Real-Time Detection, Tracking and Classification of Multiple Moving Objects in UAV Videos. - Huseyin Can Baykara; Erdem Biyik; Gamze Gül; Deniz Onural; Ahmet Safa Ozturk (ICTAI, 2017)

Generalized approximate message-passing decoder for universal sparse superposition codes. - Erdem Biyik; Jean Barbier; Mohamad Dia (ISIT, 2017)

Batch Active Preference-Based Learning of Reward Functions. - Erdem Biyik; Dorsa Sadigh (CoRL, 2018)

Altruistic Autonomy: Beating Congestion on Shared Roads. - Erdem Biyik; Daniel A. Lazar; Ramtin Pedarsani; Dorsa Sadigh (WAFR, 2018)

Efficient and Safe Exploration in Deterministic Markov Decision Processes with Unknown Transition Models. - Erdem Biyik; Jonathan Margoliash; Shahrouz Ryan Alimo; Dorsa Sadigh (ACC, 2019)

The Green Choice: Learning and Influencing Human Decisions on Shared Roads. - Erdem Biyik; Daniel A. Lazar; Dorsa Sadigh; Ramtin Pedarsani (CDC, 2019)

Asking Easy Questions: A User-Friendly Approach to Active Reward Learning. - Erdem Biyik; Malayandi Palan; Nicholas C. Landolfi; Dylan P. Losey; Dorsa Sadigh (CoRL, 2019)

Active Learning of Reward Dynamics from Hierarchical Queries. - Chandrayee Basu; Erdem Biyik; Zhixun He; Mukesh Singhal; Dorsa Sadigh (IROS, 2019)

Batch Active Learning Using Determinantal Point Processes. - Erdem Biyik; Kenneth Wang 003; Nima Anari; Dorsa Sadigh (CoRR, 2019)

Learning How to Dynamically Route Autonomous Vehicles on Shared Roads. - Daniel A. Lazar; Erdem Biyik; Dorsa Sadigh; Ramtin Pedarsani (CoRR, 2019)

When Humans Aren't Optimal: Robots that Collaborate with Risk-Aware Humans. - Minae Kwon; Erdem Biyik; Aditi Talati; Karan Bhasin; Dylan P. Losey; Dorsa Sadigh (HRI, 2020)

Multi-Agent Safe Planning with Gaussian Processes. - Zheqing Zhu; Erdem Biyik; Dorsa Sadigh (IROS, 2020)

Active Preference-Based Gaussian Process Regression for Reward Learning. - Erdem Biyik; Nicolas Huynh; Mykel J. Kochenderfer; Dorsa Sadigh (Robotics: Science and Systems, 2020)

Reinforcement Learning based Control of Imitative Policies for Near-Accident Driving. - Zhangjie Cao; Erdem Biyik; Woodrow Z. Wang; Allan Raventos; Adrien Gaidon; Guy Rosman; Dorsa Sadigh (Robotics: Science and Systems, 2020)

Learning Multimodal Rewards from Rankings. - Vivek Myers; Erdem Biyik; Nima Anari; Dorsa Sadigh (CoRL, 2021)

Learning Reward Functions from Scale Feedback. - Nils Wilde; Erdem Biyik; Dorsa Sadigh; Stephen L. Smith 001 (CoRL, 2021)

Incentivizing routing choices for safe and efficient transportation in the face of the COVID-19 pandemic. - Mark Beliaev; Erdem Biyik; Daniel A. Lazar; Woodrow Z. Wang; Dorsa Sadigh; Ramtin Pedarsani (ICCPS, 2021)

ROIAL: Region of Interest Active Learning for Characterizing Exoskeleton Gait Preference Landscapes. - Kejun Li; Maegan Tucker; Erdem Biyik; Ellen R. Novoseller; Joel W. Burdick; Yanan Sui; Dorsa Sadigh; Yisong Yue; Aaron D. Ames (ICRA, 2021)

Emergent Prosociality in Multi-Agent Games Through Gifting. - Woodrow Z. Wang; Mark Beliaev; Erdem Biyik; Daniel A. Lazar; Ramtin Pedarsani; Dorsa Sadigh (IJCAI, 2021)

Preference-based Learning of Reward Function Features. - Sydney M. Katz; Amir Maleki; Erdem Biyik; Mykel J. Kochenderfer (CoRR, 2021)

Incentivizing Efficient Equilibria in Traffic Networks With Mixed Autonomy. - Erdem Biyik; Daniel A. Lazar; Ramtin Pedarsani; Dorsa Sadigh (IEEE Trans. Control. Netw. Syst., 2021)

Partner-Aware Algorithms in Decentralized Cooperative Bandit Teams. - Erdem Biyik; Anusha Lalitha; Rajarshi Saha; Andrea Goldsmith; Dorsa Sadigh (AAAI, 2022)

How do people incorporate advice from artificial agents when making physical judgments? - Erik Brockbank; Haoliang Wang; Justin Yang; Suvir Mirchandani; Erdem Biyik; Dorsa Sadigh; Judith E. Fan (CogSci, 2022)

Learning from Humans for Adaptive Interaction. - Erdem Biyik (HRI, 2022)

APReL: A Library for Active Preference-based Reward Learning Algorithms. - Erdem Biyik; Aditi Talati; Dorsa Sadigh (HRI, 2022)

Leveraging Smooth Attention Prior for Multi-Agent Trajectory Prediction. - Zhangjie Cao; Erdem Biyik; Guy Rosman; Dorsa Sadigh (ICRA, 2022)

Assistive Teaching of Motor Control Tasks to Humans. - Megha Srivastava; Erdem Biyik; Suvir Mirchandani; Noah D. Goodman; Dorsa Sadigh (NeurIPS, 2022)

Learning Preferences for Interactive Autonomy. - Erdem Biyik (CoRR, 2022)

Learning reward functions from diverse sources of human feedback: Optimally integrating demonstrations and preferences. - Erdem Biyik; Dylan P. Losey; Malayandi Palan; Nicholas C. Landolfi; Gleb Shevchuk; Dorsa Sadigh (Int. J. Robotics Res., 2022)

Active Reward Learning from Online Preferences. - Vivek Myers; Erdem Biyik; Dorsa Sadigh (ICRA, 2023)

RoboCLIP: One Demonstration is Enough to Learn Robot Policies. - Sumedh Sontakke; Jesse Zhang; Sébastien M. R. Arnold; Karl Pertsch; Erdem Biyik; Dorsa Sadigh; Chelsea Finn; Laurent Itti (NeurIPS, 2023)

Preference Elicitation with Soft Attributes in Interactive Recommendation. - Erdem Biyik; Fan Yao; Yinlam Chow; Alex Haig; Chih-Wei Hsu; Mohammad Ghavamzadeh; Craig Boutilier (CoRR, 2023)

Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback. - Stephen Casper; Xander Davies; Claudia Shi; Thomas Krendl Gilbert; Jérémy Scheurer; Javier Rando; Rachel Freedman; Tomasz Korbak; David Lindner; Pedro Freire; Tony Tong Wang; Samuel Marks; et al. (Trans. Mach. Learn. Res., 2023)

Trajectory Improvement and Reward Learning from Comparative Language Feedback. - Zhaojing Yang; Miru Jun; Jeremy Tien; Stuart Russell 001; Anca D. Dragan; Erdem Biyik (CoRL, 2024)

EXTRACT: Efficient Policy Learning by Extracting Transferable Robot Skills from Offline Data. - Jesse Zhang; Minho Heo; Zuxin Liu; Erdem Biyik; Joseph J. Lim; Yao Liu 009; Rasool Fakoor (CoRL, 2024)

Accurate and Data-Efficient Toxicity Prediction when Annotators Disagree. - Harbani Jaggi; Kashyap Coimbatore Murali; Eve Fleisig; Erdem Biyik (EMNLP, 2024)

Coprocessor Actor Critic: A Model-Based Reinforcement Learning Approach For Adaptive Brain Stimulation. - Michelle Pan; Mariah L. Schrum; Vivek Myers; Erdem Biyik; Anca D. Dragan (ICML, 2024)

RL-VLM-F: Reinforcement Learning from Vision Language Foundation Model Feedback. - Yufei Wang 007; Zhanyi Sun; Jesse Zhang; Zhou Xian; Erdem Biyik; David Held; Zackory Erickson (ICML, 2024)

A Generalized Acquisition Function for Preference-based Reward Learning. - Evan Ellis; Gaurav R. Ghosal; Stuart J. Russell; Anca D. Dragan; Erdem Biyik (ICRA, 2024)

ViSaRL: Visual Reinforcement Learning Guided by Human Saliency. - Anthony Liang; Jesse Thomason; Erdem Biyik (IROS, 2024)

DynaMITE-RL: A Dynamic Model for Improved Temporal Meta-Reinforcement Learning. - Anthony Liang; Guy Tennenholtz; Chih-Wei Hsu; Yinlam Chow; Erdem Biyik; Craig Boutilier (NeurIPS, 2024)

EXTRACT: Efficient Policy Learning by Extracting Transferrable Robot Skills from Offline Data. - Jesse Zhang; Minho Heo; Zuxin Liu; Erdem Biyik; Joseph J. Lim; Yao Liu 009; Rasool Fakoor (CoRR, 2024)

Mitigating Suboptimality of Deterministic Policy Gradients in Complex Q-functions. - Ayush Jain 003; Norio Kosaka; Xinhu Li; Kyung-Min Kim; Erdem Biyik; Joseph J. Lim (CoRR, 2024)

NaVILA: Legged Robot Vision-Language-Action Model for Navigation. - An-Chieh Cheng; Yandong Ji; Zhaojing Yang; Xueyan Zou; Jan Kautz; Erdem Biyik; Hongxu Yin; Sifei Liu; Xiaolong Wang 004 (CoRR, 2024)

Active preference-based Gaussian process regression for reward learning and optimization. - Erdem Biyik; Nicolas Huynh; Mykel J. Kochenderfer; Dorsa Sadigh (Int. J. Robotics Res., 2024)

Batch Active Learning of Reward Functions from Human Preferences. - Erdem Biyik; Nima Anari; Dorsa Sadigh (ACM Trans. Hum. Robot Interact., 2024)

Efficient Robot Learning via Interaction with Humans. - Erdem Biyik (AAAI, 2025)

MILE: Model-based Intervention Learning. - Yigit Korkmaz; Erdem Biyik (CoRR, 2025)

RAILGUN: A Unified Convolutional Policy for Multi-Agent Path Finding Across Different Environments and Tasks. - Yimin Tang; Xiao Xiong; Jingyi Xi; Jiaoyang Li 001; Erdem Biyik; Sven Koenig (CoRR, 2025)

Multi-Agent Inverse Q-Learning from Demonstrations. - Nathaniel Haynam; Adam Khoja; Dhruv Kumar 009; Vivek Myers; Erdem Biyik (CoRR, 2025)

IMPACT: Intelligent Motion Planning with Acceptable Contact Trajectories via Vision-Language Models. - Yiyang Ling; Karan Owalekar; Oluwatobiloba Adesanya; Erdem Biyik; Daniel Seita (CoRR, 2025)

CLAM: Continuous Latent Action Models for Robot Learning from Unlabeled Demonstrations. - Anthony Liang; Pavel Czempin; Matthew Hong 002; Yutai Zhou; Erdem Biyik; Stephen Tu (CoRR, 2025)

ReWiND: Language-Guided Rewards Teach Robot Policies without New Demonstrations. - Jiahui Zhang; Yusen Luo; Abrar Anwar; Sumedh Anand Sontakke; Joseph J. Lim; Jesse Thomason; Erdem Biyik; Jesse Zhang (CoRR, 2025)

HAND Me the Data: Fast Robot Adaptation via Hand Path Retrieval. - Matthew Hong 002; Anthony Liang; Kevin Kim; Harshitha Rajaprakash; Jesse Thomason; Erdem Biyik; Jesse Zhang (CoRR, 2025)

GABRIL: Gaze-Based Regularization for Mitigating Causal Confusion in Imitation Learning. - Amin Banayeeanzade; Fatemeh Bahrani; Yutai Zhou; Erdem Biyik (CoRR, 2025)