locatello_challenging_2019,
	title = {Challenging {Common} {Assumptions} in the {Unsupervised} {Learning} of {Disentangled} {Representations}},
	url = {https://proceedings.mlr.press/v97/locatello19a.html},
	abstract = {The key idea behind the unsupervised learning of disentangled representations is that real-world data is generated by a few explanatory factors of variation which can be recovered by unsupervised learning algorithms. In this paper, we provide a sober look at recent progress in the field and challenge some common assumptions. We first theoretically show that the unsupervised learning of disentangled representations is fundamentally impossible without inductive biases on both the models and the data. Then, we train more than 120001200012000 models covering most prominent methods and evaluation metrics in a reproducible large-scale experimental study on seven different data sets. We observe that while the different methods successfully enforce properties “encouraged” by the corresponding losses, well-disentangled models seemingly cannot be identified without supervision. Furthermore, increased disentanglement does not seem to lead to a decreased sample complexity of learning for downstream tasks. Our results suggest that future work on disentanglement learning should be explicit about the role of inductive biases and (implicit) supervision, investigate concrete benefits of enforcing disentanglement of the learned representations, and consider a reproducible experimental setup covering several data sets.},
	language = {en},
	urldate = {2023-01-05},
	booktitle = {Proceedings of the 36th {International} {Conference} on {Machine} {Learning}},
	publisher = {PMLR},
	author = {Locatello, Francesco and Bauer, Stefan and Lucic, Mario and Raetsch, Gunnar and Gelly, Sylvain and Schölkopf, Bernhard and Bachem, Olivier},
	month = may,
	year = {2019},
	pages = {4114--4124},
	file = {Full Text PDF:/home/robin/snap/zotero-snap/common/Zotero/storage/VXESVEIB/Locatello et al. - 2019 - Challenging Common Assumptions in the Unsupervised.pdf:application/pdf;Supplementary PDF:/home/robin/snap/zotero-snap/common/Zotero/storage/K75HF8LH/Locatello et al. - 2019 - Challenging Common Assumptions in the Unsupervised.pdf:application/pdf},
}

@inproceedings{mathieu_disentangling_2019,
	title = {Disentangling {Disentanglement} in {Variational} {Autoencoders}},
	abstract = {We develop a generalisation of disentanglement in variational autoencoders (VAEs)—decomposition of the latent representation—characterising it as the fulfilment of two factors: a) the latent encodings of the data having an appropriate level of overlap, and b) the aggregate encoding of the data conforming to a desired structure, represented through the prior. Decomposition permits disentanglement, i.e. explicit independence between latents, as a special case, but also allows for a much richer class of properties to be imposed on the learnt representation, such as sparsity, clustering, independent subspaces, or even intricate hierarchical dependency relationships. We show that the ��β{\textbackslash}beta-VAE varies from the standard VAE predominantly in its control of latent overlap and that for the standard choice of an isotropic Gaussian prior, its objective is invariant to rotations of the latent representation. Viewed from the decomposition perspective, breaking this invariance with simple manipulations of the prior can yield better disentanglement with little or no detriment to reconstructions. We further demonstrate how other choices of prior can assist in producing different decompositions and introduce an alternative training objective that allows the control of both decomposition factors in a principled manner.},
	language = {en},
	urldate = {2023-01-05},
	booktitle = {Proceedings of the 36th {International} {Conference} on {Machine} {Learning}},
	publisher = {PMLR},
	author = {Mathieu, Emile and Rainforth, Tom and Siddharth, N. and Teh, Yee Whye},
	month = may,
	year = {2019},
	pages = {4402--4412},
	file = {Full Text PDF:/home/robin/snap/zotero-snap/common/Zotero/storage/T4CXLFTY/Mathieu et al. - 2019 - Disentangling Disentanglement in Variational Autoe.pdf:application/pdf;Supplementary PDF:/home/robin/snap/zotero-snap/common/Zotero/storage/A9P6HREI/Mathieu et al. - 2019 - Disentangling Disentanglement in Variational Autoe.pdf:application/pdf},
}

@article{severson_unsupervised_2019,
	title = {Unsupervised {Learning} with {Contrastive} {Latent} {Variable} {Models}},
	volume = {33},
	copyright = {Copyright (c) 2019 Association for the Advancement of Artificial Intelligence},
	abstract = {In unsupervised learning, dimensionality reduction is an important tool for data exploration and visualization. Because these aims are typically open-ended, it can be useful to frame the problem as looking for patterns that are enriched in one dataset relative to another. These pairs of datasets occur commonly, for instance a population of interest vs. control or signal vs. signal free recordings. However, there are few methods that work on sets of data as opposed to data points or sequences. Here, we present a probabilistic model for dimensionality reduction to discover signal that is enriched in the target dataset relative to the background dataset. The data in these sets do not need to be paired or grouped beyond set membership. By using a probabilistic model where some structure is shared amongst the two datasets and some is unique to the target dataset, we are able to recover interesting structure in the latent space of the target dataset. The method also has the advantages of a probabilistic model, namely that it allows for the incorporation of prior information, handles missing data, and can be generalized to different distributional assumptions. We describe several possible variations of the model and demonstrate the application of the technique to de-noising, feature selection, and subgroup discovery settings.},
	language = {en},
	number = {01},
	urldate = {2023-01-05},
	journal = {Proceedings of the AAAI Conference on Artificial Intelligence},
	author = {Severson, Kristen A. and Ghosh, Soumya and Ng, Kenney},
	month = jul,
	year = {2019},
	pages = {4862--4869},
	file = {Full Text PDF:/home/robin/snap/zotero-snap/common/Zotero/storage/JSU2L7IK/Severson et al. - 2019 - Unsupervised Learning with Contrastive Latent Vari.pdf:application/pdf},
}

@misc{abid_contrastive_2019,
	title = {Contrastive {Variational} {Autoencoder} {Enhances} {Salient} {Features}},
	abstract = {Variational autoencoders are powerful algorithms for identifying dominant latent structure in a single dataset. In many applications, however, we are interested in modeling latent structure and variation that are enriched in a target dataset compared to some background—e.g. enriched in patients compared to the general population. Contrastive learning is a principled framework to capture such enriched variation between the target and background, but state-of-the-art contrastive methods are limited to linear models. In this paper, we introduce the contrastive variational autoencoder (cVAE), which combines the beneﬁts of contrastive learning with the power of deep generative models. The cVAE is designed to identify and enhance salient latent features. The cVAE is trained on two related but unpaired datasets, one of which has minimal contribution from the salient latent features. The cVAE explicitly models latent features that are shared between the datasets, as well as those that are enriched in one dataset relative to the other, which allows the algorithm to isolate and enhance the salient latent features. The algorithm is straightforward to implement, has a similar run-time to the standard VAE, and is robust to noise and dataset purity. We conduct experiments across diverse types of data, including gene expression and facial images, showing that the cVAE effectively uncovers latent structure that is salient in a particular analysis.},
	language = {en},
	urldate = {2023-01-05},
	publisher = {arXiv},
	author = {Abid, Abubakar and Zou, James},
	month = feb,
	note = {arXiv:1902.04601 [cs, stat]},
        year = 2019,
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
	annote = {Comment: Submitted to ICML 2019},
	file = {Abid et Zou - 2019 - Contrastive Variational Autoencoder Enhances Salie.pdf:/home/robin/snap/zotero-snap/common/Zotero/storage/KUMDFCDK/Abid et Zou - 2019 - Contrastive Variational Autoencoder Enhances Salie.pdf:application/pdf},
}

@misc{ruiz_learning_2019,
	title = {Learning {Disentangled} {Representations} with {Reference}-{Based} {Variational} {Autoencoders}},
	abstract = {Learning disentangled representations from visual data, where different high-level generative factors are independently encoded, is of importance for many computer vision tasks. Solving this problem, however, typically requires to explicitly label all the factors of interest in training images. To alleviate the annotation cost, we introduce a learning setting which we refer to as reference-based disentangling. Given a pool of unlabelled images, the goal is to learn a representation where a set of target factors are disentangled from others. The only supervision comes from an auxiliary reference set containing images where the factors of interest are constant. In order to address this problem, we propose reference-based variational autoencoders, a novel deep generative model designed to exploit the weak-supervision provided by the reference set. By addressing tasks such as feature learning, conditional image generation or attribute transfer, we validate the ability of the proposed model to learn disentangled representations from this minimal form of supervision.},
	language = {en},
	urldate = {2023-01-05},
	publisher = {arXiv},
	author = {Ruiz, Adria and Martinez, Oriol and Binefa, Xavier and Verbeek, Jakob},
	month = jan,
	year = {2019},
	note = {arXiv:1901.08534 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
	file = {Ruiz et al. - 2019 - Learning Disentangled Representations with Referen.pdf:/home/robin/snap/zotero-snap/common/Zotero/storage/HHDEDVMG/Ruiz et al. - 2019 - Learning Disentangled Representations with Referen.pdf:application/pdf},
}

@article{kingma_auto-encoding_2013,
	title = {Auto-{Encoding} {Variational} {Bayes}},
	abstract = {Can we efficiently learn the parameters of directed probabilistic models, in the presence of continuous latent variables with intractable posterior distributions? We introduce an unsupervised on-line learning method that efficiently optimizes the variational lower bound on the marginal likelihood and that, under some mild conditions, even works in the intractable case. The method optimizes a probabilistic encoder (also called a recognition network) to approximate the intractable posterior distribution of the latent variables. The crucial element is a reparameterization of the variational bound with an independent noise variable, yielding a stochastic objective function which can be jointly optimized w.r.t. variational and generative parameters using standard gradient-based stochastic optimization methods. Theoretical advantages are reflected in experimental results.},
	language = {en},
        journal = {International Conference on Learning Representations - ICLR 2014},
	urldate = {2023-01-05},
	author = {Kingma, Diederik P. and Welling, Max},
	month = dec,
	year = {2013},
}

@article{wu_multimodal_nodate,
	title = {Multimodal {Generative} {Models} for {Scalable} {Weakly}-{Supervised} {Learning}},
	abstract = {Multiple modalities often co-occur when describing natural phenomena. Learning a joint representation of these modalities should yield deeper and more useful representations. Previous generative approaches to multi-modal input either do not learn a joint distribution or require additional computation to handle missing data. Here, we introduce a multimodal variational autoencoder (MVAE) that uses a product-of-experts inference network and a sub-sampled training paradigm to solve the multi-modal inference problem. Notably, our model shares parameters to efﬁciently learn under any combination of missing modalities. We apply the MVAE on four datasets and match state-of-the-art performance using many fewer parameters. In addition, we show that the MVAE is directly applicable to weaklysupervised learning, and is robust to incomplete supervision. We then consider two case studies, one of learning image transformations—edge detection, colorization, segmentation—as a set of modalities, followed by one of machine translation between two languages. We ﬁnd appealing results across this range of tasks.},
	language = {en},
	author = {Wu, Mike and Goodman, Noah},
	file = {Wu et Goodman - Multimodal Generative Models for Scalable Weakly-S.pdf:/home/robin/snap/zotero-snap/common/Zotero/storage/PAJPTEG3/Wu et Goodman - Multimodal Generative Models for Scalable Weakly-S.pdf:application/pdf},
}

@article{suzuki_joint_2022,
	title = {Joint {Multimodal} {Learning} with {Deep} {Generative} {Models}},
	url = {https://openreview.net/forum?id=BkL7bONFe},
	abstract = {We investigate deep generative models that can exchange multiple modalities bi-directionally, e.g., generating images from corresponding texts and vice versa. Recently, some studies handle multiple modalities on deep generative models. However, these models typically assume that modalities are forced to have a conditioned relation, i.e., we can only generate modalities in one direction. To achieve our objective, we should extract a joint representation that captures high-level concepts among all modalities and through which we can exchange them bi-directionally. As described herein, we propose a joint multimodal variational autoencoder (JMVAE), in which all modalities are independently conditioned on joint representation. In other words, it models a joint distribution of modalities. Furthermore, to be able to generate missing modalities from the remaining modalities properly, we develop an additional method, JMVAE-kl, that is trained by reducing the divergence between JMVAE's encoder and prepared networks of respective modalities. Our experiments show that JMVAE can generate multiple modalities bi-directionally.},
	language = {en},
	urldate = {2023-01-05},
	author = {Suzuki, Masahiro and Nakayama, Kotaro and Matsuo, Yutaka},
	month = jul,
	year = {2022},
	file = {Full Text PDF:/home/robin/snap/zotero-snap/common/Zotero/storage/DSGHBESB/Suzuki et al. - 2022 - Joint Multimodal Learning with Deep Generative Mod.pdf:application/pdf},
}


@article{kermany_identifying_2018,
	title = {Identifying {Medical} {Diagnoses} and {Treatable} {Diseases} by {Image}-{Based} {Deep} {Learning}},
	volume = {172},
	language = {English},
	number = {5},
	urldate = {2021-11-16},
	journal = {Cell},
	author = {Daniel S. Kermany et al.},
	month = feb,
	year = {2018},
	keywords = {age-related macular degeneration, artificial intelligence, choroidal neovascularization, deep learning, diabetic macular edema, diabetic retinopathy, optical coherence tomography, pneumonia, screening, transfer learning},
	pages = {1122--1131},
	file = {Full Text PDF:/home/robin/snap/zotero-snap/common/Zotero/storage/39EWUX4W/Kermany et al. - 2018 - Identifying Medical Diagnoses and Treatable Diseas.pdf:application/pdf;Snapshot:/home/robin/snap/zotero-snap/common/Zotero/storage/Y69CKD8L/S0092-8674(18)30154-5.html:text/html},
}

@inproceedings{weinberger_moment_nodate,
	title = {Moment {Matching} {Deep} {Contrastive} {Latent} {Variable} {Models}},
	abstract = {In the contrastive analysis (CA) setting, machine learning practitioners are speciﬁcally interested in discovering patterns that are enriched in a target dataset as compared to a background dataset generated from sources of variation irrelevant to the task at hand. For example, a biomedical data analyst may seek to understand variations in genomic data only present among patients with a given disease as opposed to those also present in healthy control subjects. Such scenarios have motivated the development of contrastive latent variable models to isolate variations unique to these target datasets from those shared across the target and background datasets, with current state of the art models based on the variational autoencoder (VAE) framework. However, previously proposed models do not explicitly enforce the constraints on latent variables underlying CA, potentially leading to the undesirable leakage of information between the two sets of latent variables. Here we propose the moment matching contrastive VAE (MM-cVAE), a reformulation of the VAE for CA that uses the maximum mean discrepancy to explicitly enforce two crucial latent variable constraints underlying CA. On three challenging CA tasks we ﬁnd that our method outperforms the previous state-ofthe-art both qualitatively and on a set of quantitative metrics.},
	year = {2022},
	author = {Weinberger, Ethan and Beebe-Wang, Nicasia and Lee, Su-In},
booktitle = { International Conference on Artificial Intelligence and Statistics (AISTATS)}
}

@article{abid_exploring_2018,
	title = {Exploring patterns enriched in a dataset with contrastive principal component analysis},
	volume = {9},
	copyright = {2018 The Author(s)},
	abstract = {Visualization and exploration of high-dimensional data is a ubiquitous challenge across disciplines. Widely used techniques such as principal component analysis (PCA) aim to identify dominant trends in one dataset. However, in many settings we have datasets collected under different conditions, e.g., a treatment and a control experiment, and we are interested in visualizing and exploring patterns that are specific to one dataset. This paper proposes a method, contrastive principal component analysis (cPCA), which identifies low-dimensional structures that are enriched in a dataset relative to comparison data. In a wide variety of experiments, we demonstrate that cPCA with a background dataset enables us to visualize dataset-specific patterns missed by PCA and other standard methods. We further provide a geometric interpretation of cPCA and strong mathematical guarantees. An implementation of cPCA is publicly available, and can be used for exploratory data analysis in many applications where PCA is currently used.},
	language = {en},
	number = {1},
	urldate = {2023-01-05},
	journal = {Nature Communications},
	author = {Abid, Abubakar and Zhang, Martin J. and Bagaria, Vivek K. and Zou, James},
	month = may,
	year = {2018},
	keywords = {Machine learning, Statistics, Computational biology and bioinformatics},
	pages = {2134},
	file = {Full Text PDF:/home/robin/snap/zotero-snap/common/Zotero/storage/NE49QIMX/Abid et al. - 2018 - Exploring patterns enriched in a dataset with cont.pdf:application/pdf},
}

@inproceedings{zou_contrastive_2013,
	title = {Contrastive {Learning} {Using} {Spectral} {Methods}},
	volume = {26},
	abstract = {In many natural settings, the analysis goal is not to characterize a single data set in isolation, but rather to understand the difference between one set of observations and another. For example, given a background corpus of news articles together with writings of a particular author, one may want a topic model that explains word patterns and themes specific to the author. Another example comes from genomics, in which biological signals may be collected from different regions of a genome, and one wants a model that captures the differential statistics observed in these regions. This paper formalizes this notion of contrastive learning for  mixture models, and develops spectral algorithms for inferring mixture components specific to a foreground data set when contrasted with a background data set. The method builds on recent moment-based estimators and tensor decompositions for latent variable models, and has the intuitive feature of using background data statistics to appropriately modify moments estimated from foreground data. A key advantage of the method is that the background data need only be coarsely modeled, which is important when the background is too complex, noisy, or not of interest. The method is demonstrated on applications in contrastive topic modeling and genomic sequence analysis.},
	urldate = {2023-01-05},
	booktitle = {Advances in {Neural} {Information} {Processing} {Systems}},
	publisher = {Curran Associates, Inc.},
	author = {Zou, James Y and Hsu, Daniel J and Parkes, David C and Adams, Ryan P},
	year = {2013},
	file = {Full Text PDF:/home/robin/snap/zotero-snap/common/Zotero/storage/77WYPSMN/Zou et al. - 2013 - Contrastive Learning Using Spectral Methods.pdf:application/pdf},
}

@inproceedings{ge_rich_2016,
	address = {New York, NY, USA},
	series = {{ICML}'16},
	title = {Rich component analysis},
	abstract = {In many settings, we have multiple data sets (also called views) that capture different and overlapping aspects of the same phenomenon. We are often interested in finding patterns that are unique to one or to a subset of the views. For example, we might have one set of molecular observations and one set of physiological observations on the same group of individuals, and we want to quantify molecular patterns that are uncorrelated with physiology. Despite being a common problem, this is highly challenging when the correlations come from complex distributions. In this paper, we develop the general framework of Rich Component Analysis (RCA) to model settings where the observations from different views are driven by different sets of latent components, and each component can be a complex, high-dimensional distribution. We introduce algorithms based on cumulant extraction that provably learn each of the components without having to model the other components. We show how to integrate RCA with stochastic gradient descent into a meta-algorithm for learning general models, and demonstrate substantial improvement in accuracy on several synthetic and real datasets in both supervised and unsupervised tasks. Our method makes it possible to learn latent variable models when we don't have samples from the true model but only samples after complex perturbations.},
	urldate = {2023-01-05},
	booktitle = {Proceedings of the 33rd {International} {Conference} on {International} {Conference} on {Machine} {Learning} - {Volume} 48},
	publisher = {JMLR.org},
	author = {Ge, Rong and Zou, James},
	month = jun,
	year = {2016},
	pages = {1502--1510},
}

@article{joy_capturing_2021-1,
	title = {{CAPTURING} {LABEL} {CHARACTERISTICS} {IN} {VAEs}},
	abstract = {We present a principled approach to incorporating labels in variational autoencoders (VAEs) that captures the rich characteristic information associated with those labels. While prior work has typically conﬂated these by learning latent variables that directly correspond to label values, we argue this is contrary to the intended effect of supervision in VAEs—capturing rich label characteristics with the latents. For example, we may want to capture the characteristics of a face that make it look young, rather than just the age of the person. To this end, we develop the characteristic capturing VAE (CCVAE), a novel VAE model and concomitant variational objective which captures label characteristics explicitly in the latent space, eschewing direct correspondences between label values and latents. Through judicious structuring of mappings between such characteristic latents and labels, we show that the CCVAE can effectively learn meaningful representations of the characteristics of interest across a variety of supervision schemes. In particular, we show that the CCVAE allows for more effective and more general interventions to be performed, such as smooth traversals within the characteristics for a given label, diverse conditional generation, and transferring characteristics across datapoints.},
	language = {en},
	author = {Joy, Tom and Schmon, Sebastian M and Torr, Philip H S and Siddharth, N and Rainforth, Tom},
	year = {2021},
	file = {Joy et al. - 2021 - CAPTURING LABEL CHARACTERISTICS IN VAEs.pdf:/home/robin/snap/zotero-snap/common/Zotero/storage/RKLQ4F9T/Joy et al. - 2021 - CAPTURING LABEL CHARACTERISTICS IN VAEs.pdf:application/pdf},
}

@inproceedings{ainsworth_oi-vae_2018,
	title = {oi-{VAE}: {Output} {Interpretable} {VAEs} for {Nonlinear} {Group} {Factor} {Analysis}},
	shorttitle = {oi-{VAE}},
	abstract = {Deep generative models have recently yielded encouraging results in producing subjectively realistic samples of complex data. Far less attention has been paid to making these generative models interpretable. In many scenarios, ranging from scientific applications to finance, the observed variables have a natural grouping. It is often of interest to understand systems of interaction amongst these groups, and latent factor models (LFMs) are an attractive approach. However, traditional LFMs are limited by assuming a linear correlation structure. We present an output interpretable VAE (oi-VAE) for grouped data that models complex, nonlinear latent-to-observed relationships. We combine a structured VAE comprised of group-specific generators with a sparsity-inducing prior. We demonstrate that oi-VAE yields meaningful notions of interpretability in the analysis of motion capture and MEG data. We further show that in these situations, the regularization inherent to oi-VAE can actually lead to improved generalization and learned generative processes.},
	language = {en},
	urldate = {2023-01-05},
	booktitle = {Proceedings of the 35th {International} {Conference} on {Machine} {Learning}},
	publisher = {PMLR},
	author = {Ainsworth, Samuel K. and Foti, Nicholas J. and Lee, Adrian K. C. and Fox, Emily B.},
	month = jul,
	year = {2018},
	pages = {119--128},
	file = {Full Text PDF:/home/robin/snap/zotero-snap/common/Zotero/storage/CHSVIE78/Ainsworth et al. - 2018 - oi-VAE Output Interpretable VAEs for Nonlinear Gr.pdf:application/pdf;Supplementary PDF:/home/robin/snap/zotero-snap/common/Zotero/storage/NKUXQP6J/Ainsworth et al. - 2018 - oi-VAE Output Interpretable VAEs for Nonlinear Gr.pdf:application/pdf},
}

@article{antelmi_sparse_nodate,
	title = {Sparse {Multi}-{Channel} {Variational} {Autoencoder} for the {Joint} {Analysis} of {Heterogeneous} {Data}},
	abstract = {Interpretable modeling of heterogeneous data channels is essential in medical applications, for example when jointly analyzing clinical scores and medical images. Variational Autoencoders (VAE) are powerful generative models that learn representations of complex data. The ﬂexibility of VAE may come at the expense of lack of interpretability in describing the joint relationship between heterogeneous data. To tackle this problem, in this work we extend the variational framework of VAE to bring parsimony and interpretability when jointly account for latent relationships across multiple channels. In the latent space, this is achieved by constraining the variational distribution of each channel to a common target prior. Parsimonious latent representations are enforced by variational dropout. Experiments on synthetic data show that our model correctly identiﬁes the prescribed latent dimensions and data relationships across multiple testing scenarios. When applied to imaging and clinical data, our method allows to identify the joint effect of age and pathology in describing clinical condition in a large scale clinical cohort.},
	language = {en},
	author = {Antelmi, Luigi and Ayache, Nicholas and Robert, Philippe and Lorenzi, Marco},
	file = {Antelmi et al. - Sparse Multi-Channel Variational Autoencoder for t.pdf:/home/robin/snap/zotero-snap/common/Zotero/storage/4X9ZZ4JE/Antelmi et al. - Sparse Multi-Channel Variational Autoencoder for t.pdf:application/pdf},
}

@inproceedings{zou_joint_2022,
	title = {Joint {Disentanglement} of {Labels} and {Their} {Features} with {VAE}},
	booktitle = {{IEEE} {International} {Conference} on {Image} {Processing} ({ICIP})},
	author = {Zou, Kaifeng and Faisan, Sylvain and Heitz, Fabrice and Valette, Sebastien},
	year = {2022},
	pages = {1341--1345}
}

@inproceedings{vowels_nestedvae_2020,
	title = {{NestedVAE}: {Isolating} {Common} {Factors} via {Weak} {Supervision}},
	shorttitle = {{NestedVAE}},
	url = {https://openaccess.thecvf.com/content_CVPR_2020/html/Vowels_NestedVAE_Isolating_Common_Factors_via_Weak_Supervision_CVPR_2020_paper.html},
	urldate = {2023-01-17},
	author = {Vowels, Matthew J. and Camgoz, Necati Cihan and Bowden, Richard},
	year = {2020},
	pages = {9202--9212},
	file = {Full Text PDF:/home/robin/snap/zotero-snap/common/Zotero/storage/5DVGGVC9/Vowels et al. - 2020 - NestedVAE Isolating Common Factors via Weak Super.pdf:application/pdf},
}

@article{choudhuri_towards_2019,
	title = {Towards {Principled} {Objectives} for {Contrastive} {Disentanglement}},
	abstract = {Unsupervised learning is an important tool that has received a significant amount of attention for decades. Its goal is `unsupervised recovery,' i.e., extracting salient factors/properties from unlabeled data. Because of the challenges in defining salient properties, recently, `contrastive disentanglement' has gained popularity to discover the additional variations that are enhanced in one dataset relative to another. \%In fact, contrastive disentanglement and unsupervised recovery are often combined in that we seek additional variations that exhibit salient factors/properties. Existing formulations have devised a variety of losses for this task. However, all present day methods exhibit two major shortcomings: (1) encodings for data that does not exhibit salient factors is not pushed to carry no signal; and (2) introduced losses are often hard to estimate and require additional trainable parameters. We present a new formulation for contrastive disentanglement which avoids both shortcomings by carefully formulating a probabilistic model and by using non-parametric yet easily computable metrics. We show on four challenging datasets that the proposed approach is able to better disentangle salient factors.},
	language = {en},
	urldate = {2023-01-17},
	author = {Choudhuri, Anwesa and Makkuva, Ashok Vardhan and Rana, Ranvir and Oh, Sewoong and Chowdhary, Girish and Schwing, Alexander},
	month = dec,
	year = {2019},
	file = {Full Text PDF:/home/robin/snap/zotero-snap/common/Zotero/storage/6VWE9CHZ/Choudhuri et al. - 2019 - Towards Principled Objectives for Contrastive Dise.pdf:application/pdf},
}

@inproceedings{he_bounding_2019,
	title = {Bounding {Box} {Regression} {With} {Uncertainty} for {Accurate} {Object} {Detection}},
	doi = {10.1109/CVPR.2019.00300},
	abstract = {Large-scale object detection datasets (e.g., MS-COCO) try to define the ground truth bounding boxes as clear as possible. However, we observe that ambiguities are still introduced when labeling the bounding boxes. In this paper, we propose a novel bounding box regression loss for learning bounding box transformation and localization variance together. Our loss greatly improves the localization accuracies of various architectures with nearly no additional computation. The learned localization variance allows us to merge neighboring bounding boxes during non-maximum suppression (NMS), which further improves the localization performance. On MS-COCO, we boost the Average Precision (AP) of VGG-16 Faster R-CNN from 23.6\% to 29.1\%. More importantly, for ResNet-50-FPN Mask R-CNN, our method improves the AP and AP90 by 1.8\% and 6.2\% respectively, which significantly outperforms previous state-of-the-art bounding box refinement methods. Our code and models are available at github.com/yihui-he/KL-Loss.},
	booktitle = {2019 {IEEE}/{CVF} {Conference} on {Computer} {Vision} and {Pattern} {Recognition} ({CVPR})},
	author = {He, Yihui and Zhu, Chenchen and Wang, Jianren and Savvides, Marios and Zhang, Xiangyu},
	month = jun,
	year = {2019},
	keywords = {Categorization, Recognition: Detection, Retrieval},
	pages = {2883--2892},
	file = {IEEE Xplore Full Text PDF:/home/robin/snap/zotero-snap/common/Zotero/storage/FE83ULTQ/He et al. - 2019 - Bounding Box Regression With Uncertainty for Accur.pdf:application/pdf},
}

@misc{jones_contrastive_2021,
	title = {Contrastive latent variable modeling with application to case-control sequencing experiments},
	abstract = {High-throughput RNA-sequencing (RNA-seq) technologies are powerful tools for understanding cellular state. Often it is of interest to quantify and summarize changes in cell state that occur between experimental or biological conditions. Differential expression is typically assessed using univariate tests to measure gene-wise shifts in expression. However, these methods largely ignore changes in transcriptional correlation. Furthermore, there is a need to identify the low-dimensional structure of the gene expression shift to identify collections of genes that change between conditions. Here, we propose contrastive latent variable models designed for count data to create a richer portrait of differential expression in sequencing data. These models disentangle the sources of transcriptional variation in different conditions, in the context of an explicit model of variation at baseline. Moreover, we develop a model-based hypothesis testing framework that can test for global and gene subset-specific changes in expression. We test our model through extensive simulations and analyses with count-based gene expression data from perturbation and observational sequencing experiments. We find that our methods can effectively summarize and quantify complex transcriptional changes in case-control experimental sequencing data.},
	urldate = {2023-01-24},
	publisher = {arXiv},
	author = {Jones, Andrew and Townes, F. William and Li, Didong and Engelhardt, Barbara E.},
	month = feb,
	year = {2021},
	keywords = {Quantitative Biology - Quantitative Methods, Statistics - Methodology, Quantitative Biology - Genomics},
	file = {arXiv Fulltext PDF:/home/robin/snap/zotero-snap/common/Zotero/storage/5X8LBWME/Jones et al. - 2021 - Contrastive latent variable modeling with applicat.pdf:application/pdf;arXiv.org Snapshot:/home/robin/snap/zotero-snap/common/Zotero/storage/DZ93YEVM/2102.html:text/html},
}

@misc{li_probabilistic_2021,
	title = {Probabilistic {Contrastive} {Principal} {Component} {Analysis}},
	url = {http://arxiv.org/abs/2012.07977},
	doi = {10.48550/arXiv.2012.07977},
	abstract = {Dimension reduction is useful for exploratory data analysis. In many applications, it is of interest to discover variation that is enriched in a "foreground" dataset relative to a "background" dataset. Recently, contrastive principal component analysis (CPCA) was proposed for this setting. However, the lack of a formal probabilistic model makes it difficult to reason about CPCA and to tune its hyperparameter. In this work, we propose probabilistic contrastive principal component analysis (PCPCA), a model-based alternative to CPCA. We discuss how to set the hyperparameter in theory and in practice, and we show several of PCPCA's advantages over CPCA, including greater interpretability, uncertainty quantification and principled inference, robustness to noise and missing data, and the ability to generate data from the model. We demonstrate PCPCA's performance through a series of simulations and case-control experiments with datasets of gene expression, protein expression, and images.},
	urldate = {2023-01-24},
	publisher = {arXiv},
	author = {Li, Didong and Jones, Andrew and Engelhardt, Barbara},
	month = apr,
	year = {2021},
	note = {arXiv:2012.07977 [cs, stat]},
	keywords = {Computer Science - Machine Learning, Statistics - Methodology},
	file = {arXiv Fulltext PDF:/home/robin/snap/zotero-snap/common/Zotero/storage/DTN4BVXM/Li et al. - 2021 - Probabilistic Contrastive Principal Component Anal.pdf:application/pdf;arXiv.org Snapshot:/home/robin/snap/zotero-snap/common/Zotero/storage/C2WBTSAW/2012.html:text/html},
}

@article{ashburner_voxel-based_2000,
	title = {Voxel-based morphometry--the methods},
	volume = {11},
	abstract = {At its simplest, voxel-based morphometry (VBM) involves a voxel-wise comparison of the local concentration of gray matter between two groups of subjects. The procedure is relatively straightforward and involves spatially normalizing high-resolution images from all the subjects in the study into the same stereotactic space. This is followed by segmenting the gray matter from the spatially normalized images and smoothing the gray-matter segments. Voxel-wise parametric statistical tests which compare the smoothed gray-matter images from the two groups are performed. Corrections for multiple comparisons are made using the theory of Gaussian random fields. This paper describes the steps involved in VBM, with particular emphasis on segmenting gray matter from MR images with nonuniformity artifact. We provide evaluations of the assumptions that underpin the method, including the accuracy of the segmentation and the assumptions made about the statistical distribution of the data.},
	language = {eng},
	number = {6},
	journal = {NeuroImage},
	author = {Ashburner, J. and Friston, K. J.},
	month = jun,
	year = {2000},
	pmid = {10860804},
	keywords = {Brain, Humans, Magnetic Resonance Imaging, Models, Neurological, False Positive Reactions, Periaqueductal Gray, Random Allocation},
	pages = {805--821},
	file = {Version soumise:/home/robin/snap/zotero-snap/common/Zotero/storage/MD84DZVN/Ashburner et Friston - 2000 - Voxel-based morphometry--the methods.pdf:application/pdf},
}

@article{jack_nia-aa_2018,
	title = {{NIA}-{AA} {Research} {Framework}: {Toward} a biological definition of {Alzheimer}'s disease},
	volume = {14},
	shorttitle = {{NIA}-{AA} {Research} {Framework}},
	abstract = {In 2011, the National Institute on Aging and Alzheimer's Association created separate diagnostic recommendations for the preclinical, mild cognitive impairment, and dementia stages of Alzheimer's disease. Scientific progress in the interim led to an initiative by the National Institute on Aging and Alzheimer's Association to update and unify the 2011 guidelines. This unifying update is labeled a "research framework" because its intended use is for observational and interventional research, not routine clinical care. In the National Institute on Aging and Alzheimer's Association Research Framework, Alzheimer's disease (AD) is defined by its underlying pathologic processes that can be documented by postmortem examination or in vivo by biomarkers. The diagnosis is not based on the clinical consequences of the disease (i.e., symptoms/signs) in this research framework, which shifts the definition of AD in living people from a syndromal to a biological construct. The research framework focuses on the diagnosis of AD with biomarkers in living persons. Biomarkers are grouped into those of β amyloid deposition, pathologic tau, and neurodegeneration [AT(N)]. This ATN classification system groups different biomarkers (imaging and biofluids) by the pathologic process each measures. The AT(N) system is flexible in that new biomarkers can be added to the three existing AT(N) groups, and new biomarker groups beyond AT(N) can be added when they become available. We focus on AD as a continuum, and cognitive staging may be accomplished using continuous measures. However, we also outline two different categorical cognitive schemes for staging the severity of cognitive impairment: a scheme using three traditional syndromal categories and a six-stage numeric scheme. It is important to stress that this framework seeks to create a common language with which investigators can generate and test hypotheses about the interactions among different pathologic processes (denoted by biomarkers) and cognitive symptoms. We appreciate the concern that this biomarker-based research framework has the potential to be misused. Therefore, we emphasize, first, it is premature and inappropriate to use this research framework in general medical practice. Second, this research framework should not be used to restrict alternative approaches to hypothesis testing that do not use biomarkers. There will be situations where biomarkers are not available or requiring them would be counterproductive to the specific research goals (discussed in more detail later in the document). Thus, biomarker-based research should not be considered a template for all research into age-related cognitive impairment and dementia; rather, it should be applied when it is fit for the purpose of the specific research goals of a study. Importantly, this framework should be examined in diverse populations. Although it is possible that β-amyloid plaques and neurofibrillary tau deposits are not causal in AD pathogenesis, it is these abnormal protein deposits that define AD as a unique neurodegenerative disease among different disorders that can lead to dementia. We envision that defining AD as a biological construct will enable a more accurate characterization and understanding of the sequence of events that lead to cognitive impairment that is associated with AD, as well as the multifactorial etiology of dementia. This approach also will enable a more precise approach to interventional trials where specific pathways can be targeted in the disease process and in the appropriate people.},
	language = {eng},
	number = {4},
	journal = {Alzheimer's \& Dementia: The Journal of the Alzheimer's Association},
	author = {Jack, Clifford R. et al.},
	month = apr,
	year = {2018},
	pmid = {29653606},
	pmcid = {PMC5958625},
	keywords = {Brain, Humans, Alzheimer Disease, Biomarkers, United States, Alzheimer's disease diagnosis, Alzheimer's disease imaging, Amyloid PET, Animals, Biomarkers Alzheimer's disease, CSF biomarkers Alzheimer's disease, National Institute on Aging (U.S.), Preclinical Alzheimer's disease, Tau PET},
	pages = {535--562},
	file = {Texte intégral:/home/robin/snap/zotero-snap/common/Zotero/storage/SSH4GBFG/Jack et al. - 2018 - NIA-AA Research Framework Toward a biological def.pdf:application/pdf},
}

@misc{liu_deep_2015,
	title = {Deep {Learning} {Face} {Attributes} in the {Wild}},
	abstract = {Predicting face attributes in the wild is challenging due to complex face variations. We propose a novel deep learning framework for attribute prediction in the wild. It cascades two CNNs, LNet and ANet, which are fine-tuned jointly with attribute tags, but pre-trained differently. LNet is pre-trained by massive general object categories for face localization, while ANet is pre-trained by massive face identities for attribute prediction. This framework not only outperforms the state-of-the-art with a large margin, but also reveals valuable facts on learning face representation. (1) It shows how the performances of face localization (LNet) and attribute prediction (ANet) can be improved by different pre-training strategies. (2) It reveals that although the filters of LNet are fine-tuned only with image-level attribute tags, their response maps over entire images have strong indication of face locations. This fact enables training LNet for face localization with only image-level annotations, but without face bounding boxes or landmarks, which are required by all attribute recognition works. (3) It also demonstrates that the high-level hidden neurons of ANet automatically discover semantic concepts after pre-training with massive face identities, and such concepts are significantly enriched after fine-tuning with attribute tags. Each attribute can be well explained with a sparse linear combination of these concepts.},
	urldate = {2023-01-24},
	journal = {arXiv},
	author = {Liu, Ziwei and Luo, Ping and Wang, Xiaogang and Tang, Xiaoou},
	month = sep,
	note = {International Conference on Computer Vision (ICCV), 2015.},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
	file = {arXiv Fulltext PDF:/home/robin/snap/zotero-snap/common/Zotero/storage/8FCW5ZEJ/Liu et al. - 2015 - Deep Learning Face Attributes in the Wild.pdf:application/pdf;arXiv.org Snapshot:/home/robin/snap/zotero-snap/common/Zotero/storage/Y3T74UEE/1411.html:text/html},
}

@article{tamminga_bipolar_2014,
	title = {Bipolar and {Schizophrenia} {Network} for {Intermediate} {Phenotypes}: {Outcomes} {Across} the {Psychosis} {Continuum}},
	volume = {40},
	shorttitle = {Bipolar and {Schizophrenia} {Network} for {Intermediate} {Phenotypes}},
	abstract = {Bipolar and schizophrenia network for intermediate phenotypes is a network of investigator-driven laboratories focused on developing phenotypes, genotypes, and biomarkers for psychosis. Over the last 5 years, the consortium has accomplished a dense phenotyping protocol using probands with a lifetime history of psychosis, their relatives, and healthy controls. This has established a library of biomarker information on individuals with schizophrenia, schizoaffective disorder, and bipolar disorder with psychosis. The founding goal of establishing disease biomarkers for current psychotic diagnoses has been poorly met, because the cognitive, electrophysiologic, eye movement, and brain imaging biomarkers did not regularly discriminate individuals with different DSM psychosis diagnoses. In future, we will use this biomarker information to establish a pathway to biomarker-based classification in psychoses.},
	number = {2},
	urldate = {2023-01-24},
	journal = {Schizophrenia Bulletin},
	author = {Tamminga, Carol A. and Pearlson, Godfrey and Keshavan, Matcheri and Sweeney, John and Clementz, Brett and Thaker, Gunvant},
	month = mar,
	year = {2014},
	pmid = {24562492},
	pmcid = {PMC3934403},
	pages = {S131--S137},
	file = {PubMed Central Full Text PDF:/home/robin/snap/zotero-snap/common/Zotero/storage/ZG5PFP99/Tamminga et al. - 2014 - Bipolar and Schizophrenia Network for Intermediate.pdf:application/pdf},
}

@article{wang_schizconnect_2016,
	title = {{SchizConnect}: {Mediating} neuroimaging databases on schizophrenia and related disorders for large-scale integration},
	volume = {124},
	shorttitle = {{SchizConnect}},
	abstract = {SchizConnect (www.schizconnect.org) is built to address the issues of multiple data repositories in schizophrenia neuroimaging studies. It includes a level of mediation--translating across data sources--so that the user can place one query, e.g. for diffusion images from male individuals with schizophrenia, and find out from across participating data sources how many datasets there are, as well as downloading the imaging and related data. The current version handles the Data Usage Agreements across different studies, as well as interpreting database-specific terminologies into a common framework. New data repositories can also be mediated to bring immediate access to existing datasets. Compared with centralized, upload data sharing models, SchizConnect is a unique, virtual database with a focus on schizophrenia and related disorders that can mediate live data as information is being updated at each data source. It is our hope that SchizConnect can facilitate testing new hypotheses through aggregated datasets, promoting discovery related to the mechanisms underlying schizophrenic dysfunction.},
	language = {eng},
	number = {Pt B},
	journal = {NeuroImage},
	author = {Wang, Lei and Alpert, Kathryn I. and Calhoun, Vince D. and Cobia, Derin J. and Keator, David B. and King, Margaret D. and Kogan, Alexandr and Landis, Drew and Tallis, Marcelo and Turner, Matthew D. and Potkin, Steven G. and Turner, Jessica A. and Ambite, Jose Luis},
	month = jan,
	year = {2016},
	pmid = {26142271},
	pmcid = {PMC4651768},
	keywords = {Adult, Female, Humans, Male, Young Adult, Aged, Neuroimaging, Databases, Factual, Adolescent, Middle Aged, Schizophrenia, Information Dissemination, Child, Internet, Data mediation and integration, Database Management Systems, Datasets as Topic, Mega analysis, Neuroinformatics, Schizophrenia databases, Terminology as Topic, User-Computer Interface},
	pages = {1155--1167},
	file = {Version acceptée:/home/robin/snap/zotero-snap/common/Zotero/storage/LXM566M7/Wang et al. - 2016 - SchizConnect Mediating neuroimaging databases on .pdf:application/pdf},
}

@article{zheng_massively_2017,
	title = {Massively parallel digital transcriptional profiling of single cells},
	volume = {8},
	copyright = {2017 The Author(s)},
	abstract = {Characterizing the transcriptome of individual cells is fundamental to understanding complex biological systems. We describe a droplet-based system that enables 3′ mRNA counting of tens of thousands of single cells per sample. Cell encapsulation, of up to 8 samples at a time, takes place in ∼6 min, with ∼50\% cell capture efficiency. To demonstrate the system’s technical performance, we collected transcriptome data from ∼250k single cells across 29 samples. We validated the sensitivity of the system and its ability to detect rare populations using cell lines and synthetic RNAs. We profiled 68k peripheral blood mononuclear cells to demonstrate the system’s ability to characterize large immune populations. Finally, we used sequence variation in the transcriptome data to determine host and donor chimerism at single-cell resolution from bone marrow mononuclear cells isolated from transplant patients.},
	language = {en},
	number = {1},
	urldate = {2023-01-24},
	journal = {Nature Communications},
	author = {Zheng, Grace X. Y. and Terry, Jessica M. and Belgrader, Phillip and Ryvkin, Paul and Bent, Zachary W. and Wilson, Ryan and Ziraldo, Solongo B. and Wheeler, Tobias D. and McDermott, Geoff P. and Zhu, Junjie and Gregory, Mark T. and Shuga, Joe and Montesclaros, Luz and Underwood, Jason G. and Masquelier, Donald A. and Nishimura, Stefanie Y. and Schnall-Levin, Michael and Wyatt, Paul W. and Hindson, Christopher M. and Bharadwaj, Rajiv and Wong, Alexander and Ness, Kevin D. and Beppu, Lan W. and Deeg, H. Joachim and McFarland, Christopher and Loeb, Keith R. and Valente, William J. and Ericson, Nolan G. and Stevens, Emily A. and Radich, Jerald P. and Mikkelsen, Tarjei S. and Hindson, Benjamin J. and Bielas, Jason H.},
	month = jan,
	year = {2017},
	note = {Number: 1
Publisher: Nature Publishing Group},
	keywords = {Cancer genomics, Transcriptomics},
	pages = {14049},
	file = {Full Text PDF:/home/robin/snap/zotero-snap/common/Zotero/storage/G62KEBW5/Zheng et al. - 2017 - Massively parallel digital transcriptional profili.pdf:application/pdf},
}

@article{haber_single-cell_2017,
	title = {A single-cell survey of the small intestinal epithelium},
	volume = {551},
	copyright = {2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.},
	abstract = {Intestinal epithelial cells absorb nutrients, respond to microbes, function as a barrier and help to coordinate immune responses. Here we report profiling of 53,193 individual epithelial cells from the small intestine and organoids of mice, which enabled the identification and characterization of previously unknown subtypes of intestinal epithelial cell and their gene signatures. We found unexpected diversity in hormone-secreting enteroendocrine cells and constructed the taxonomy of newly identified subtypes, and distinguished between two subtypes of tuft cell, one of which expresses the epithelial cytokine Tslp and the pan-immune marker CD45, which was not previously associated with non-haematopoietic cells. We also characterized the ways in which cell-intrinsic states and the proportions of different cell types respond to bacterial and helminth infections: Salmonella infection caused an increase in the abundance of Paneth cells and enterocytes, and broad activation of an antimicrobial program; Heligmosomoides polygyrus caused an increase in the abundance of goblet and tuft cells. Our survey highlights previously unidentified markers and programs, associates sensory molecules with cell types, and uncovers principles of gut homeostasis and response to pathogens.},
	language = {en},
	number = {7680},
	urldate = {2023-01-24},
	journal = {Nature},
	author = {Haber, Adam L. and Biton, Moshe and Rogel, Noga and Herbst, Rebecca H. and Shekhar, Karthik and Smillie, Christopher and Burgin, Grace and Delorey, Toni M. and Howitt, Michael R. and Katz, Yarden and Tirosh, Itay and Beyaz, Semir and Dionne, Danielle and Zhang, Mei and Raychowdhury, Raktima and Garrett, Wendy S. and Rozenblatt-Rosen, Orit and Shi, Hai Ning and Yilmaz, Omer and Xavier, Ramnik J. and Regev, Aviv},
	month = nov,
	year = {2017},
	note = {Number: 7680
Publisher: Nature Publishing Group},
	keywords = {Classification and taxonomy, Multicellular systems, Transcriptomics},
	pages = {333--339},
	file = {Texte intégral:/home/robin/snap/zotero-snap/common/Zotero/storage/5IB9LE3L/Haber et al. - 2017 - A single-cell survey of the small intestinal epith.pdf:application/pdf},
}


@article{menze_multimodal_2015,
	title = {The {Multimodal} {Brain} {Tumor} {Image} {Segmentation} {Benchmark} ({BRATS})},
	volume = {34},
	number = {10},
	journal = {IEEE Transactions on Medical Imaging},
	author = {Menze, Bjoern H. and Jakab, Andras and Bauer, Stefan and Kalpathy-Cramer, Jayashree and Farahani, Keyvan and Kirby, Justin and Burren, Yuliya and Porz, Nicole and et al.},
	month = oct,
	year = {2015},
	pages = {1993--2024}
}


@article{nguyen_estimating_2010,
	title = {Estimating divergence functionals and the likelihood ratio by convex risk minimization},
	volume = {56},
	abstract = {We develop and analyze \$M\$-estimation methods for divergence functionals and the likelihood ratios of two probability distributions. Our method is based on a non-asymptotic variational characterization of \$f\$-divergences, which allows the problem of estimating divergences to be tackled via convex empirical risk optimization. The resulting estimators are simple to implement, requiring only the solution of standard convex programs. We present an analysis of consistency and convergence for these estimators. Given conditions only on the ratios of densities, we show that our estimators can achieve optimal minimax rates for the likelihood ratio and the divergence functionals in certain regimes. We derive an efficient optimization algorithm for computing our estimates, and illustrate their convergence behavior and practical viability by simulations.},
	number = {11},
	urldate = {2023-03-09},
	journal = {IEEE Transactions on Information Theory},
	author = {Nguyen, XuanLong and Wainwright, Martin J. and Jordan, Michael I.},
	month = nov,
	year = {2010},
	keywords = {Computer Science - Information Theory, Mathematics - Statistics Theory},
	pages = {5847--5861},
	annote = {Comment: 28 pages},
	file = {arXiv Fulltext PDF:/home/robin/snap/zotero-snap/common/Zotero/storage/96WIMDQ9/Nguyen et al. - 2010 - Estimating divergence functionals and the likeliho.pdf:application/pdf;arXiv.org Snapshot:/home/robin/snap/zotero-snap/common/Zotero/storage/BG6B5HUN/0809.html:text/html},
}

@article{Sugiyama2012DensityratioMU,
  title={Density-ratio matching under the Bregman divergence: a unified framework of density-ratio estimation},
  author={Masashi Sugiyama and Teruyuki Suzuki and Takafumi Kanamori},
  journal={Annals of the Institute of Statistical Mathematics},
  year={2012},
  volume={64},
  pages={1009-1044}
}


@article{heinsfeld_identification_2017,
	title = {Identification of autism spectrum disorder using deep learning and the {ABIDE} dataset},
	volume = {17},
	abstract = {The goal of the present study was to apply deep learning algorithms to identify autism spectrum disorder (ASD) patients from large brain imaging dataset, based solely on the patients brain activation patterns. We investigated ASD patients brain imaging data from a world-wide multi-site database known as ABIDE (Autism Brain Imaging Data Exchange). ASD is a brain-based disorder characterized by social deficits and repetitive behaviors. According to recent Centers for Disease Control data, ASD affects one in 68 children in the United States. We investigated patterns of functional connectivity that objectively identify ASD participants from functional brain imaging data, and attempted to unveil the neural patterns that emerged from the classification. The results improved the state-of-the-art by achieving 70\% accuracy in identification of ASD versus control patients in the dataset. The patterns that emerged from the classification show an anticorrelation of brain function between anterior and posterior areas of the brain; the anticorrelation corroborates current empirical evidence of anterior-posterior disruption in brain connectivity in ASD. We present the results and identify the areas of the brain that contributed most to differentiating ASD from typically developing controls as per our deep learning model., 
          
            
              •
              We successfully applied Deep Learning to classify ASD and controls using ABIDE data
            
            
              •
              We extracted patterns of brain function in rs-fMRI and showed anterior-posterior underconnectivity in the autistic brain
            
            
              •
              Underconnected areas in ASD rs-fMRI data: Paracingulate Gyrus, Supramarginal Gyrus and Middle Temporal Gyrus
            
            
              •
              We surpass the state-of-the-art in deep learning classification of brain activation by achieving 70\% accuracy},
	urldate = {2023-03-09},
	journal = {NeuroImage : Clinical},
	author = {Heinsfeld, Anibal Sólon and Franco, Alexandre Rosa and Craddock, R. Cameron and Buchweitz, Augusto and Meneguzzi, Felipe},
	month = aug,
	year = {2017},
	pmid = {29034163},
	pmcid = {PMC5635344},
	pages = {16--23},
	file = {PubMed Central Full Text PDF:/home/robin/snap/zotero-snap/common/Zotero/storage/AU6DCEQH/Heinsfeld et al. - 2017 - Identification of autism spectrum disorder using d.pdf:application/pdf},
}

@article{akshoomoff_role_2006,
	title = {The {Role} of the {Autism} {Diagnostic} {Observation} {Schedule} in the {Assessment} of {Autism} {Spectrum} {Disorders} in {School} and {Community} {Settings}},
	volume = {11},
	abstract = {Autism diagnostic practices among school and clinical psychologists, particularly those using the Autism Diagnostic Observation Schedule (ADOS), were examined using national survey results (N = 132). School and clinical psychologists were similar in following the Best Practice Guidelines for screening, diagnosis and assessment, School psychologists were more likely to include a school or home observation and teacher report than clinical psychologists but evaluated significantly fewer children with autism spectrum disorders per year compared to clinical psychologists. School psychologists who were ADOS users were more likely to consider themselves autism experts and include a review of records than ADOS non-users. Perceived advantages of the ADOS included its strength in capturing ASD-specific behaviors and the standardized structure provided for observation, while diagnostic discrimination and required resources were the most commonly identified disadvantages.},
	language = {eng},
	journal = {The California school psychologist},
	author = {Akshoomoff, Natacha and Corsello, Christina and Schmidt, Heather},
	year = {2006},
	pmid = {17502922},
	pmcid = {PMC1868476},
	pages = {7--19},
	file = {Version acceptée:/home/robin/snap/zotero-snap/common/Zotero/storage/L3F69AH6/Akshoomoff et al. - 2006 - The Role of the Autism Diagnostic Observation Sche.pdf:application/pdf},
}


@InProceedings{pmlr-v97-antelmi19a,
  title = 	 {Sparse Multi-Channel Variational Autoencoder for the Joint Analysis of Heterogeneous Data},
  author =       {Antelmi, Luigi and Ayache, Nicholas and Robert, Philippe and Lorenzi, Marco},
  booktitle = 	 {Proceedings of the 36th International Conference on Machine Learning},
  pages = 	 {302--311},
  year = 	 {2019},
  volume = 	 {97},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {09--15 Jun},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v97/antelmi19a/antelmi19a.pdf},
  abstract = 	 {Interpretable modeling of heterogeneous data channels is essential in medical applications, for example when jointly analyzing clinical scores and medical images. Variational Autoencoders (VAE) are powerful generative models that learn representations of complex data. The flexibility of VAE may come at the expense of lack of interpretability in describing the joint relationship between heterogeneous data. To tackle this problem, in this work we extend the variational framework of VAE to bring parsimony and interpretability when jointly account for latent relationships across multiple channels. In the latent space, this is achieved by constraining the variational distribution of each channel to a common target prior. Parsimonious latent representations are enforced by variational dropout. Experiments on synthetic data show that our model correctly identifies the prescribed latent dimensions and data relationships across multiple testing scenarios. When applied to imaging and clinical data, our method allows to identify the joint effect of age and pathology in describing clinical condition in a large scale clinical cohort.}
}
