David Balduzzi

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, ...

0 Marta Garnelo, et al. ∙

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...

0 Ian Gemp, et al. ∙

research

∙ 04/20/2020

Real World Games Look Like Spinning Tops

This paper investigates the geometrical properties of real world games (...

10 Wojciech Marian Czarnecki, et al. ∙

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 ...

0 Edward Hughes, et al. ∙

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 ...

32 Julien Perolat, et al. ∙

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...

0 Gauthier Gidel, et al. ∙

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...

10 David Balduzzi, et al. ∙

research

∙ 12/02/2019

LOGAN: Latent Optimisation for Generative Adversarial Networks

Training generative adversarial networks requires balancing of delicate ...

6 Yan Wu, et al. ∙

research

∙ 05/13/2019

Differentiable Game Mechanics

Deep learning is built on the foundational guarantee that gradient desce...

0 Alistair Letcher, et al. ∙

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...

16 David Balduzzi, et al. ∙

research

∙ 11/20/2018

Stable Opponent Shaping in Differentiable Games

A growing number of learning methods are actually games which optimise m...

74 Alistair Letcher, et al. ∙

research

∙ 06/07/2018

Re-evaluating evaluation

Progress in machine learning is measured by careful evaluation on proble...

0 David Balduzzi, et al. ∙

research

∙ 02/15/2018

The Mechanics of n-Player Differentiable Games

The cornerstone underpinning deep learning is the guarantee that gradien...

0 David Balduzzi, et al. ∙

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 ...

0 David Balduzzi, et al. ∙

research

∙ 02/24/2017

Strongly-Typed Agents are Guaranteed to Interact Safely

As artificial agents proliferate, it is becoming increasingly important ...

0 David Balduzzi, et al. ∙

research

∙ 11/07/2016

Neural Taylor Approximations: Convergence and Exploration in Rectifier Networks

Modern convolutional networks, incorporating rectifiers and max-pooling,...

0 David Balduzzi, et al. ∙

research

∙ 07/12/2016

Deep Reconstruction-Classification Networks for Unsupervised Domain Adaptation

In this paper, we propose a novel unsupervised domain adaptation algorit...

0 Muhammad Ghifary, et al. ∙

research

∙ 04/07/2016

Deep Online Convex Optimization with Gated Games

Methods from convex optimization are widely used as building blocks for ...

0 David Balduzzi, et al. ∙

research

∙ 02/09/2016

Compliance-Aware Bandits

Motivated by clinical trials, we study bandits with observable non-compl...

0 Nicolás Della Penna, et al. ∙

research

∙ 02/06/2016

Strongly-Typed Recurrent Neural Networks

Recurrent neural networks are increasing popular models for sequential l...

0 David Balduzzi, et al. ∙

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 ...

0 Muhammad Ghifary, et al. ∙

research

∙ 09/29/2015

Semantics, Representations and Grammars for Deep Learning

Deep learning is currently the subject of intensive study. However, fund...

0 David Balduzzi, et al. ∙

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...

0 David Balduzzi, et al. ∙

research

∙ 09/06/2015

Deep Online Convex Optimization by Putting Forecaster to Sleep

Methods from convex optimization such as accelerated gradient descent ar...

0 David Balduzzi, et al. ∙

research

∙ 08/31/2015

Domain Generalization for Object Recognition with Multi-task Autoencoders

The problem of domain generalization is to take knowledge acquired from ...

0 Muhammad Ghifary, et al. ∙

research

∙ 01/07/2014

Cortical prediction markets

We investigate cortical learning from the perspective of mechanism desig...

0 David Balduzzi, et al. ∙

research

∙ 06/24/2013

Correlated random features for fast semi-supervised learning

This paper presents Correlated Nystrom Views (XNV), a fast semi-supervis...

0 Brian McWilliams, et al. ∙

research

∙ 01/10/2013

Domain Generalization via Invariant Feature Representation

This paper investigates domain generalization: How to take knowledge acq...

0 Krikamol Muandet, et al. ∙

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...

0 David Balduzzi, et al. ∙

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...

0 Pedro A. Ortega, et al. ∙

research

∙ 11/23/2011

Falsification and future performance

We information-theoretically reformulate two measures of capacity from s...

0 David Balduzzi, et al. ∙

research

∙ 10/17/2011

Information, learning and falsification

There are (at least) three approaches to quantifying information. The fi...

0 David Balduzzi, et al. ∙

research

∙ 07/06/2011

On the information-theoretic structure of distributed measurements

The internal structure of a measuring device, which depends on what its ...

0 David Balduzzi, et al. ∙

David Balduzzi

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