research
∙
10/08/2021
Pick Your Battles: Interaction Graphs as Population-Level Objectives for Strategic Diversity
Strategic diversity is often essential in games: in multi-player games, ...
share
research
∙
10/01/2020
D3C: Reducing the Price of Anarchy in Multi-Agent Learning
Even in simple multi-agent systems, fixed incentives can lead to outcome...
research
∙
04/20/2020
Real World Games Look Like Spinning Tops
This paper investigates the geometrical properties of real world games (...
research
∙
02/27/2020
Learning to Resolve Alliance Dilemmas in Many-Player Zero-Sum Games
Zero-sum games have long guided artificial intelligence research, since ...
research
∙
02/19/2020
From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization
In this paper we investigate the Follow the Regularized Leader dynamics ...
research
∙
02/14/2020
Minimax Theorem for Latent Games or: How I Learned to Stop Worrying about Mixed-Nash and Love Neural Nets
Adversarial training, a special case of multi-objective optimization, is...
research
∙
01/14/2020
Smooth markets: A basic mechanism for organizing gradient-based learners
With the success of modern machine learning, it is becoming increasingly...
research
∙
12/02/2019
LOGAN: Latent Optimisation for Generative Adversarial Networks
Training generative adversarial networks requires balancing of delicate ...
research
∙
05/13/2019
Differentiable Game Mechanics
Deep learning is built on the foundational guarantee that gradient desce...
research
∙
01/23/2019
Open-ended Learning in Symmetric Zero-sum Games
Zero-sum games such as chess and poker are, abstractly, functions that e...
research
∙
11/20/2018
Stable Opponent Shaping in Differentiable Games
A growing number of learning methods are actually games which optimise m...
research
∙
06/07/2018
Re-evaluating evaluation
Progress in machine learning is measured by careful evaluation on proble...
research
∙
02/15/2018
The Mechanics of n-Player Differentiable Games
The cornerstone underpinning deep learning is the guarantee that gradien...
research
∙
02/28/2017
The Shattered Gradients Problem: If resnets are the answer, then what is the question?
A long-standing obstacle to progress in deep learning is the problem of ...
research
∙
02/24/2017
Strongly-Typed Agents are Guaranteed to Interact Safely
As artificial agents proliferate, it is becoming increasingly important ...
research
∙
11/07/2016
Neural Taylor Approximations: Convergence and Exploration in Rectifier Networks
Modern convolutional networks, incorporating rectifiers and max-pooling,...
research
∙
07/12/2016
Deep Reconstruction-Classification Networks for Unsupervised Domain Adaptation
In this paper, we propose a novel unsupervised domain adaptation algorit...
research
∙
04/07/2016
Deep Online Convex Optimization with Gated Games
Methods from convex optimization are widely used as building blocks for ...
research
∙
02/09/2016
Compliance-Aware Bandits
Motivated by clinical trials, we study bandits with observable non-compl...
research
∙
02/06/2016
Strongly-Typed Recurrent Neural Networks
Recurrent neural networks are increasing popular models for sequential l...
research
∙
10/15/2015
Scatter Component Analysis: A Unified Framework for Domain Adaptation and Domain Generalization
This paper addresses classification tasks on a particular target domain ...
research
∙
09/29/2015
Semantics, Representations and Grammars for Deep Learning
Deep learning is currently the subject of intensive study. However, fund...
research
∙
09/10/2015
Compatible Value Gradients for Reinforcement Learning of Continuous Deep Policies
This paper proposes GProp, a deep reinforcement learning algorithm for c...
research
∙
09/06/2015
Deep Online Convex Optimization by Putting Forecaster to Sleep
Methods from convex optimization such as accelerated gradient descent ar...
research
∙
08/31/2015
Domain Generalization for Object Recognition with Multi-task Autoencoders
The problem of domain generalization is to take knowledge acquired from ...
research
∙
01/07/2014
Cortical prediction markets
We investigate cortical learning from the perspective of mechanism desig...
research
∙
06/24/2013
Correlated random features for fast semi-supervised learning
This paper presents Correlated Nystrom Views (XNV), a fast semi-supervis...
research
∙
01/10/2013
Domain Generalization via Invariant Feature Representation
This paper investigates domain generalization: How to take knowledge acq...
research
∙
09/25/2012
Towards a learning-theoretic analysis of spike-timing dependent plasticity
This paper suggests a learning-theoretic perspective on how synaptic pla...
research
∙
06/09/2012
A Nonparametric Conjugate Prior Distribution for the Maximizing Argument of a Noisy Function
We propose a novel Bayesian approach to solve stochastic optimization pr...
research
∙
11/23/2011
Falsification and future performance
We information-theoretically reformulate two measures of capacity from s...
research
∙
10/17/2011
Information, learning and falsification
There are (at least) three approaches to quantifying information. The fi...
research
∙
07/06/2011
