Towards Explaining Distribution Shifts

Towards Explaining Distribution Shifts

19 October 2022

David I. Inouye

Papers citing "Towards Explaining Distribution Shifts"

18 / 18 papers shown

Title
Wasserstein Distances Made Explainable: Insights into Dataset Shifts and Transport Phenomena Philip Naumann Jacob R. Kauffmann G. Montavon 19 0 0 09 May 2025
Revealing the Intrinsic Ethical Vulnerability of Aligned Large Language Models Jiawei Lian Jianhong Pan L. Wang Yi Wang Shaohui Mei Lap-Pui Chau AAML 24 0 0 07 Apr 2025
datadriftR: An R Package for Concept Drift Detection in Predictive Models Ugur Dar Mustafa Cavus 89 0 0 15 Dec 2024
Self-Healing Machine Learning: A Framework for Autonomous Adaptation in Real-World Environments Paulius Rauba Nabeel Seedat Krzysztof Kacprzyk M. Schaar AI4CE 44 1 0 31 Oct 2024
Control+Shift: Generating Controllable Distribution Shifts Roy Friedman Rhea Chowers 39 0 0 12 Sep 2024
Enhancing Uncertainty Quantification in Drug Discovery with Censored Regression Labels Emma Svensson Hannah Rosa Friesacher S. Winiwarter Lewis H. Mervin Adam Arany O. Engkvist 29 2 0 06 Sep 2024
Multiply-Robust Causal Change Attribution Victor Quintas-Martinez M. T. Bahadori Eduardo Santiago Jeff Mu Dominik Janzing David Heckerman CML 16 1 0 12 Apr 2024
A Survey on Evaluation of Out-of-Distribution Generalization Han Yu Jiashuo Liu Xingxuan Zhang Jiayun Wu Peng Cui OOD 42 9 0 04 Mar 2024
Recent Advances, Applications, and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2023 Symposium Hyewon Jeong Sarah Jabbour Yuzhe Yang Rahul Thapta Hussein Mozannar ... Linying Zhang Harvineet Singh Tom Hartvigsen Helen Zhou Chinasa T. Okolo VLM AI4TS OOD 43 2 0 03 Mar 2024
A hierarchical decomposition for explaining ML performance discrepancies Jean Feng Harvineet Singh Fan Xia Adarsh Subbaswamy Alexej Gossmann CML 25 0 0 22 Feb 2024
Distributional Counterfactual Explanations With Optimal Transport Lei You Lele Cao Mattias Nilsson Bo Zhao Lei Lei OT OffRL 20 1 0 23 Jan 2024
Towards Characterizing Domain Counterfactuals For Invertible Latent Causal Models Zeyu Zhou Ruqi Bai Sean Kulinski Murat Kocaoglu David I. Inouye CML 19 1 0 20 Jun 2023
Rectifying Group Irregularities in Explanations for Distribution Shift Adam Stein Yinjun Wu Eric Wong Mayur Naik 22 1 0 25 May 2023
Scalable Optimal Transport Methods in Machine Learning: A Contemporary Survey Abdelwahed Khamis Russell Tsuchida Mohamed Tarek V. Rolland Lars Petersson OT 38 12 0 08 May 2023
"Why did the Model Fail?": Attributing Model Performance Changes to Distribution Shifts Haoran Zhang Harvineet Singh Marzyeh Ghassemi Shalmali Joshi 22 18 0 19 Oct 2022
Wasserstein-2 Generative Networks Alexander Korotin Vage Egiazarian Arip Asadulaev Alexander Safin E. Burnaev GAN 115 100 0 28 Sep 2019
A Style-Based Generator Architecture for Generative Adversarial Networks Tero Karras S. Laine Timo Aila 262 10,320 0 12 Dec 2018
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 225 3,672 0 28 Feb 2017