Niekum, Scott

65 publications

ICLR 2026 Regularized Latent Dynamics Prediction Is a Strong Baseline for Behavioral Foundation Models Pranaya Jajoo, Harshit Sikchi, Siddhant Agarwal, Amy Zhang, Scott Niekum, Martha White

ICLR 2025 An Optimal Discriminator Weighted Imitation Perspective for Reinforcement Learning Haoran Xu, Shuozhe Li, Harshit Sikchi, Scott Niekum, Amy Zhang

ICLR 2025 Null Counterfactual Factor Interactions for Goal-Conditioned Reinforcement Learning Caleb Chuck, Fan Feng, Carl Qi, Chang Shi, Siddhant Agarwal, Amy Zhang, Scott Niekum

ICLRW 2025 RL Zero: Zero-Shot Language to Behaviors Without Any Supervision Harshit Sikchi, Siddhant Agarwal, Pranaya Jajoo, Samyak Parajuli, Caleb Chuck, Max Rudolph, Peter Stone, Amy Zhang, Scott Niekum

NeurIPS 2025 RLZero: Direct Policy Inference from Language Without In-Domain Supervision Harshit Sikchi, Siddhant Agarwal, Pranaya Jajoo, Samyak Parajuli, Caleb Chuck, Max Rudolph, Peter Stone, Amy Zhang, Scott Niekum

CoRL 2024 A Dual Approach to Imitation Learning from Observations with Offline Datasets Harshit Sikchi, Caleb Chuck, Amy Zhang, Scott Niekum

ICLR 2024 Contrastive Preference Learning: Learning from Human Feedback Without Reinforcement Learning Joey Hejna, Rafael Rafailov, Harshit Sikchi, Chelsea Finn, Scott Niekum, W. Bradley Knox, Dorsa Sadigh

JMLR 2024 Data-Efficient Policy Evaluation Through Behavior Policy Search Josiah P. Hanna, Yash Chandak, Philip S. Thomas, Martha White, Peter Stone, Scott Niekum

ICLR 2024 Dual RL: Unification and New Methods for Reinforcement and Imitation Learning Harshit Sikchi, Qinqing Zheng, Amy Zhang, Scott Niekum

TMLR 2024 Granger Causal Interaction Skill Chains Caleb Chuck, Kevin Black, Aditya Arjun, Yuke Zhu, Scott Niekum

AAAI 2024 Learning Optimal Advantage from Preferences and Mistaking It for Reward W. Bradley Knox, Stephane Hatgis-Kessell, Sigurdur O. Adalgeirsson, Serena Booth, Anca D. Dragan, Peter Stone, Scott Niekum

TMLR 2024 Models of Human Preference for Learning Reward Functions W. Bradley Knox, Stephane Hatgis-Kessell, Serena Booth, Scott Niekum, Peter Stone, Alessandro G Allievi

NeurIPSW 2024 Pareto-Optimal Learning from Preferences with Hidden Context Ryan Boldi, Li Ding, Lee Spector, Scott Niekum

NeurIPS 2024 Predicting Future Actions of Reinforcement Learning Agents Stephen Chung, Scott Niekum, David Krueger

NeurIPS 2024 Scaling Laws for Reward Model Overoptimization in Direct Alignment Algorithms Rafael Rafailov, Yaswanth Chittepu, Ryan Park, Harshit Sikchi, Joey Hejna, W. Bradley Knox, Chelsea Finn, Scott Niekum

ICMLW 2024 Scaling Laws for Reward Model Overoptimization in Direct Alignment Algorithms Rafael Rafailov, Yaswanth Chittepu, Ryan Park, Harshit Sikchi, Joey Hejna, W. Bradley Knox, Chelsea Finn, Scott Niekum

ICLR 2024 Score Models for Offline Goal-Conditioned Reinforcement Learning Harshit Sikchi, Rohan Chitnis, Ahmed Touati, Alborz Geramifard, Amy Zhang, Scott Niekum

NeurIPS 2024 SkiLD: Unsupervised Skill Discovery Guided by Factor Interactions Zizhao Wang, Jiaheng Hu, Caleb Chuck, Stephen Chen, Roberto Martín-Martín, Amy Zhang, Scott Niekum, Peter Stone

TMLR 2023 A Ranking Game for Imitation Learning Harshit Sikchi, Akanksha Saran, Wonjoon Goo, Scott Niekum

ICMLW 2023 A Ranking Game for Imitation Learning Harshit Sikchi, Akanksha Saran, Wonjoon Goo, Scott Niekum

NeurIPSW 2023 Hierarchical Empowerment: Toward Tractable Empowerment-Based Skill Learning Andrew Levy, Sreehari Rammohan, Alessandro Allievi, Scott Niekum, George Konidaris

ICLRW 2023 Imitation from Arbitrary Experience: A Dual Unification of Reinforcement and Imitation Learning Methods Harshit Sikchi, Amy Zhang, Scott Niekum

ICMLW 2023 Learning Optimal Advantage from Preferences and Mistaking It for Reward W. Bradley Knox, Stephane Hatgis-Kessell, Sigurdur Orn Adalgeirsson, Serena Booth, Anca Dragan, Peter Stone, Scott Niekum

NeurIPSW 2023 Score-Models for Offline Goal-Conditioned Reinforcement Learning Harshit Sikchi, Rohan Chitnis, Ahmed Touati, Alborz Geramifard, Amy Zhang, Scott Niekum

AAAI 2023 The Perils of Trial-and-Error Reward Design: Misdesign Through Overfitting and Invalid Task Specifications Serena Booth, W. Bradley Knox, Julie Shah, Scott Niekum, Peter Stone, Alessandro Allievi

NeurIPSW 2022 A Ranking Game for Imitation Learning Harshit Sikchi, Akanksha Saran, Wonjoon Goo, Scott Niekum

L4DC 2022 Can Foundation Models Perform Zero-Shot Task Specification for Robot Manipulation? Yuchen Cui, Scott Niekum, Abhinav Gupta, Vikash Kumar, Aravind Rajeswaran

ICLR 2022 Fairness Guarantees Under Demographic Shift Stephen Giguere, Blossom Metevier, Bruno Castro da Silva, Yuriy Brun, Philip S. Thomas, Scott Niekum

NeurIPSW 2022 Language-Guided Task Adaptation for Imitation Learning Prasoon Goyal, Ray Mooney, Scott Niekum

JMLR 2021 A Review of Robot Learning for Manipulation: Challenges, Representations, and Algorithms Oliver Kroemer, Scott Niekum, George Konidaris

NeurIPS 2021 Adversarial Intrinsic Motivation for Reinforcement Learning Ishan Durugkar, Mauricio Tec, Scott Niekum, Peter Stone

AAAI 2021 Demonstration of the EMPATHIC Framework for Task Learning from Implicit Human Feedback Yuchen Cui, Qiping Zhang, Sahil Jain, Alessandro Allievi, Peter Stone, Scott Niekum, W. Bradley Knox

CoRL 2021 Distributional Depth-Based Estimation of Object Articulation Models Ajinkya Jain, Stephen Giguere, Rudolf Lioutikov, Scott Niekum

MLJ 2021 Importance Sampling in Reinforcement Learning with an Estimated Behavior Policy Josiah P. Hanna, Scott Niekum, Peter Stone

CoRL 2021 SCAPE: Learning Stiffness Control from Augmented Position Control Experiences Mincheol Kim, Scott Niekum, Ashish D. Deshpande

NeurIPS 2021 SOPE: Spectrum of Off-Policy Estimators Christina Yuan, Yash Chandak, Stephen Giguere, Philip S. Thomas, Scott Niekum

IJCAI 2021 Understanding the Relationship Between Interactions and Outcomes in Human-in-the-Loop Machine Learning Yuchen Cui, Pallavi Koppol, Henny Admoni, Scott Niekum, Reid G. Simmons, Aaron Steinfeld, Tesca Fitzgerald

NeurIPS 2021 Universal Off-Policy Evaluation Yash Chandak, Scott Niekum, Bruno da Silva, Erik Learned-Miller, Emma Brunskill, Philip S. Thomas

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

CoRL 2021 You Only Evaluate Once: A Simple Baseline Algorithm for Offline RL Wonjoon Goo, Scott Niekum

NeurIPS 2020 Bayesian Robust Optimization for Imitation Learning Daniel Brown, Scott Niekum, Marek Petrik

IJCAI 2020 Human Gaze Assisted Artificial Intelligence: A Review Ruohan Zhang, Akanksha Saran, Bo Liu, Yifeng Zhu, Sihang Guo, Scott Niekum, Dana H. Ballard, Mary M. Hayhoe

CoRL 2020 PixL2R: Guiding Reinforcement Learning Using Natural Language by Mapping Pixels to Rewards Prasoon Goyal, Scott Niekum, Raymond Mooney

ICMLW 2020 PixL2R: Guiding Reinforcement Learning Using Natural Language by Mapping Pixels to Rewards Prasoon Goyal, Scott Niekum, Ray Mooney

ICML 2020 Safe Imitation Learning via Fast Bayesian Reward Inference from Preferences Daniel Brown, Russell Coleman, Ravi Srinivasan, Scott Niekum

CoRL 2020 The EMPATHIC Framework for Task Learning from Implicit Human Feedback Yuchen Cui, Qiping Zhang, Brad Knox, Alessandro Allievi, Peter Stone, 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

ICML 2019 Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations Daniel Brown, Wonjoon Goo, Prabhat Nagarajan, Scott Niekum

ICML 2019 Importance Sampling Policy Evaluation with an Estimated Behavior Policy Josiah Hanna, Scott Niekum, Peter Stone

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

CoRL 2019 Understanding Teacher Gaze Patterns for Robot Learning Akanksha Saran, Elaine Schaertl Short, Andrea Thomaz, Scott Niekum

IJCAI 2019 Using Natural Language for Reward Shaping in Reinforcement Learning Prasoon Goyal, Scott Niekum, Raymond J. Mooney

CoRL 2018 Efficient Hierarchical Robot Motion Planning Under Uncertainty and Hybrid Dynamics Ajinkya Jain, 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

AAAI 2018 Safe Reinforcement Learning via Shielding Mohammed Alshiekh, Roderick Bloem, Rüdiger Ehlers, Bettina Könighofer, Scott Niekum, Ufuk Topcu

AAAI 2017 Bootstrapping with Models: Confidence Intervals for Off-Policy Evaluation Josiah P. Hanna, Peter Stone, Scott Niekum

ICML 2017 Data-Efficient Policy Evaluation Through Behavior Policy Search Josiah P. Hanna, Philip S. Thomas, Peter Stone, Scott Niekum

ICML 2016 On the Analysis of Complex Backup Strategies in Monte Carlo Tree Search Piyush Khandelwal, Elad Liebman, Scott Niekum, Peter Stone

NeurIPS 2015 Policy Evaluation Using the Ω-Return Philip S. Thomas, Scott Niekum, Georgios Theocharous, George Konidaris

AAAI 2012 Complex Task Learning from Unstructured Demonstrations Scott Niekum

NeurIPS 2011 Clustering via Dirichlet Process Mixture Models for Portable Skill Discovery Scott Niekum, Andrew G. Barto

NeurIPS 2011 TD_gamma: Re-Evaluating Complex Backups in Temporal Difference Learning George Konidaris, Scott Niekum, Philip S. Thomas

AAAI 2010 Evolved Intrinsic Reward Functions for Reinforcement Learning Scott Niekum