More from the Same Authors

• 2021 : A Practical Notation for Information-Theoretic Quantities between Outcomes and Random Variables »
  Andreas Kirsch · Yarin Gal
• 2021 : Active Learning under Pool Set Distribution Shift and Noisy Data »
  Andreas Kirsch · Tom Rainforth · Yarin Gal
• 2021 : Batch Active Learning with Stochastic Acquisition Functions »
  Andreas Kirsch · Sebastian Farquhar · Yarin Gal
• 2021 : Order in the Court: Explainable AI Methods Prone to Disagreement »
  · Michael Neely · Stefan F. Schouten · Ana Lucic
• 2021 : Counterfactual Explanations for Graph Neural Networks »
  Ana Lucic · Maartje ter Hoeve · Gabriele Tolomei · Maarten de Rijke · Fabrizio Silvestri
• 2021 : Convergence of a Human-in-the-Loop Policy-Gradient Algorithm With Eligibility Trace Under Reward, Policy, and Advantage Feedback »
  Ishaan Shah
• 2021 : Flexible Interpretability through Optimizable Counterfactual Explanations for Tree Ensembles »
  Ana Lucic · Harrie Oosterhuis · Hinda Haned · Maarten de Rijke
• 2021 : Pretrained Encoders are All You Need »
  Mina Khan · Advait Rane · Srivatsa P · Shriram Chenniappa · Rishabh Anand · Sherjil Ozair · Patricia Maes
• 2021 : Of Moments and Matching: A Game-Theoretic Framework for Closing the Imitation Gap »
  Gokul Swamy · Sanjiban Choudhury · J. Bagnell · Steven Wu
• 2021 : Improving Human Decision-Making with Machine Learning »
  Hamsa Bastani · Osbert Bastani · Wichinpong Sinchaisri
• 2021 : Scalable Algorithms for Nonlinear Causal Inference »
  Gokul Swamy · Sanjiban Choudhury · James Bagnell · Steven Wu
• 2021 : Differentiable Learning Under Triage »
  Nastaran Okati · Abir De · Manuel Gomez-Rodriguez
• 2021 : Improving Human Decision-Making with Machine Learning »
  Hamsa Bastani · Osbert Bastani · Wichinpong Sinchaisri
• 2021 : To Trust or Not to Trust a Regressor: Estimating and Explaining Trustworthiness of Regression Predictions »
  Kim de Bie · Ana Lucic · Hinda Haned
• 2021 : CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networks »
  Ana Lucic · Maartje ter Hoeve · Gabriele Tolomei · Maarten de Rijke · Fabrizio Silvestri
• 2021 : Personalizing Pretrained Models »
  Mina Khan · Advait Rane · Pattie Maes
• 2021 : Convergence of a Human-in-the-Loop Policy-Gradient Algorithm With Eligibility Trace Under Reward, Policy, and Advantage Feedback »
  Ishaan Shah · David Halpern · Michael L. Littman · Kavosh Asadi
• 2021 : Effect of Combination of HBM and Certainty Sampling onWorkload of Semi-Automated Grey Literature Screening »
  JINGHUI LU · Brian Mac Namee
• 2021 : A Simple Baseline for Batch Active Learning with Stochastic Acquisition Functions »
  Andreas Kirsch · Sebastian Farquhar · Yarin Gal
• 2021 : Active Learning under Pool Set Distribution Shift and Noisy Data »
  Andreas Kirsch · Tom Rainforth · Yarin Gal
• 2021 : Explaining Reinforcement Learning Policies through Counterfactual Trajectories »
  Julius Frost · Olivia Watkins · Eric Weiner · Pieter Abbeel · Trevor Darrell · Bryan Plummer · Kate Saenko
• 2021 : Explicable Policy Search via Preference-Based Learning under Human Biases »
  Ze Gong · Yu Zhang
• 2021 : Of Moments and Matching: A Game-Theoretic Framework for Closing the Imitation Gap »
  Gokul Swamy · Sanjiban Choudhury · James Bagnell · Steven Wu
• 2021 : On The State of Data In Computer Vision: Human Annotations Remain Indispensable for Developing Deep Learning Models. »
  Zeyad Emam · Sasha Harrison · Felix Lau · Ah Young Kim
• 2021 : Less is more: An Empirical Analysis of Model Compression for Dialogue »
  Ahmed Baruwa
• 2021 : Mitigating Sampling Bias and Improving Robustness in Active Learning »
  Ranganath Krishnan · Alok Sinha · Nilesh Ahuja · Mahesh Subedar · Omesh Tickoo · Ravi Iyer
• 2022 : Plex: Towards Reliability using Pretrained Large Model Extensions »
  Dustin Tran · Andreas Kirsch · Balaji Lakshminarayanan · Huiyi Hu · Du Phan · D. Sculley · Jasper Snoek · Jeremiah Liu · Jie Ren · Joost van Amersfoort · Kehang Han · E. Kelly Buchanan · Kevin Murphy · Mark Collier · Mike Dusenberry · Neil Band · Nithum Thain · Rodolphe Jenatton · Tim G. J Rudner · Yarin Gal · Zachary Nado · Zelda Mariet · Zi Wang · Zoubin Ghahramani
• 2023 Poster: On the Within-Group Discrimination of Screening Classifiers »
  Nastaran Okati · Stratis Tsirtsis · Manuel Gomez-Rodriguez
• 2023 Poster: Inverse Reinforcement Learning without Reinforcement Learning »
  Gokul Swamy · David Wu · Sanjiban Choudhury · J. Bagnell · Steven Wu
• 2023 Poster: Improving Expert Predictions with Conformal Prediction »
  Eleni Straitouri · Luke Lequn Wang · Nastaran Okati · Manuel Gomez-Rodriguez
• 2022 Poster: Causal Imitation Learning under Temporally Correlated Noise »
  Gokul Swamy · Sanjiban Choudhury · James Bagnell · Steven Wu
• 2022 Oral: Causal Imitation Learning under Temporally Correlated Noise »
  Gokul Swamy · Sanjiban Choudhury · James Bagnell · Steven Wu
• 2021 : Active Learning under Pool Set Distribution Shift and Noisy Data »
  Yarin Gal · Tom Rainforth · Andreas Kirsch
• 2021 Workshop: ICML Workshop on Algorithmic Recourse »
  Stratis Tsirtsis · Amir-Hossein Karimi · Ana Lucic · Manuel Gomez-Rodriguez · Isabel Valera · Hima Lakkaraju
• 2021 Workshop: ICML Workshop on Human in the Loop Learning (HILL) »
  Trevor Darrell · Xin Wang · Li Erran Li · Fisher Yu · Zeynep Akata · Wenwu Zhu · Pradeep Ravikumar · Shiji Zhou · Shanghang Zhang · Kalesha Bullard
• 2021 : Opening Remark »
  Shanghang Zhang · Shiji Zhou
• 2021 Poster: Of Moments and Matching: A Game-Theoretic Framework for Closing the Imitation Gap »
  Gokul Swamy · Sanjiban Choudhury · J. Bagnell · Steven Wu
• 2021 Spotlight: Of Moments and Matching: A Game-Theoretic Framework for Closing the Imitation Gap »
  Gokul Swamy · Sanjiban Choudhury · J. Bagnell · Steven Wu