271 citations · 1.3k across the 23 of their papers we have counts for
42 papers
Why Large Language Models Fail at Tabular Prediction
Marta Garnelo, Wojciech M. Czarnecki
Large language models (LLMs) have become the default tool for a remarkable range of tasks, yet they have had conspicuously little success at one of the most common machine learning…
AlphaStar Unplugged: Large-Scale Offline Reinforcement Learning
Michaël Mathieu, Sherjil Ozair, Srivatsan Srinivasan +21
StarCraft II is one of the most challenging simulated reinforcement learning environments; it is partially observable, stochastic, multi-agent, and mastering StarCraft II requires…
Exploring the Space of Key-Value-Query Models with Intention
Marta Garnelo, Wojciech Marian Czarnecki
Attention-based models have been a key element of many recent breakthroughs in deep learning. Two key components of Attention are the structure of its input (which consists of keys…
On the Limitations of Elo: Real-World Games, are Transitive, not Additive
Quentin Bertrand, Wojciech Marian Czarnecki, Gauthier Gidel
Real-world competitive games, such as chess, go, or StarCraft II, rely on Elo models to measure the strength of their players. Since these games are not fully transitive, using Elo…
Pick Your Battles: Interaction Graphs as Population-Level Objectives for Strategic Diversity
Marta Garnelo, Wojciech Marian Czarnecki, Siqi Liu +5
Strategic diversity is often essential in games: in multi-player games, for example, evaluating a player against a diverse set of strategies will yield a more accurate estimate of…
Open-Ended Learning Leads to Generally Capable Agents
Open Ended Learning Team, Adam Stooke, Anuj Mahajan +15
In this work we create agents that can perform well beyond a single, individual task, that exhibit much wider generalisation of behaviour to a massive, rich space of challenges. We…