Eric J. Michaud

Publication Activity (10 Years)

Years Active: 2020-2024
Publications (10 Years): 17

Top Topics

Information Theory

Conditional Entropy

Reinforcement Learning

Top Venues

Publications

Eric J. Michaud, Isaac Liao, Vedang Lad, Ziming Liu, Anish Mudide, Chloe Loughridge, Zifan Carl Guo, Tara Rezaei Kheirkhah, Mateja Vukelic, Max Tegmark
Opening the AI black box: program synthesis via mechanistic interpretability. CoRR (2024)
Xiaoman Delores Ding, Zifan Carl Guo, Eric J. Michaud, Ziming Liu, Max Tegmark
Survival of the Fittest Representation: A Case Study with Modular Addition. CoRR (2024)
Joshua Engels, Isaac Liao, Eric J. Michaud, Wes Gurnee, Max Tegmark
Not All Language Model Features Are Linear. CoRR (2024)
Samuel Marks, Can Rager, Eric J. Michaud, Yonatan Belinkov, David Bau, Aaron Mueller
Sparse Feature Circuits: Discovering and Editing Interpretable Causal Graphs in Language Models. CoRR (2024)
Eric J. Michaud, Ziming Liu, Uzay Girit, Max Tegmark
The Quantization Model of Neural Scaling. NeurIPS (2023)
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, Charbel-Raphaël Ségerie, Micah Carroll, Andi Peng, Phillip J. K. Christoffersen, Mehul Damani, Stewart Slocum, Usman Anwar, Anand Siththaranjan, Max Nadeau, Eric J. Michaud, Jacob Pfau, Dmitrii Krasheninnikov, Xin Chen, Lauro Langosco, Peter Hase, Erdem Biyik, Anca D. Dragan, David Krueger, Dorsa Sadigh, Dylan Hadfield-Menell
Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback. Trans. Mach. Learn. Res. 2023 (2023)
Stephen Casper, Xander Davies, Claudia Shi, Thomas Krendl Gilbert, Jérémy Scheurer, Javier Rando, Rachel Freedman, Tomasz Korbak, David Lindner, Pedro Freire, Tony Wang, Samuel Marks, Charbel-Raphaël Ségerie, Micah Carroll, Andi Peng, Phillip J. K. Christoffersen, Mehul Damani, Stewart Slocum, Usman Anwar, Anand Siththaranjan, Max Nadeau, Eric J. Michaud, Jacob Pfau, Dmitrii Krasheninnikov, Xin Chen, Lauro Langosco, Peter Hase, Erdem Biyik, Anca D. Dragan, David Krueger, Dorsa Sadigh, Dylan Hadfield-Menell
Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback. CoRR (2023)
Eric J. Michaud, Ziming Liu, Max Tegmark
Precision Machine Learning. Entropy 25 (1) (2023)
Ziming Liu, Eric J. Michaud, Max Tegmark
Omnigrok: Grokking Beyond Algorithmic Data. ICLR (2023)
Eric J. Michaud, Ziming Liu, Uzay Girit, Max Tegmark
The Quantization Model of Neural Scaling. CoRR (2023)
Eric J. Michaud, Ziming Liu, Max Tegmark
Precision Machine Learning. CoRR (2022)
Ziming Liu, Ouail Kitouni, Niklas Nolte, Eric J. Michaud, Max Tegmark, Mike Williams
Towards Understanding Grokking: An Effective Theory of Representation Learning. NeurIPS (2022)
Ziming Liu, Eric J. Michaud, Max Tegmark
Omnigrok: Grokking Beyond Algorithmic Data. CoRR (2022)
Ziming Liu, Ouail Kitouni, Niklas Nolte, Eric J. Michaud, Max Tegmark, Mike Williams
Towards Understanding Grokking: An Effective Theory of Representation Learning. CoRR (2022)
Scythia Marrow, Eric J. Michaud, Erik Hoel
Examining the Causal Structures of Deep Neural Networks Using Information Theory. Entropy 22 (12) (2020)
Simon Mattsson, Eric J. Michaud, Erik Hoel
Examining the causal structures of deep neural networks using information theory. CoRR (2020)
Eric J. Michaud, Adam Gleave, Stuart Russell
Understanding Learned Reward Functions. CoRR (2020)