ICML 2023 Workshop Differentiable Almost Everything Submissions

Score Function Gradient Estimation to Widen the Applicability of Decision-Focused Learning
Mattia Silvestri, Senne Berden, Jayanta Mandi, Ali İrfan Mahmutoğulları, Maxime Mulamba, Allegra De Filippo, Tias Guns, Michele Lombardi
- Published: 20 Jun 2023, Last Modified: 18 Sept 2023
- Differentiable Almost Everything
- Readers: Everyone
Probabilistic Task-Adaptive Graph Rewiring
Chendi Qian, Andrei Manolache, Kareem Ahmed, Zhe Zeng, Guy Van den Broeck, Mathias Niepert, Christopher Morris
- Published: 20 Jun 2023, Last Modified: 17 Sept 2023
- Differentiable Almost Everything
- Readers: Everyone
Differentiable Sampling of Categorical Distributions Using the CatLog-Derivative Trick
Lennert De Smet, Emanuele Sansone, Pedro Zuidberg Dos Martires
- Published: 20 Jun 2023, Last Modified: 17 Sept 2023
- Differentiable Almost Everything
- Readers: Everyone
SelMix: Selective Mixup Fine Tuning for Optimizing Non-Decomposable Metrics
Shrinivas Ramasubramanian, Harsh Rangwani, Sho Takemori, Kunal Samanta, Yuhei Umeda, Venkatesh Babu Radhakrishnan
- Published: 20 Jun 2023, Last Modified: 17 Sept 2023
- Differentiable Almost Everything
- Readers: Everyone
Dynamic Control of Queuing Networks via Differentiable Discrete-Event Simulation
Ethan Che, Hongseok Namkoong, Jing Dong
- Published: 20 Jun 2023, Last Modified: 17 Sept 2023
- Differentiable Almost Everything
- Readers: Everyone
A Unified Approach to Count-Based Weakly-Supervised Learning
Vinay Shukla, Zhe Zeng, Kareem Ahmed, Guy Van den Broeck
- Published: 20 Jun 2023, Last Modified: 17 Sept 2023
- Differentiable Almost Everything
- Readers: Everyone
Data Models for Dataset Drift Controls in Machine Learning With Optical Images
Luis Oala, Marco Aversa, Gabriel Nobis, Kurt Willis, Yoan Neuenschwander, Michèle Buck, Christian Matek, Jerome Extermann, Enrico Pomarico, Wojciech Samek, Roderick Murray-Smith, Christoph Clausen, Bruno Sanguinetti
- Published: 20 Jun 2023, Last Modified: 17 Sept 2023
- Differentiable Almost Everything
- Readers: Everyone
A Gradient Flow Modification to Improve Learning from Differentiable Quantum Simulators
Patrick Schnell, Nils Thuerey
- Published: 20 Jun 2023, Last Modified: 17 Sept 2023
- Differentiable Almost Everything
- Readers: Everyone
Differentiating Metropolis-Hastings to Optimize Intractable Densities
Gaurav Arya, Ruben Seyer, Frank Schäfer, Kartik Chandra, Alexander K. Lew, Mathieu Huot, Vikash Mansinghka, Jonathan Ragan-Kelley, Christopher Vincent Rackauckas, Moritz Schauer
- Published: 20 Jun 2023, Last Modified: 17 Sept 2023
- Differentiable Almost Everything
- Readers: Everyone
A Short Review of Automatic Differentiation Pitfalls in Scientific Computing
Jan Hueckelheim, Harshitha Menon, William S. Moses, Bruce Christianson, Paul Hovland, Laurent Hascoet
- Published: 20 Jun 2023, Last Modified: 17 Sept 2023
- Differentiable Almost Everything
- Readers: Everyone
Lossless hardening with $\partial\mathbb{B}$ nets
Ian Wright
- Published: 20 Jun 2023, Last Modified: 17 Sept 2023
- Differentiable Almost Everything
- Readers: Everyone
Learning Observation Models with Incremental Non-Differentiable Graph Optimizers in the Loop for Robotics State Estimation
Mohamad Qadri, Michael Kaess
- Published: 20 Jun 2023, Last Modified: 17 Sept 2023
- Differentiable Almost Everything
- Readers: Everyone
Differentiable Set Partitioning
Thomas M. Sutter, Alain Ryser, Joram Liebeskind, Julia E Vogt
- Published: 20 Jun 2023, Last Modified: 17 Sept 2023
- Differentiable Almost Everything
- Readers: Everyone
Landscape Surrogate: Learning Decision Losses for Mathematical Optimization Under Partial Information
Arman Zharmagambetov, Brandon Amos, Aaron M Ferber, Taoan Huang, Bistra Dilkina, Yuandong Tian
- Published: 20 Jun 2023, Last Modified: 17 Sept 2023
- Differentiable Almost Everything
- Readers: Everyone
Investigating Axis-Aligned Differentiable Trees through Neural Tangent Kernels
Ryuichi Kanoh, Mahito Sugiyama
- Published: 20 Jun 2023, Last Modified: 17 Sept 2023
- Differentiable Almost Everything
- Readers: Everyone
PMaF: Deep Declarative Layers for Principal Matrix Features
Zhiwei Xu, Hao Wang, Yanbin Liu, Stephen Gould
- Published: 20 Jun 2023, Last Modified: 17 Sept 2023
- Differentiable Almost Everything
- Readers: Everyone
JAX FDM: A differentiable solver for inverse form-finding
Rafael Pastrana, Deniz Oktay, Ryan P Adams, Sigrid Adriaenssens
- Published: 20 Jun 2023, Last Modified: 17 Sept 2023
- Differentiable Almost Everything
- Readers: Everyone
Fine-Tuning Language Models with Just Forward Passes
Sadhika Malladi, Tianyu Gao, Eshaan Nichani, Jason D. Lee, Danqi Chen, Sanjeev Arora
- Published: 20 Jun 2023, Last Modified: 17 Sept 2023
- Differentiable Almost Everything
- Readers: Everyone
Differentiable Causal Discovery with Smooth Acyclic Orientations
Riccardo Massidda, Francesco Landolfi, Martina Cinquini, Davide Bacciu
- Published: 20 Jun 2023, Last Modified: 17 Sept 2023
- Differentiable Almost Everything
- Readers: Everyone
DNArch: Learning Convolutional Neural Architectures by Backpropagation
David W. Romero, Neil Zeghidour
- Published: 20 Jun 2023, Last Modified: 17 Sept 2023
- Differentiable Almost Everything
- Readers: Everyone
Towards Understanding Gradient Approximation in Equality Constrained Deep Declarative Networks
Stephen Gould, Ming Xu, Zhiwei Xu, Yanbin Liu
- Published: 20 Jun 2023, Last Modified: 17 Sept 2023
- Differentiable Almost Everything
- Readers: Everyone