1.2k citations · 4.4k across the 42 of their papers we have counts for
16 papers · 1 filter
PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis
Yan Wu, Esther Wershof, Sebastian M Schmon +7
We introduce a comprehensive framework for modeling single cell transcriptomic responses to perturbations, aimed at standardizing benchmarking in this rapidly evolving field. Our a…
A PAC-Bayesian Analysis of Distance-Based Classifiers: Why Nearest-Neighbour works!
Thore Graepel, Ralf Herbrich
Abstract We present PAC-Bayesian bounds for the generalisation error of the K-nearest-neighbour classifier (K-NN). This is achieved by casting the K-NN classifier into a kernel spa…
Negotiating Team Formation Using Deep Reinforcement Learning
Yoram Bachrach, Richard Everett, Edward Hughes +6
When autonomous agents interact in the same environment, they must often cooperate to achieve their goals. One way for agents to cooperate effectively is to form a team, make a bin…
EigenGame: PCA as a Nash Equilibrium
Ian Gemp, Brian McWilliams, Claire Vernade +1
We present a novel view on principal component analysis (PCA) as a competitive game in which each approximate eigenvector is controlled by a player whose goal is to maximize their…
Learning to Play No-Press Diplomacy with Best Response Policy Iteration
Thomas Anthony, Tom Eccles, Andrea Tacchetti +11
Recent advances in deep reinforcement learning (RL) have led to considerable progress in many 2-player zero-sum games, such as Go, Poker and Starcraft. The purely adversarial natur…
Smooth markets: A basic mechanism for organizing gradient-based learners
David Balduzzi, Wojciech M Czarnecki, Thomas W Anthony +5
With the success of modern machine learning, it is becoming increasingly important to understand and control how learning algorithms interact. Unfortunately, negative results from…