Timezone: »
Supervised learning models are increasingly used in algorithmic decision-making. The traditional assumption on the training and testing data being independently and identically distributed is often violated in practical learning settings, due to distribution shifts. To mitigate the effects of such nonstationarities, risk-sensitive learning is proposed to train models under different (risk) functionals beyond the expected loss. For example, learning under the conditional value-at-risk of the losses is equivalent to training a model under a particular type of worst-case distribution shift. While many risk functionals and learning procedures have been proposed, their implementations are either nonexistent or in individualized repositories. With no common implementations and baseline test beds, it is difficult to decide which risk functionals and learning procedures to use. To address this, we introduce a library (RiskyZoo) for risk-sensitive supervised learning. The library contains implementations of risk-sensitive learning objectives and optimization procedures that can be used as add-ons to the PyTorch library. We also provide datasets to compare these learning methods. We demonstrate usage of our library through comparing models learned under different risk objectives, optimization performances of different methods for a single objective, and risk assessments of pretrained ImageNet models.
Author Information
William Wong (Carnegie Mellon University)
Audrey Huang (CMU)
Liu Leqi (Carnegie Mellon University)
Kamyar Azizzadenesheli (Purdue University)
Zachary Lipton (Carnegie Mellon University)
More from the Same Authors
-
2021 : Do You See What I See? A Comparison of Radiologist Eye Gaze to Computer Vision Saliency Maps for Chest X-ray Classification »
Jesse Kim · Helen Zhou · Zachary Lipton -
2022 : Domain Adaptation under Open Set Label Shift »
Saurabh Garg · Sivaraman Balakrishnan · Zachary Lipton -
2022 : Unsupervised Learning under Latent Label Shift »
Pranav Mani · Manley Roberts · Saurabh Garg · Zachary Lipton -
2022 : Characterizing Datapoints via Second-Split Forgetting »
Pratyush Maini · Saurabh Garg · Zachary Lipton · Zico Kolter -
2022 : Counterfactual Metrics for Auditing Black-Box Recommender Systems for Ethical Concerns »
Nil-Jana Akpinar · Liu Leqi · Dylan Hadfield-Menell · Zachary Lipton -
2022 Workshop: Principles of Distribution Shift (PODS) »
Elan Rosenfeld · Saurabh Garg · Shibani Santurkar · Jamie Morgenstern · Hossein Mobahi · Zachary Lipton · Andrej Risteski -
2022 Poster: Supervised Learning with General Risk Functionals »
Liu Leqi · Audrey Huang · Zachary Lipton · Kamyar Azizzadenesheli -
2022 Spotlight: Supervised Learning with General Risk Functionals »
Liu Leqi · Audrey Huang · Zachary Lipton · Kamyar Azizzadenesheli -
2021 : RL Explainability & Interpretability Panel »
Ofra Amir · Finale Doshi-Velez · Alan Fern · Zachary Lipton · Omer Gottesman · Niranjani Prasad -
2021 Poster: Correcting Exposure Bias for Link Recommendation »
Shantanu Gupta · Hao Wang · Zachary Lipton · Yuyang Wang -
2021 Spotlight: Correcting Exposure Bias for Link Recommendation »
Shantanu Gupta · Hao Wang · Zachary Lipton · Yuyang Wang -
2021 Poster: RATT: Leveraging Unlabeled Data to Guarantee Generalization »
Saurabh Garg · Sivaraman Balakrishnan · Zico Kolter · Zachary Lipton -
2021 Oral: RATT: Leveraging Unlabeled Data to Guarantee Generalization »
Saurabh Garg · Sivaraman Balakrishnan · Zico Kolter · Zachary Lipton -
2021 Poster: On Proximal Policy Optimization's Heavy-tailed Gradients »
Saurabh Garg · Joshua Zhanson · Emilio Parisotto · Adarsh Prasad · Zico Kolter · Zachary Lipton · Sivaraman Balakrishnan · Ruslan Salakhutdinov · Pradeep Ravikumar -
2021 Spotlight: On Proximal Policy Optimization's Heavy-tailed Gradients »
Saurabh Garg · Joshua Zhanson · Emilio Parisotto · Adarsh Prasad · Zico Kolter · Zachary Lipton · Sivaraman Balakrishnan · Ruslan Salakhutdinov · Pradeep Ravikumar -
2020 Poster: Uncertainty-Aware Lookahead Factor Models for Quantitative Investing »
Lakshay Chauhan · John Alberg · Zachary Lipton -
2019 Poster: Domain Adaptation with Asymmetrically-Relaxed Distribution Alignment »
Yifan Wu · Ezra Winston · Divyansh Kaushik · Zachary Lipton -
2019 Poster: What is the Effect of Importance Weighting in Deep Learning? »
Jonathon Byrd · Zachary Lipton -
2019 Oral: Domain Adaptation with Asymmetrically-Relaxed Distribution Alignment »
Yifan Wu · Ezra Winston · Divyansh Kaushik · Zachary Lipton -
2019 Oral: What is the Effect of Importance Weighting in Deep Learning? »
Jonathon Byrd · Zachary Lipton -
2018 Poster: Detecting and Correcting for Label Shift with Black Box Predictors »
Zachary Lipton · Yu-Xiang Wang · Alexander Smola -
2018 Poster: Born Again Neural Networks »
Tommaso Furlanello · Zachary Lipton · Michael Tschannen · Laurent Itti · Anima Anandkumar -
2018 Oral: Born Again Neural Networks »
Tommaso Furlanello · Zachary Lipton · Michael Tschannen · Laurent Itti · Anima Anandkumar -
2018 Oral: Detecting and Correcting for Label Shift with Black Box Predictors »
Zachary Lipton · Yu-Xiang Wang · Alexander Smola