János Kramár

Publication Activity (10 Years)

Years Active: 2010-2024
Publications (10 Years): 28

Top Topics

Continuous Space

Binary Matrices

General Intelligence

Imitation Learning

Top Venues

Publications

Senthooran Rajamanoharan, Tom Lieberum, Nicolas Sonnerat, Arthur Conmy, Vikrant Varma, János Kramár, Neel Nanda
Jumping Ahead: Improving Reconstruction Fidelity with JumpReLU Sparse Autoencoders. CoRR (2024)
Senthooran Rajamanoharan, Arthur Conmy, Lewis Smith, Tom Lieberum, Vikrant Varma, János Kramár, Rohin Shah, Neel Nanda
Improving Dictionary Learning with Gated Sparse Autoencoders. CoRR (2024)
Zachary Kenton, Noah Y. Siegel, János Kramár, Jonah Brown-Cohen, Samuel Albanie, Jannis Bulian, Rishabh Agarwal, David Lindner, Yunhao Tang, Noah D. Goodman, Rohin Shah
On scalable oversight with weak LLMs judging strong LLMs. CoRR (2024)
János Kramár, Tom Lieberum, Rohin Shah, Neel Nanda
AtP*: An efficient and scalable method for localizing LLM behaviour to components. CoRR (2024)
Thomas McGrath, Matthew Rahtz, János Kramár, Vladimir Mikulik, Shane Legg
The Hydra Effect: Emergent Self-repair in Language Model Computations. CoRR (2023)
Victoria Krakovna, János Kramár
Power-seeking can be probable and predictive for trained agents. CoRR (2023)
David Lindner, János Kramár, Sebastian Farquhar, Matthew Rahtz, Tom McGrath, Vladimir Mikulik
Tracr: Compiled Transformers as a Laboratory for Interpretability. NeurIPS (2023)
David Lindner, János Kramár, Matthew Rahtz, Thomas McGrath, Vladimir Mikulik
Tracr: Compiled Transformers as a Laboratory for Interpretability. CoRR (2023)
Tom Lieberum, Matthew Rahtz, János Kramár, Neel Nanda, Geoffrey Irving, Rohin Shah, Vladimir Mikulik
Does Circuit Analysis Interpretability Scale? Evidence from Multiple Choice Capabilities in Chinchilla. CoRR (2023)
Vikrant Varma, Rohin Shah, Zachary Kenton, János Kramár, Ramana Kumar
Explaining grokking through circuit efficiency. CoRR (2023)
Ian M. Gemp, Rahul Savani, Marc Lanctot, Yoram Bachrach, Thomas W. Anthony, Richard Everett, Andrea Tacchetti, Tom Eccles, János Kramár
Sample-based Approximation of Nash in Large Many-Player Games via Gradient Descent. AAMAS (2022)
Yoram Bachrach, Ian M. Gemp, Marta Garnelo, János Kramár, Tom Eccles, Dan Rosenbaum, Thore Graepel
A Neural Network Auction For Group Decision Making Over a Continuous Space. IJCAI (2021)
Ian M. Gemp, Rahul Savani, Marc Lanctot, Yoram Bachrach, Thomas W. Anthony, Richard Everett, Andrea Tacchetti, Tom Eccles, János Kramár
Sample-based Approximation of Nash in Large Many-Player Games via Gradient Descent. CoRR (2021)
Thomas W. Anthony, Tom Eccles, Andrea Tacchetti, János Kramár, Ian M. Gemp, Thomas C. Hudson, Nicolas Porcel, Marc Lanctot, Julien Pérolat, Richard Everett, Satinder Singh, Thore Graepel, Yoram Bachrach
Learning to Play No-Press Diplomacy with Best Response Policy Iteration. NeurIPS (2020)
János Kramár, Neil C. Rabinowitz, Tom Eccles, Andrea Tacchetti
Should I Tear down This Wall? Optimizing Social Metrics by Evaluating Novel Actions. COIN(E)@AAMAS (2020)
János Kramár, Neil C. Rabinowitz, Tom Eccles, Andrea Tacchetti
Should I tear down this wall? Optimizing social metrics by evaluating novel actions. CoRR (2020)
Thomas W. Anthony, Tom Eccles, Andrea Tacchetti, János Kramár, Ian M. Gemp, Thomas C. Hudson, Nicolas Porcel, Marc Lanctot, Julien Pérolat, Richard Everett, Satinder Singh, Thore Graepel, Yoram Bachrach
Learning to Play No-Press Diplomacy with Best Response Policy Iteration. CoRR (2020)
Tom Eccles, Edward Hughes, János Kramár, Steven Wheelwright, Joel Z. Leibo
Learning Reciprocity in Complex Sequential Social Dilemmas. CoRR (2019)
Tom Eccles, Edward Hughes, János Kramár, Steven Wheelwright, Joel Z. Leibo
The Imitation Game: Learned Reciprocity in Markov games. AAMAS (2019)
Marc Lanctot, Edward Lockhart, Jean-Baptiste Lespiau, Vinícius Flores Zambaldi, Satyaki Upadhyay, Julien Pérolat, Sriram Srinivasan, Finbarr Timbers, Karl Tuyls, Shayegan Omidshafiei, Daniel Hennes, Dustin Morrill, Paul Muller, Timo Ewalds, Ryan Faulkner, János Kramár, Bart De Vylder, Brennan Saeta, James Bradbury, David Ding, Sebastian Borgeaud, Matthew Lai, Julian Schrittwieser, Thomas W. Anthony, Edward Hughes, Ivo Danihelka, Jonah Ryan-Davis
OpenSpiel: A Framework for Reinforcement Learning in Games. CoRR (2019)
Andrea Tacchetti, H. Francis Song, Pedro A. M. Mediano, Vinícius Flores Zambaldi, János Kramár, Neil C. Rabinowitz, Thore Graepel, Matthew Botvinick, Peter W. Battaglia
Relational Forward Models for Multi-Agent Learning. ICLR (Poster) (2019)
Yuke Zhu, Ziyu Wang, Josh Merel, Andrei A. Rusu, Tom Erez, Serkan Cabi, Saran Tunyasuvunakool, János Kramár, Raia Hadsell, Nando de Freitas, Nicolas Heess
Reinforcement and Imitation Learning for Diverse Visuomotor Skills. Robotics: Science and Systems (2018)
Yuke Zhu, Ziyu Wang, Josh Merel, Andrei A. Rusu, Tom Erez, Serkan Cabi, Saran Tunyasuvunakool, János Kramár, Raia Hadsell, Nando de Freitas, Nicolas Heess
Reinforcement and Imitation Learning for Diverse Visuomotor Skills. CoRR (2018)
James Babcock, János Kramár, Roman V. Yampolskiy
Guidelines for Artificial Intelligence Containment. CoRR (2017)
David Krueger, Tegan Maharaj, János Kramár, Mohammad Pezeshki, Nicolas Ballas, Nan Rosemary Ke, Anirudh Goyal, Yoshua Bengio, Aaron C. Courville, Christopher J. Pal
Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations. ICLR (Poster) (2017)
James Babcock, János Kramár, Roman Yampolskiy
The AGI Containment Problem. AGI (2016)
James Babcock, János Kramár, Roman Yampolskiy
The AGI Containment Problem. CoRR (2016)
David Krueger, Tegan Maharaj, János Kramár, Mohammad Pezeshki, Nicolas Ballas, Nan Rosemary Ke, Anirudh Goyal, Yoshua Bengio, Hugo Larochelle, Aaron C. Courville, Chris Pal
Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations. CoRR (2016)
Matthew Skala, Victoria Krakovna, János Kramár, Gerald Penn
A Generalized-Zero-Preserving Method for Compact Encoding of Concept Lattices. ACL (2010)