209 citations · 718 across the 24 of their papers we have counts for
24 papers
AI co-mathematician: Accelerating mathematicians with agentic AI
Daniel Zheng, Ingrid von Glehn, Yori Zwols +15
We introduce the AI co-mathematician, a workbench for mathematicians to interactively leverage AI agents to pursue open-ended research. The AI co-mathematician is optimized to prov…
Amplifying human performance in combinatorial competitive programming
Petar Veličković, Alex Vitvitskyi, Larisa Markeeva +4
Recent years have seen a significant surge in complex AI systems for competitive programming, capable of performing at admirable levels against human competitors. While steady prog…
SemPPL: Predicting pseudo-labels for better contrastive representations
Matko Bošnjak, Pierre H. Richemond, Nenad Tomasev +7
Learning from large amounts of unsupervised data and a small amount of supervision is an important open problem in computer vision. We propose a new semi-supervised learning method…
Pushing the limits of self-supervised ResNets: Can we outperform supervised learning without labels on ImageNet?
Nenad Tomasev, Ioana Bica, Brian McWilliams +4
Despite recent progress made by self-supervised methods in representation learning with residual networks, they still underperform supervised learning on the ImageNet classificatio…
Towards combinatorial invariance for Kazhdan-Lusztig polynomials
Charles Blundell, Lars Buesing, Alex Davies +2
Kazhdan-Lusztig polynomials are important and mysterious objects in representation theory. Here we present a new formula for their computation for symmetric groups based on the Bru…
Counterfactual Credit Assignment in Model-Free Reinforcement Learning
Thomas Mesnard, Théophane Weber, Fabio Viola +11
Credit assignment in reinforcement learning is the problem of measuring an action's influence on future rewards. In particular, this requires separating skill from luck, i.e. disen…