Cassidy Laidlaw

25 papers A* 11Journal 13Unranked 1

Year	Rank	Type	Title / Venue / Authors
2025	A*	`conf`	AssistanceZero: Scalably Solving Assistance Games. ICML Cassidy Laidlaw, Eli Bronstein, Timothy Guo, Dylan Feng, Lukas Berglund, Justin Svegliato, Stuart Russell, Anca D. Dragan
2025	J	`jnl`	AssistanceZero: Scalably Solving Assistance Games. CoRR Cassidy Laidlaw, Eli Bronstein, Timothy Guo, Dylan Feng, Lukas Berglund, Justin Svegliato, Stuart Russell, Anca D. Dragan
2025	A*	`conf`	Correlated Proxies: A New Definition and Improved Mitigation for Reward Hacking. ICLR Cassidy Laidlaw, Shivam Singhal, Anca D. Dragan
2025	A*	`conf`	Iterative Label Refinement Matters More than Preference Optimization under Weak Supervision. ICLR Yaowen Ye, Cassidy Laidlaw, Jacob Steinhardt
2025	J	`jnl`	Iterative Label Refinement Matters More than Preference Optimization under Weak Supervision. CoRR Yaowen Ye, Cassidy Laidlaw, Jacob Steinhardt
2024	A*	`conf`	Distributional Preference Learning: Understanding and Accounting for Hidden Context in RLHF. ICLR Anand Siththaranjan, Cassidy Laidlaw, Dylan Hadfield-Menell
2024	J	`jnl`	Preventing Reward Hacking with Occupancy Measure Regularization. CoRR Cassidy Laidlaw, Shivam Singhal, Anca D. Dragan
2024	A*	`conf`	The Effective Horizon Explains Deep RL Performance in Stochastic Environments. ICLR Cassidy Laidlaw, Banghua Zhu, Stuart Russell, Anca D. Dragan
2024	—	`conf`	Toward Computationally Efficient Inverse Reinforcement Learning via Reward Shaping. Tiny Papers @ ICLR Lauren H. Cooke, Harvey Klyne, Edwin Zhang, Cassidy Laidlaw, Milind Tambe, Finale Doshi-Velez
2023	A*	`conf`	Bridging RL Theory and Practice with the Effective Horizon. NeurIPS Cassidy Laidlaw, Stuart J. Russell, Anca D. Dragan
2023	J	`jnl`	Bridging RL Theory and Practice with the Effective Horizon. CoRR Cassidy Laidlaw, Stuart Russell, Anca D. Dragan
2023	J	`jnl`	Distributional Preference Learning: Understanding and Accounting for Hidden Context in RLHF. CoRR Anand Siththaranjan, Cassidy Laidlaw, Dylan Hadfield-Menell
2023	J	`jnl`	The Effective Horizon Explains Deep RL Performance in Stochastic Environments. CoRR Cassidy Laidlaw, Banghua Zhu, Stuart Russell, Anca D. Dragan
2023	J	`jnl`	Toward Computationally Efficient Inverse Reinforcement Learning via Reward Shaping. CoRR Lauren H. Cooke, Harvey Klyne, Edwin Zhang, Cassidy Laidlaw, Milind Tambe, Finale Doshi-Velez
2022	A*	`conf`	The Boltzmann Policy Distribution: Accounting for Systematic Suboptimality in Human Models. ICLR Cassidy Laidlaw, Anca D. Dragan
2022	J	`jnl`	The Boltzmann Policy Distribution: Accounting for Systematic Suboptimality in Human Models. CoRR Cassidy Laidlaw, Anca D. Dragan
2021	J	`jnl`	Learning the Preferences of Uncertain Humans with Inverse Decision Theory. CoRR Cassidy Laidlaw, Stuart Russell
2021	A*	`conf`	Perceptual Adversarial Robustness: Defense Against Unseen Threat Models. ICLR Cassidy Laidlaw, Sahil Singla, Soheil Feizi
2021	A*	`conf`	Uncertain Decisions Facilitate Better Preference Learning. NeurIPS Cassidy Laidlaw, Stuart Russell
2020	J	`jnl`	Perceptual Adversarial Robustness: Defense Against Unseen Threat Models. CoRR Cassidy Laidlaw, Sahil Singla, Soheil Feizi
2019	A*	`conf`	Capture, Learning, and Synthesis of 3D Speaking Styles. CVPR Daniel Cudeiro, Timo Bolkart, Cassidy Laidlaw, Anurag Ranjan, Michael J. Black
2019	J	`jnl`	Capture, Learning, and Synthesis of 3D Speaking Styles. CoRR Daniel Cudeiro, Timo Bolkart, Cassidy Laidlaw, Anurag Ranjan, Michael J. Black
2019	A*	`conf`	Functional Adversarial Attacks. NeurIPS Cassidy Laidlaw, Soheil Feizi
2019	J	`jnl`	Functional Adversarial Attacks. CoRR Cassidy Laidlaw, Soheil Feizi
2019	J	`jnl`	Playing it Safe: Adversarial Robustness with an Abstain Option. CoRR Cassidy Laidlaw, Soheil Feizi