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Cassidy Laidlaw
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2020 – today
- 2024
- [c9]Lauren H. Cooke
, Harvey Klyne, Edwin Zhang, Cassidy Laidlaw, Milind Tambe, Finale Doshi-Velez:
Toward Computationally Efficient Inverse Reinforcement Learning via Reward Shaping. Tiny Papers @ ICLR 2024 - [c8]Cassidy Laidlaw, Banghua Zhu, Stuart Russell, Anca D. Dragan:
The Effective Horizon Explains Deep RL Performance in Stochastic Environments. ICLR 2024 - [c7]Anand Siththaranjan, Cassidy Laidlaw, Dylan Hadfield-Menell:
Distributional Preference Learning: Understanding and Accounting for Hidden Context in RLHF. ICLR 2024 - [i11]Cassidy Laidlaw, Shivam Singhal, Anca D. Dragan:
Preventing Reward Hacking with Occupancy Measure Regularization. CoRR abs/2403.03185 (2024) - 2023
- [c6]Cassidy Laidlaw, Stuart J. Russell, Anca D. Dragan:
Bridging RL Theory and Practice with the Effective Horizon. NeurIPS 2023 - [i10]Cassidy Laidlaw, Stuart Russell, Anca D. Dragan:
Bridging RL Theory and Practice with the Effective Horizon. CoRR abs/2304.09853 (2023) - [i9]Anand Siththaranjan, Cassidy Laidlaw, Dylan Hadfield-Menell:
Distributional Preference Learning: Understanding and Accounting for Hidden Context in RLHF. CoRR abs/2312.08358 (2023) - [i8]Cassidy Laidlaw, Banghua Zhu, Stuart Russell, Anca D. Dragan:
The Effective Horizon Explains Deep RL Performance in Stochastic Environments. CoRR abs/2312.08369 (2023) - [i7]Lauren H. Cooke, Harvey Klyne, Edwin Zhang, Cassidy Laidlaw, Milind Tambe, Finale Doshi-Velez:
Toward Computationally Efficient Inverse Reinforcement Learning via Reward Shaping. CoRR abs/2312.09983 (2023) - 2022
- [c5]Cassidy Laidlaw, Anca D. Dragan:
The Boltzmann Policy Distribution: Accounting for Systematic Suboptimality in Human Models. ICLR 2022 - [i6]Cassidy Laidlaw, Anca D. Dragan:
The Boltzmann Policy Distribution: Accounting for Systematic Suboptimality in Human Models. CoRR abs/2204.10759 (2022) - 2021
- [c4]Cassidy Laidlaw, Sahil Singla, Soheil Feizi:
Perceptual Adversarial Robustness: Defense Against Unseen Threat Models. ICLR 2021 - [c3]Cassidy Laidlaw, Stuart Russell:
Uncertain Decisions Facilitate Better Preference Learning. NeurIPS 2021: 15070-15083 - [i5]Cassidy Laidlaw, Stuart Russell:
Learning the Preferences of Uncertain Humans with Inverse Decision Theory. CoRR abs/2106.10394 (2021) - 2020
- [i4]Cassidy Laidlaw, Sahil Singla, Soheil Feizi:
Perceptual Adversarial Robustness: Defense Against Unseen Threat Models. CoRR abs/2006.12655 (2020)
2010 – 2019
- 2019
- [c2]Daniel Cudeiro, Timo Bolkart, Cassidy Laidlaw, Anurag Ranjan, Michael J. Black:
Capture, Learning, and Synthesis of 3D Speaking Styles. CVPR 2019: 10101-10111 - [c1]Cassidy Laidlaw, Soheil Feizi:
Functional Adversarial Attacks. NeurIPS 2019: 10408-10418 - [i3]Daniel Cudeiro, Timo Bolkart, Cassidy Laidlaw, Anurag Ranjan, Michael J. Black:
Capture, Learning, and Synthesis of 3D Speaking Styles. CoRR abs/1905.03079 (2019) - [i2]Cassidy Laidlaw, Soheil Feizi:
Functional Adversarial Attacks. CoRR abs/1906.00001 (2019) - [i1]Cassidy Laidlaw, Soheil Feizi:
Playing it Safe: Adversarial Robustness with an Abstain Option. CoRR abs/1911.11253 (2019)
Coauthor Index
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last updated on 2024-10-07 22:22 CEST by the dblp team
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