NeurIPS 2023 Workshop DistShift Submissions

Context-Aware Meta-Learning
Christopher Fifty, Dennis Duan, Ronald Guenther Junkins, Ehsan Amid, Jure Leskovec, Christopher Re, Sebastian Thrun
- Published: 28 Oct 2023, Last Modified: 02 Apr 2024
- DistShift 2023 Poster
- Readers: Everyone
Reliable Test-Time Adaptation via Agreement-on-the-Line
Eungyeup Kim, Mingjie Sun, Aditi Raghunathan, J Zico Kolter
- Published: 28 Oct 2023, Last Modified: 02 Apr 2024
- DistShift 2023 Poster
- Readers: Everyone
On selective classification under distribution shift
Luís Felipe Prates Cattelan, Danilo Silva
- Published: 28 Oct 2023, Last Modified: 02 Apr 2024
- DistShift 2023 Poster
- Readers: Everyone
Reward Model Underspecification in Language Model Alignment
Jacob Eisenstein, Jonathan Berant, Chirag Nagpal, Alekh Agarwal, Ahmad Beirami, Alexander Nicholas D'Amour, Krishnamurthy Dj Dvijotham, Katherine A Heller, Stephen Robert Pfohl, Deepak Ramachandran
- Published: 28 Oct 2023, Last Modified: 02 Apr 2024
- DistShift 2023 Poster
- Readers: Everyone
Revisiting Dynamic Evaluation: Online Adaptation for Large Language Models
Amal Rannen-Triki, Jorg Bornschein, Razvan Pascanu, Alexandre Galashov, Michalis Titsias, Marcus Hutter, András György, Yee Whye Teh
- Published: 28 Oct 2023, Last Modified: 02 Apr 2024
- DistShift 2023 Poster
- Readers: Everyone
LLM Routing with Benchmark Datasets
Tal Shnitzer, Anthony Ou, Mírian Silva, Kate Soule, Yuekai Sun, Justin Solomon, Neil Thompson, Mikhail Yurochkin
- Published: 28 Oct 2023, Last Modified: 02 Apr 2024
- DistShift 2023 Oral
- Readers: Everyone
Towards Global, General-Purpose Pretrained Geographic Location Encoders
Konstantin Klemmer, Esther Rolf, Caleb Robinson, Lester Mackey, Marc Rußwurm
- Published: 28 Oct 2023, Last Modified: 02 Apr 2024
- DistShift 2023 Poster
- Readers: Everyone
HePCo: Data-Free Heterogeneous Prompt Consolidation for Continual Federated Learning
Shaunak Halbe, James Seale Smith, Junjiao Tian, Zsolt Kira
- Published: 28 Oct 2023, Last Modified: 02 Apr 2024
- DistShift 2023 Poster
- Readers: Everyone
Exploring Generalisability of Self-Distillation with No Labels for SAR-Based Vegetation Prediction
Laura Martínez-Ferrer, Anna Jungbluth, Joseph Alejandro Gallego Mejia, Matt Allen, Francisco Dorr, Freddie Kalaitzis, Raúl Ramos-Pollán
- Published: 28 Oct 2023, Last Modified: 02 Apr 2024
- DistShift 2023 Poster
- Readers: Everyone
Data Filtering Networks
Alex Fang, Albin Madappally Jose, Amit Jain, Ludwig Schmidt, Alexander T Toshev, Vaishaal Shankar
- Published: 28 Oct 2023, Last Modified: 02 Apr 2024
- DistShift 2023 Poster
- Readers: Everyone
Channel Selection for Test-Time Adaptation Under Distribution Shift
Pedro Vianna, Muawiz Sajjad Chaudhary, An Tang, Guy Cloutier, Guy Wolf, Michael Eickenberg, Eugene Belilovsky
- Published: 28 Oct 2023, Last Modified: 02 Apr 2024
- DistShift 2023 Poster
- Readers: Everyone
Beyond Top-Class Agreement: Using Divergences to Forecast Performance under Distribution Shift
Mona Schirmer, Dan Zhang, Eric Nalisnick
- Published: 28 Oct 2023, Last Modified: 02 Apr 2024
- DistShift 2023 Poster
- Readers: Everyone
The SVHN Dataset Is Deceptive for Probabilistic Generative Models Due to a Distribution Mismatch
Tim Z. Xiao, Johannes Zenn, Robert Bamler
- Published: 28 Oct 2023, Last Modified: 02 Apr 2024
- DistShift 2023 Poster
- Readers: Everyone
Robustness May be More Brittle than We Think under Different Degrees of Distribution Shifts
Kaican Li, Yifan Zhang, Lanqing HONG, Zhenguo Li, Nevin L. Zhang
- Published: 28 Oct 2023, Last Modified: 02 Apr 2024
- DistShift 2023 Poster
- Readers: Everyone
Understanding Catastrophic Forgetting in Language Models via Implicit Inference
Suhas Kotha, Jacob Springer, Aditi Raghunathan
- Published: 28 Oct 2023, Last Modified: 02 Apr 2024
- DistShift 2023 Poster
- Readers: Everyone
Continual Learning with Low Rank Adaptation
Martin Wistuba, Prabhu Teja S, Lukas Balles, Giovanni Zappella
- Published: 28 Oct 2023, Last Modified: 02 Apr 2024
- DistShift 2023 Poster
- Readers: Everyone
Does CLIP’s generalization performance mainly stem from high train-test similarity?
Prasanna Mayilvahanan, Thaddäus Wiedemer, Evgenia Rusak, Matthias Bethge, Wieland Brendel
- Published: 28 Oct 2023, Last Modified: 02 Apr 2024
- DistShift 2023 Oral
- Readers: Everyone
Domain constraints improve risk prediction when outcome data is missing
Sidhika Balachandar, Nikhil Garg, Emma Pierson
- Published: 28 Oct 2023, Last Modified: 02 Apr 2024
- DistShift 2023 Oral
- Readers: Everyone
LCA-on-the-Line: Benchmarking Out of Distribution Generalization with Class Taxonomies
Jia Shi, Gautam Rajendrakumar Gare, Jinjin Tian, Siqi Chai, Zhiqiu Lin, Arun Balajee Vasudevan, Di Feng, Francesco Ferroni, Shu Kong, Deva Ramanan
- Published: 28 Oct 2023, Last Modified: 02 Apr 2024
- DistShift 2023 Poster
- Readers: Everyone
On Mitigating Shortcut Learning for Fair Chest X-ray Classification under Distribution Shift
Yuzhe Yang, Haoran Zhang, Dina Katabi, Marzyeh Ghassemi
- Published: 28 Oct 2023, Last Modified: 02 Apr 2024
- DistShift 2023 Poster
- Readers: Everyone
Retrieval-based Language Models Using a Multi-domain Datastore
Rulin Shao, Sewon Min, Luke Zettlemoyer, Pang Wei Koh
- Published: 28 Oct 2023, Last Modified: 02 Apr 2024
- DistShift 2023 Poster
- Readers: Everyone
Enhancing Robustness of Foundation Model Representations under Provenance-related Distribution Shifts
Xiruo Ding, Zhecheng Sheng, Brian Hur, Feng Chen, Serguei V. S. Pakhomov, Trevor Cohen
- Published: 28 Oct 2023, Last Modified: 02 Apr 2024
- DistShift 2023 Poster
- Readers: Everyone
Pseudo-Calibration: Improving Predictive Uncertainty Estimation in Domain Adaptation
Dapeng Hu, Jian Liang, Xinchao Wang, Chuan-Sheng Foo
- Published: 28 Oct 2023, Last Modified: 02 Apr 2024
- DistShift 2023 Poster
- Readers: Everyone
HyperNetwork Approximating Future Parameters for Time Series Forecasting under Temporal Drifts
Jaehoon Lee, Chan Kim, Gyumin Lee, Haksoo Lim, Jeongwhan Choi, Kookjin Lee, Dongeun Lee, Sanghyun Hong, Noseong Park
- Published: 28 Oct 2023, Last Modified: 02 Apr 2024
- DistShift 2023 Poster
- Readers: Everyone
Maximum Likelihood Estimation is All You Need for Well-Specified Covariate Shift
Jiawei Ge, Shange Tang, Jianqing Fan, Cong Ma, Chi Jin
- Published: 28 Oct 2023, Last Modified: 02 Apr 2024
- DistShift 2023 Poster
- Readers: Everyone