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Distribution shifts---where the training distribution differs from the test distribution---can substantially degrade the accuracy of machine learning (ML) systems deployed in the wild. Despite their ubiquity in the real-world deployments, these distribution shifts are under-represented in the datasets widely used in the ML community today. To address this gap, we present WILDS, a curated benchmark of 10 datasets reflecting a diverse range of distribution shifts that naturally arise in real-world applications, such as shifts across hospitals for tumor identification; across camera traps for wildlife monitoring; and across time and location in satellite imaging and poverty mapping. On each dataset, we show that standard training yields substantially lower out-of-distribution than in-distribution performance. This gap remains even with models trained by existing methods for tackling distribution shifts, underscoring the need for new methods for training models that are more robust to the types of distribution shifts that arise in practice. To facilitate method development, we provide an open-source package that automates dataset loading, contains default model architectures and hyperparameters, and standardizes evaluations. The full paper, code, and leaderboards are available at https://wilds.stanford.edu.
Author Information
Pang Wei Koh (Stanford University)
Shiori Sagawa (Stanford University)
Henrik Marklund (Stanford)
Sang Michael Xie (Stanford University)
Marvin Zhang (UC Berkeley)
Akshay Balsubramani (Stanford)
Weihua Hu (Stanford University)
Michihiro Yasunaga (Stanford University)
Richard Lanas Phillips (Cornell University)
Irena Gao (Stanford University)
Tony Lee (Stanford University)
Etienne David (INRAE)
Ian Stavness (University of Saskatchewan)
Wei Guo (The University of Tokyo)
Berton Earnshaw (Recursion)
Imran Haque (Recursion)
Sara Beery (Caltech)
Jure Leskovec (Stanford University)
Anshul Kundaje (Stanford University)
Emma Pierson (Microsoft)
Sergey Levine (UC Berkeley)

Sergey Levine received a BS and MS in Computer Science from Stanford University in 2009, and a Ph.D. in Computer Science from Stanford University in 2014. He joined the faculty of the Department of Electrical Engineering and Computer Sciences at UC Berkeley in fall 2016. His work focuses on machine learning for decision making and control, with an emphasis on deep learning and reinforcement learning algorithms. Applications of his work include autonomous robots and vehicles, as well as computer vision and graphics. His research includes developing algorithms for end-to-end training of deep neural network policies that combine perception and control, scalable algorithms for inverse reinforcement learning, deep reinforcement learning algorithms, and more.
Chelsea Finn (Stanford)
Chelsea Finn is an Assistant Professor in Computer Science and Electrical Engineering at Stanford University. Finn's research interests lie in the capability of robots and other agents to develop broadly intelligent behavior through learning and interaction. To this end, her work has included deep learning algorithms for concurrently learning visual perception and control in robotic manipulation skills, inverse reinforcement methods for learning reward functions underlying behavior, and meta-learning algorithms that can enable fast, few-shot adaptation in both visual perception and deep reinforcement learning. Finn received her Bachelor's degree in Electrical Engineering and Computer Science at MIT and her PhD in Computer Science at UC Berkeley. Her research has been recognized through the ACM doctoral dissertation award, the Microsoft Research Faculty Fellowship, the C.V. Ramamoorthy Distinguished Research Award, and the MIT Technology Review 35 under 35 Award, and her work has been covered by various media outlets, including the New York Times, Wired, and Bloomberg. Throughout her career, she has sought to increase the representation of underrepresented minorities within CS and AI by developing an AI outreach camp at Berkeley for underprivileged high school students, a mentoring program for underrepresented undergraduates across four universities, and leading efforts within the WiML and Berkeley WiCSE communities of women researchers.
Percy Liang (Stanford University)
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Sergey Levine -
2019 Workshop: Generative Modeling and Model-Based Reasoning for Robotics and AI »
Aravind Rajeswaran · Emanuel Todorov · Igor Mordatch · William Agnew · Amy Zhang · Joelle Pineau · Michael Chang · Dumitru Erhan · Sergey Levine · Kimberly Stachenfeld · Marvin Zhang -
2019 Workshop: ICML 2019 Workshop on Computational Biology »
Donna Pe'er · Sandhya Prabhakaran · Elham Azizi · Abdoulaye Baniré Diallo · Anshul Kundaje · Barbara Engelhardt · Wajdi Dhifli · Engelbert MEPHU NGUIFO · Wesley Tansey · Julia Vogt · Jennifer Listgarten · Cassandra Burdziak · Workshop CompBio -
2019 Workshop: Workshop on the Security and Privacy of Machine Learning »
Nicolas Papernot · Florian Tramer · Bo Li · Dan Boneh · David Evans · Somesh Jha · Percy Liang · Patrick McDaniel · Jacob Steinhardt · Dawn Song -
2019 Poster: Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables »
Kate Rakelly · Aurick Zhou · Chelsea Finn · Sergey Levine · Deirdre Quillen -
2019 Poster: Position-aware Graph Neural Networks »
Jiaxuan You · Rex (Zhitao) Ying · Jure Leskovec -
2019 Poster: SOLAR: Deep Structured Representations for Model-Based Reinforcement Learning »
Marvin Zhang · Sharad Vikram · Laura Smith · Pieter Abbeel · Matthew Johnson · Sergey Levine -
2019 Oral: Position-aware Graph Neural Networks »
Jiaxuan You · Rex (Zhitao) Ying · Jure Leskovec -
2019 Oral: Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables »
Kate Rakelly · Aurick Zhou · Chelsea Finn · Sergey Levine · Deirdre Quillen -
2019 Oral: SOLAR: Deep Structured Representations for Model-Based Reinforcement Learning »
Marvin Zhang · Sharad Vikram · Laura Smith · Pieter Abbeel · Matthew Johnson · Sergey Levine -
2019 Poster: Learning a Prior over Intent via Meta-Inverse Reinforcement Learning »
Kelvin Xu · Ellis Ratner · Anca Dragan · Sergey Levine · Chelsea Finn -
2019 Poster: EMI: Exploration with Mutual Information »
Hyoungseok Kim · Jaekyeom Kim · Yeonwoo Jeong · Sergey Levine · Hyun Oh Song -
2019 Poster: Online Meta-Learning »
Chelsea Finn · Aravind Rajeswaran · Sham Kakade · Sergey Levine -
2019 Poster: Diagnosing Bottlenecks in Deep Q-learning Algorithms »
Justin Fu · Aviral Kumar · Matthew Soh · Sergey Levine -
2019 Oral: Learning a Prior over Intent via Meta-Inverse Reinforcement Learning »
Kelvin Xu · Ellis Ratner · Anca Dragan · Sergey Levine · Chelsea Finn -
2019 Oral: EMI: Exploration with Mutual Information »
Hyoungseok Kim · Jaekyeom Kim · Yeonwoo Jeong · Sergey Levine · Hyun Oh Song -
2019 Oral: Diagnosing Bottlenecks in Deep Q-learning Algorithms »
Justin Fu · Aviral Kumar · Matthew Soh · Sergey Levine -
2019 Oral: Online Meta-Learning »
Chelsea Finn · Aravind Rajeswaran · Sham Kakade · Sergey Levine -
2019 Tutorial: Meta-Learning: from Few-Shot Learning to Rapid Reinforcement Learning »
Chelsea Finn · Sergey Levine -
2018 Poster: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor »
Tuomas Haarnoja · Aurick Zhou · Pieter Abbeel · Sergey Levine -
2018 Poster: Regret Minimization for Partially Observable Deep Reinforcement Learning »
Peter Jin · EECS Kurt Keutzer · Sergey Levine -
2018 Poster: The Mirage of Action-Dependent Baselines in Reinforcement Learning »
George Tucker · Surya Bhupatiraju · Shixiang Gu · Richard E Turner · Zoubin Ghahramani · Sergey Levine -
2018 Oral: Regret Minimization for Partially Observable Deep Reinforcement Learning »
Peter Jin · EECS Kurt Keutzer · Sergey Levine -
2018 Oral: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor »
Tuomas Haarnoja · Aurick Zhou · Pieter Abbeel · Sergey Levine -
2018 Oral: The Mirage of Action-Dependent Baselines in Reinforcement Learning »
George Tucker · Surya Bhupatiraju · Shixiang Gu · Richard E Turner · Zoubin Ghahramani · Sergey Levine -
2018 Poster: Latent Space Policies for Hierarchical Reinforcement Learning »
Tuomas Haarnoja · Kristian Hartikainen · Pieter Abbeel · Sergey Levine -
2018 Poster: Self-Consistent Trajectory Autoencoder: Hierarchical Reinforcement Learning with Trajectory Embeddings »
John Co-Reyes · Yu Xuan Liu · Abhishek Gupta · Benjamin Eysenbach · Pieter Abbeel · Sergey Levine -
2018 Poster: Universal Planning Networks: Learning Generalizable Representations for Visuomotor Control »
Aravind Srinivas · Allan Jabri · Pieter Abbeel · Sergey Levine · Chelsea Finn -
2018 Poster: GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models »
Jiaxuan You · Rex (Zhitao) Ying · Xiang Ren · Will Hamilton · Jure Leskovec -
2018 Poster: On the Relationship between Data Efficiency and Error for Uncertainty Sampling »
Stephen Mussmann · Percy Liang -
2018 Poster: Fairness Without Demographics in Repeated Loss Minimization »
Tatsunori Hashimoto · Megha Srivastava · Hongseok Namkoong · Percy Liang -
2018 Oral: GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models »
Jiaxuan You · Rex (Zhitao) Ying · Xiang Ren · Will Hamilton · Jure Leskovec -
2018 Oral: Fairness Without Demographics in Repeated Loss Minimization »
Tatsunori Hashimoto · Megha Srivastava · Hongseok Namkoong · Percy Liang -
2018 Oral: Universal Planning Networks: Learning Generalizable Representations for Visuomotor Control »
Aravind Srinivas · Allan Jabri · Pieter Abbeel · Sergey Levine · Chelsea Finn -
2018 Oral: Self-Consistent Trajectory Autoencoder: Hierarchical Reinforcement Learning with Trajectory Embeddings »
John Co-Reyes · Yu Xuan Liu · Abhishek Gupta · Benjamin Eysenbach · Pieter Abbeel · Sergey Levine -
2018 Oral: On the Relationship between Data Efficiency and Error for Uncertainty Sampling »
Stephen Mussmann · Percy Liang -
2018 Oral: Latent Space Policies for Hierarchical Reinforcement Learning »
Tuomas Haarnoja · Kristian Hartikainen · Pieter Abbeel · Sergey Levine -
2017 : Lifelong Learning - Panel Discussion »
Sergey Levine · Joelle Pineau · Balaraman Ravindran · Andrei A Rusu -
2017 : Sergey Levine: Self-supervision as a path to lifelong learning »
Sergey Levine -
2017 Poster: World of Bits: An Open-Domain Platform for Web-Based Agents »
Tim Shi · Andrej Karpathy · Jim Fan · Jonathan Hernandez · Percy Liang -
2017 Poster: Combining Model-Based and Model-Free Updates for Trajectory-Centric Reinforcement Learning »
Yevgen Chebotar · Karol Hausman · Marvin Zhang · Gaurav Sukhatme · Stefan Schaal · Sergey Levine -
2017 Talk: World of Bits: An Open-Domain Platform for Web-Based Agents »
Tim Shi · Andrej Karpathy · Jim Fan · Jonathan Hernandez · Percy Liang -
2017 Talk: Combining Model-Based and Model-Free Updates for Trajectory-Centric Reinforcement Learning »
Yevgen Chebotar · Karol Hausman · Marvin Zhang · Gaurav Sukhatme · Stefan Schaal · Sergey Levine -
2017 Poster: Modular Multitask Reinforcement Learning with Policy Sketches »
Jacob Andreas · Dan Klein · Sergey Levine -
2017 Poster: Developing Bug-Free Machine Learning Systems With Formal Mathematics »
Daniel Selsam · Percy Liang · David L Dill -
2017 Talk: Developing Bug-Free Machine Learning Systems With Formal Mathematics »
Daniel Selsam · Percy Liang · David L Dill -
2017 Poster: Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks »
Chelsea Finn · Pieter Abbeel · Sergey Levine -
2017 Poster: Convexified Convolutional Neural Networks »
Yuchen Zhang · Percy Liang · Martin Wainwright -
2017 Poster: Learning Important Features Through Propagating Activation Differences »
Avanti Shrikumar · Peyton Greenside · Anshul Kundaje -
2017 Poster: Understanding Black-box Predictions via Influence Functions »
Pang Wei Koh · Percy Liang -
2017 Poster: Reinforcement Learning with Deep Energy-Based Policies »
Tuomas Haarnoja · Haoran Tang · Pieter Abbeel · Sergey Levine -
2017 Talk: Modular Multitask Reinforcement Learning with Policy Sketches »
Jacob Andreas · Dan Klein · Sergey Levine -
2017 Talk: Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks »
Chelsea Finn · Pieter Abbeel · Sergey Levine -
2017 Talk: Convexified Convolutional Neural Networks »
Yuchen Zhang · Percy Liang · Martin Wainwright -
2017 Talk: Understanding Black-box Predictions via Influence Functions »
Pang Wei Koh · Percy Liang -
2017 Talk: Reinforcement Learning with Deep Energy-Based Policies »
Tuomas Haarnoja · Haoran Tang · Pieter Abbeel · Sergey Levine -
2017 Talk: Learning Important Features Through Propagating Activation Differences »
Avanti Shrikumar · Peyton Greenside · Anshul Kundaje -
2017 Tutorial: Deep Reinforcement Learning, Decision Making, and Control »
Sergey Levine · Chelsea Finn