Brown, Daniel S.

20 publications

CoRL 2025 Agreement Volatility: A Second-Order Metric for Uncertainty Quantification in Surgical Robot Learning Jordan Thompson, Britton Jordan, Daniel S. Brown, Alan Kuntz

AAAI 2025 Leveraging Human Input to Enable Robust, Interactive, and Aligned AI Systems Daniel S. Brown

TMLR 2023 Benchmarks and Algorithms for Offline Preference-Based Reward Learning Daniel Shin, Anca Dragan, Daniel S. Brown

ICLR 2023 Causal Confusion and Reward Misidentification in Preference-Based Reward Learning Jeremy Tien, Jerry Zhi-Yang He, Zackory Erickson, Anca Dragan, Daniel S. Brown

ICML 2023 Contextual Reliability: When Different Features Matter in Different Contexts Gaurav Rohit Ghosal, Amrith Setlur, Daniel S. Brown, Anca Dragan, Aditi Raghunathan

CoRL 2023 Quantifying Assistive Robustness via the Natural-Adversarial Frontier Jerry Zhi-Yang He, Daniel S. Brown, Zackory Erickson, Anca Dragan

AAAI 2023 The Effect of Modeling Human Rationality Level on Learning Rewards from Multiple Feedback Types Gaurav R. Ghosal, Matthew Zurek, Daniel S. Brown, Anca D. Dragan

NeurIPSW 2022 Interpretable Reward Learning via Differentiable Decision Trees Akansha Kalra, Daniel S. Brown

ICMLW 2022 A Study of Causal Confusion in Preference-Based Reward Learning Jeremy Tien, Jerry Zhi-Yang He, Zackory Erickson, Anca Dragan, Daniel S. Brown

TMLR 2022 Bayesian Methods for Constraint Inference in Reinforcement Learning Dimitris Papadimitriou, Usman Anwar, Daniel S. Brown

CoRL 2022 Learning Representations That Enable Generalization in Assistive Tasks Jerry Zhi-Yang He, Zackory Erickson, Daniel S. Brown, Aditi Raghunathan, Anca Dragan

L4DC 2021 Optimal Cost Design for Model Predictive Control Avik Jain, Lawrence Chan, Daniel S. Brown, Anca D. Dragan

ICML 2021 Policy Gradient Bayesian Robust Optimization for Imitation Learning Zaynah Javed, Daniel S Brown, Satvik Sharma, Jerry Zhu, Ashwin Balakrishna, Marek Petrik, Anca Dragan, Ken Goldberg

CoRL 2021 ThriftyDAgger: Budget-Aware Novelty and Risk Gating for Interactive Imitation Learning Ryan Hoque, Ashwin Balakrishna, Ellen Novoseller, Albert Wilcox, Daniel S. Brown, Ken Goldberg

ICML 2021 Value Alignment Verification Daniel S Brown, Jordan Schneider, Anca Dragan, Scott Niekum

NeurIPSW 2020 Value Alignment Verification Daniel S. Brown, Jordan Schneider, Scott Niekum

CoRL 2019 Better-than-Demonstrator Imitation Learning via Automatically-Ranked Demonstrations Daniel S. Brown, Wonjoon Goo, Scott Niekum

AAAI 2019 Machine Teaching for Inverse Reinforcement Learning: Algorithms and Applications Daniel S. Brown, Scott Niekum

AAAI 2018 Efficient Probabilistic Performance Bounds for Inverse Reinforcement Learning Daniel S. Brown, Scott Niekum

CoRL 2018 Risk-Aware Active Inverse Reinforcement Learning Daniel S. Brown, Yuchen Cui, Scott Niekum