Adam Gleave

According to our database, Adam Gleave authored at least 23 papers between 2016 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Uncovering Latent Human Wellbeing in Language Model Embeddings.

Pedro Freire, ChengCheng Tan, Adam Gleave, Dan Hendrycks, Scott Emmons

CoRR, 2024

2023

Exploiting Novel GPT-4 APIs.

Kellin Pelrine, Mohammad Taufeeque, Michal Zajac, Euan McLean, Adam Gleave

CoRR, 2023

STARC: A General Framework For Quantifying Differences Between Reward Functions.

Joar Skalse, Lucy Farnik, Sumeet Ramesh Motwani, Erik Jenner, Adam Gleave, Alessandro Abate

CoRR, 2023

On The Fragility of Learned Reward Functions.

Lev McKinney, Yawen Duan, David Krueger, Adam Gleave

CoRR, 2023

Adversarial Policies Beat Superhuman Go AIs.

Tony Tong Wang, Adam Gleave, Tom Tseng, Kellin Pelrine, Nora Belrose, Joseph Miller, Michael D. Dennis, Yawen Duan, Viktor Pogrebniak, Sergey Levine, Stuart Russell

Proceedings of the International Conference on Machine Learning, 2023

Invariance in Policy Optimisation and Partial Identifiability in Reward Learning.

Joar Max Viktor Skalse, Matthew Farrugia-Roberts, Stuart Russell, Alessandro Abate, Adam Gleave

Proceedings of the International Conference on Machine Learning, 2023

2022

imitation: Clean Imitation Learning Implementations.

Adam Gleave, Mohammad Taufeeque, Juan Rocamonde, Erik Jenner, Steven H. Wang, Sam Toyer, Maximilian Ernestus, Nora Belrose, Scott Emmons, Stuart Russell

CoRR, 2022

Adversarial Policies Beat Professional-Level Go AIs.

Tony Tong Wang, Adam Gleave, Nora Belrose, Tom Tseng, Joseph Miller, Michael D. Dennis, Yawen Duan, Viktor Pogrebniak, Sergey Levine, Stuart Russell

CoRR, 2022

Calculus on MDPs: Potential Shaping as a Gradient.

Erik Jenner, Herke van Hoof, Adam Gleave

CoRR, 2022

Reducing Exploitability with Population Based Training.

Pavel Czempin, Adam Gleave

CoRR, 2022

Preprocessing Reward Functions for Interpretability.

Erik Jenner, Adam Gleave

CoRR, 2022

A Primer on Maximum Causal Entropy Inverse Reinforcement Learning.

Adam Gleave, Sam Toyer

CoRR, 2022

Uncertainty Estimation for Language Reward Models.

Adam Gleave, Geoffrey Irving

CoRR, 2022

2021

Stable-Baselines3: Reliable Reinforcement Learning Implementations.

Antonin Raffin, Ashley Hill, Adam Gleave, Anssi Kanervisto, Maximilian Ernestus, Noah Dormann

J. Mach. Learn. Res., 2021

Quantifying Differences in Reward Functions.

Adam Gleave, Michael Dennis, Shane Legg, Stuart Russell, Jan Leike

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

Understanding Learned Reward Functions.

Eric J. Michaud, Adam Gleave, Stuart Russell

CoRR, 2020

DERAIL: Diagnostic Environments for Reward And Imitation Learning.

Pedro Freire, Adam Gleave, Sam Toyer, Stuart Russell

CoRR, 2020

Adversarial Policies: Attacking Deep Reinforcement Learning.

Adam Gleave, Michael Dennis, Cody Wild, Neel Kant, Sergey Levine, Stuart Russell

Proceedings of the 8th International Conference on Learning Representations, 2020

2018

Inverse reinforcement learning for video games.

Aaron Tucker, Adam Gleave, Stuart Russell

CoRR, 2018

Active Inverse Reward Design.

Sören Mindermann, Rohin Shah, Adam Gleave, Dylan Hadfield-Menell

CoRR, 2018

Multi-task Maximum Entropy Inverse Reinforcement Learning.

Adam Gleave, Oliver Habryka

CoRR, 2018

2017

Making Compression Algorithms for Unicode Text.

Adam Gleave, Christian Steinruecken

Proceedings of the 2017 Data Compression Conference, 2017

2016

Firmament: Fast, Centralized Cluster Scheduling at Scale.

Ionel Gog, Malte Schwarzkopf, Adam Gleave, Robert N. M. Watson, Steven Hand

Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation, 2016