Publications

Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey
Sanmit Narvekar, Bei Peng, Matteo Leonetti, Jivko Sinapov, Matthew E. Taylor, and Peter Stone
JMLR, 2020
End-to-End Learning for Retrospective Change-Point Estimation
Corinne Jones, Zaid Harchaoui
MLSP, 2020
Good Systems, Bad Data? Interpretations of AI Hype and Failures
Stephen C. Slota, Kenneth R. Fleischmann, Sherri Greenberg, Nitin Verma, Brenna Cummings, Lan Li, Chris Shenefiel
asis&t, 2020
Generalizing Curricula for Reinforcement Learning
Sanmit Narvekar and Peter Stone
ICML, 2020
First-order Optimization for Superquantile-based Supervised Learning
Yassine Laguel, Jérome Malick, Zaid Harchaoui
MLSP, 2020
Is Reinforcement Learning More Difficult Than Bandits? A Near-optimal Algorithm Escaping the Curse of Horizon
Zihan Zhang, Xiangyang Ji, Simon S. Du
arXiv, 2020
Safe Imitation Learning via Fast Bayesian Reward Inference from Preferences
Daniel S. Brown, Russell Coleman, Ravi Srinivasan, Scott Niekum
ICML, 2020
Learning Deep ReLU Networks is Fixed Parameter Tractable
Sitan Chen, Adam R. Klivans, Raghu Meka
arXiv, 2020
Learning to Improve Multi-Robot Hallway Navigation
Jin-Soo Park, Brian Tsang, Harel Yedidsion, Garrett Warnell, Daehyun Kyoung, and Peter Stone
CoRL, 2020
Entanglement is Necessary for Optimal Quantum Property Testing
Sebastien Bubeck, Sitan Chen, Jerry Li
arXiv, 2020
Adaptive Sampling to Reduce Disparate Performance
Jacob Abernethy, Pranjal Awasthi, Matthäus Kleindessner, Jamie Morgenstern, Jie (Claire) Zhang
arXiv, 2020
Value Alignment Verification
Daniel S. Brown, Jordan Schneioder, Scott Niekum
NeurIPS, 2020
Superpolynomial Lower Bounds for Learning One-Layer Neural Networks using Gradient Descent.
Surbhi Goel, Aravind Gollakota, Zhihan Jin, Sushrut Karmalkar, Adam Klivans
ICML, 2020
Kernel and Rich Regimes in Overparametrized Models
Blake Woodworth, Suriya Gunasekar, Jason D. Lee, Edward Moroshko, Pedro Savarese, Itay Golan, Daniel Soudry, Nathan Srebro
PMLR, 2020
RIDM: Reinforced Inverse Dynamics Modeling for Learning from a Single Observed Demonstration
Brahma Pavse, Faraz Torabi, Josiah Hanna, Garrett Warnell, and Peter Stone
IROS, 2020
Reducing Sampling Error in Batch Temporal Difference Learning
Brahma Pavse, Ishan Durugkar, Josiah Hanna, and Peter Stone
ICML, 2020
The EMPATHIC Framework for Task Learning from Implicit Human Feedback
Yuchen Cui, Qiping Zhang, Alessandro Allievi, Peter Stone, Scott Niekum, W. Bradley Knox
CRL, 2020
On Sampling Error in Batch Action-Value Prediction Algorithms
Brahma S. Pavse, Josiah P. Hanna, Ishan Durugkar, and Peter Stone
NeurIPS, 2020