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Books and Theses
- 2022
[b1]Adam Gleave:
Towards Trustworthy Machine Learning. University of California, Berkeley, USA, 2022
Journal Articles
- 2021
- [j1]Antonin Raffin, Ashley Hill, Adam Gleave, Anssi Kanervisto, Maximilian Ernestus, Noah Dormann:
Stable-Baselines3: Reliable Reinforcement Learning Implementations. J. Mach. Learn. Res. 22: 268:1-268:8 (2021)
Conference and Workshop Papers
- 2024
- [c7]Joar Max Viktor Skalse, Lucy Farnik, Sumeet Ramesh Motwani, Erik Jenner, Adam Gleave, Alessandro Abate:
STARC: A General Framework For Quantifying Differences Between Reward Functions. ICLR 2024 - 2023
- [c6]Joar Max Viktor Skalse, Matthew Farrugia-Roberts, Stuart Russell, Alessandro Abate, Adam Gleave:
Invariance in Policy Optimisation and Partial Identifiability in Reward Learning. ICML 2023: 32033-32058 - [c5]Tony Tong Wang, Adam Gleave, Tom Tseng
, Kellin Pelrine, Nora Belrose, Joseph Miller, Michael D. Dennis, Yawen Duan, Viktor Pogrebniak, Sergey Levine, Stuart Russell:
Adversarial Policies Beat Superhuman Go AIs. ICML 2023: 35655-35739 - 2021
- [c4]Adam Gleave, Michael Dennis, Shane Legg, Stuart Russell, Jan Leike:
Quantifying Differences in Reward Functions. ICLR 2021 - 2020
- [c3]Adam Gleave, Michael Dennis, Cody Wild, Neel Kant, Sergey Levine, Stuart Russell:
Adversarial Policies: Attacking Deep Reinforcement Learning. ICLR 2020 - 2017
- [c2]Adam Gleave, Christian Steinruecken:
Making Compression Algorithms for Unicode Text. DCC 2017: 441 - 2016
- [c1]Ionel Gog, Malte Schwarzkopf, Adam Gleave, Robert N. M. Watson, Steven Hand:
Firmament: Fast, Centralized Cluster Scheduling at Scale. OSDI 2016: 99-115
Informal and Other Publications
- 2024
- [i24]Pedro Freire, ChengCheng Tan, Adam Gleave, Dan Hendrycks, Scott Emmons:
Uncovering Latent Human Wellbeing in Language Model Embeddings. CoRR abs/2402.11777 (2024) - [i23]Tom Tseng, Euan McLean, Kellin Pelrine, Tony T. Wang, Adam Gleave:
Can Go AIs be adversarially robust? CoRR abs/2406.12843 (2024) - [i22]Adrià Garriga-Alonso, Mohammad Taufeeque, Adam Gleave:
Planning behavior in a recurrent neural network that plays Sokoban. CoRR abs/2407.15421 (2024) - [i21]Nikolaus H. R. Howe, Michal Zajac, Ian R. McKenzie, Oskar Hollinsworth, Tom Tseng, Pierre-Luc Bacon, Adam Gleave:
Exploring Scaling Trends in LLM Robustness. CoRR abs/2407.18213 (2024) - [i20]Dillon Bowen, Brendan Murphy, Will Cai, David Khachaturov, Adam Gleave, Kellin Pelrine:
Scaling Laws for Data Poisoning in LLMs. CoRR abs/2408.02946 (2024) - 2023
- [i19]Lev McKinney, Yawen Duan, David Krueger, Adam Gleave:
On The Fragility of Learned Reward Functions. CoRR abs/2301.03652 (2023) - [i18]Joar Skalse, Lucy Farnik, Sumeet Ramesh Motwani, Erik Jenner, Adam Gleave, Alessandro Abate:
STARC: A General Framework For Quantifying Differences Between Reward Functions. CoRR abs/2309.15257 (2023) - [i17]Kellin Pelrine, Mohammad Taufeeque, Michal Zajac, Euan McLean, Adam Gleave:
Exploiting Novel GPT-4 APIs. CoRR abs/2312.14302 (2023) - 2022
- [i16]Adam Gleave, Geoffrey Irving:
Uncertainty Estimation for Language Reward Models. CoRR abs/2203.07472 (2022) - [i15]Joar Skalse, Matthew Farrugia-Roberts, Stuart Russell, Alessandro Abate, Adam Gleave:
Invariance in Policy Optimisation and Partial Identifiability in Reward Learning. CoRR abs/2203.07475 (2022) - [i14]Adam Gleave, Sam Toyer:
A Primer on Maximum Causal Entropy Inverse Reinforcement Learning. CoRR abs/2203.11409 (2022) - [i13]Erik Jenner, Adam Gleave:
Preprocessing Reward Functions for Interpretability. CoRR abs/2203.13553 (2022) - [i12]Pavel Czempin, Adam Gleave:
Reducing Exploitability with Population Based Training. CoRR abs/2208.05083 (2022) - [i11]Erik Jenner, Herke van Hoof, Adam Gleave:
Calculus on MDPs: Potential Shaping as a Gradient. CoRR abs/2208.09570 (2022) - [i10]Tony Tong Wang, Adam Gleave, Nora Belrose, Tom Tseng, Joseph Miller, Michael D. Dennis, Yawen Duan, Viktor Pogrebniak, Sergey Levine, Stuart Russell:
Adversarial Policies Beat Professional-Level Go AIs. CoRR abs/2211.00241 (2022) - [i9]Adam Gleave, Mohammad Taufeeque, Juan Rocamonde, Erik Jenner, Steven H. Wang, Sam Toyer, Maximilian Ernestus, Nora Belrose, Scott Emmons, Stuart Russell:
imitation: Clean Imitation Learning Implementations. CoRR abs/2211.11972 (2022) - 2020
- [i8]Adam Gleave, Michael Dennis, Shane Legg, Stuart Russell, Jan Leike:
Quantifying Differences in Reward Functions. CoRR abs/2006.13900 (2020) - [i7]Pedro Freire, Adam Gleave, Sam Toyer, Stuart Russell:
DERAIL: Diagnostic Environments for Reward And Imitation Learning. CoRR abs/2012.01365 (2020) - [i6]Eric J. Michaud, Adam Gleave, Stuart Russell:
Understanding Learned Reward Functions. CoRR abs/2012.05862 (2020) - 2019
- [i5]Adam Gleave, Michael Dennis, Neel Kant, Cody Wild, Sergey Levine, Stuart Russell:
Adversarial Policies: Attacking Deep Reinforcement Learning. CoRR abs/1905.10615 (2019) - 2018
- [i4]Adam Gleave, Oliver Habryka:
Multi-task Maximum Entropy Inverse Reinforcement Learning. CoRR abs/1805.08882 (2018) - [i3]Sören Mindermann, Rohin Shah, Adam Gleave, Dylan Hadfield-Menell:
Active Inverse Reward Design. CoRR abs/1809.03060 (2018) - [i2]Aaron Tucker, Adam Gleave, Stuart Russell:
Inverse reinforcement learning for video games. CoRR abs/1810.10593 (2018) - 2017
- [i1]Adam Gleave, Christian Steinruecken:
Making compression algorithms for Unicode text. CoRR abs/1701.04047 (2017)
Coauthor Index
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