
-
D3C: Reducing the Price of Anarchy in Multi-Agent Learning
Even in simple multi-agent systems, fixed incentives can lead to outcome...
read it
-
Real World Games Look Like Spinning Tops
This paper investigates the geometrical properties of real world games (...
read it
-
Learning to Resolve Alliance Dilemmas in Many-Player Zero-Sum Games
Zero-sum games have long guided artificial intelligence research, since ...
read it
-
From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization
In this paper we investigate the Follow the Regularized Leader dynamics ...
read it
-
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...
read it
-
Smooth markets: A basic mechanism for organizing gradient-based learners
With the success of modern machine learning, it is becoming increasingly...
read it
-
LOGAN: Latent Optimisation for Generative Adversarial Networks
Training generative adversarial networks requires balancing of delicate ...
read it
-
Differentiable Game Mechanics
Deep learning is built on the foundational guarantee that gradient desce...
read it
-
Open-ended Learning in Symmetric Zero-sum Games
Zero-sum games such as chess and poker are, abstractly, functions that e...
read it
-
Stable Opponent Shaping in Differentiable Games
A growing number of learning methods are actually games which optimise m...
read it
-
Re-evaluating evaluation
Progress in machine learning is measured by careful evaluation on proble...
read it
-
The Mechanics of n-Player Differentiable Games
The cornerstone underpinning deep learning is the guarantee that gradien...
read it
-
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 ...
read it
-
Strongly-Typed Agents are Guaranteed to Interact Safely
As artificial agents proliferate, it is becoming increasingly important ...
read it
-
Neural Taylor Approximations: Convergence and Exploration in Rectifier Networks
Modern convolutional networks, incorporating rectifiers and max-pooling,...
read it
-
Deep Reconstruction-Classification Networks for Unsupervised Domain Adaptation
In this paper, we propose a novel unsupervised domain adaptation algorit...
read it
-
Deep Online Convex Optimization with Gated Games
Methods from convex optimization are widely used as building blocks for ...
read it
-
Compliance-Aware Bandits
Motivated by clinical trials, we study bandits with observable non-compl...
read it
-
Strongly-Typed Recurrent Neural Networks
Recurrent neural networks are increasing popular models for sequential l...
read it
-
Scatter Component Analysis: A Unified Framework for Domain Adaptation and Domain Generalization
This paper addresses classification tasks on a particular target domain ...
read it
-
Semantics, Representations and Grammars for Deep Learning
Deep learning is currently the subject of intensive study. However, fund...
read it
-
Compatible Value Gradients for Reinforcement Learning of Continuous Deep Policies
This paper proposes GProp, a deep reinforcement learning algorithm for c...
read it
-
Deep Online Convex Optimization by Putting Forecaster to Sleep
Methods from convex optimization such as accelerated gradient descent ar...
read it
-
Domain Generalization for Object Recognition with Multi-task Autoencoders
The problem of domain generalization is to take knowledge acquired from ...
read it
-
Cortical prediction markets
We investigate cortical learning from the perspective of mechanism desig...
read it
-
Correlated random features for fast semi-supervised learning
This paper presents Correlated Nystrom Views (XNV), a fast semi-supervis...
read it
-
Domain Generalization via Invariant Feature Representation
This paper investigates domain generalization: How to take knowledge acq...
read it
-
Towards a learning-theoretic analysis of spike-timing dependent plasticity
This paper suggests a learning-theoretic perspective on how synaptic pla...
read it
-
A Nonparametric Conjugate Prior Distribution for the Maximizing Argument of a Noisy Function
We propose a novel Bayesian approach to solve stochastic optimization pr...
read it
-
Falsification and future performance
We information-theoretically reformulate two measures of capacity from s...
read it
-
Information, learning and falsification
There are (at least) three approaches to quantifying information. The fi...
read it
-
On the information-theoretic structure of distributed measurements
The internal structure of a measuring device, which depends on what its ...
read it