David Balduzzi

Real World Games Look Like Spinning Tops

This paper investigates the geometrical properties of real world games (...
04/20/2020 ∙ by Wojciech Marian Czarnecki, et al. ∙ 10 ∙ share

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Learning to Resolve Alliance Dilemmas in Many-Player Zero-Sum Games

Zero-sum games have long guided artificial intelligence research, since ...
02/27/2020 ∙ by Edward Hughes, et al. ∙ 0 ∙ share

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From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization

In this paper we investigate the Follow the Regularized Leader dynamics ...
02/19/2020 ∙ by Julien Perolat, et al. ∙ 32 ∙ share

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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...
02/14/2020 ∙ by Gauthier Gidel, et al. ∙ 0 ∙ share

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Smooth markets: A basic mechanism for organizing gradient-based learners

With the success of modern machine learning, it is becoming increasingly...
01/14/2020 ∙ by David Balduzzi, et al. ∙ 10 ∙ share

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LOGAN: Latent Optimisation for Generative Adversarial Networks

Training generative adversarial networks requires balancing of delicate ...
12/02/2019 ∙ by Yan Wu, et al. ∙ 6 ∙ share

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Differentiable Game Mechanics

Deep learning is built on the foundational guarantee that gradient desce...
05/13/2019 ∙ by Alistair Letcher, et al. ∙ 0 ∙ share

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Open-ended Learning in Symmetric Zero-sum Games

Zero-sum games such as chess and poker are, abstractly, functions that e...
01/23/2019 ∙ by David Balduzzi, et al. ∙ 16 ∙ share

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Stable Opponent Shaping in Differentiable Games

A growing number of learning methods are actually games which optimise m...
11/20/2018 ∙ by Alistair Letcher, et al. ∙ 74 ∙ share

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Re-evaluating evaluation

Progress in machine learning is measured by careful evaluation on proble...
06/07/2018 ∙ by David Balduzzi, et al. ∙ 0 ∙ share

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The Mechanics of n-Player Differentiable Games

The cornerstone underpinning deep learning is the guarantee that gradien...
02/15/2018 ∙ by David Balduzzi, et al. ∙ 0 ∙ share

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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 ...
02/28/2017 ∙ by David Balduzzi, et al. ∙ 0 ∙ share

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Strongly-Typed Agents are Guaranteed to Interact Safely

As artificial agents proliferate, it is becoming increasingly important ...
02/24/2017 ∙ by David Balduzzi, et al. ∙ 0 ∙ share

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Neural Taylor Approximations: Convergence and Exploration in Rectifier Networks

Modern convolutional networks, incorporating rectifiers and max-pooling,...
11/07/2016 ∙ by David Balduzzi, et al. ∙ 0 ∙ share

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Deep Reconstruction-Classification Networks for Unsupervised Domain Adaptation

In this paper, we propose a novel unsupervised domain adaptation algorit...
07/12/2016 ∙ by Muhammad Ghifary, et al. ∙ 0 ∙ share

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Deep Online Convex Optimization with Gated Games

Methods from convex optimization are widely used as building blocks for ...
04/07/2016 ∙ by David Balduzzi, et al. ∙ 0 ∙ share

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Compliance-Aware Bandits

Motivated by clinical trials, we study bandits with observable non-compl...
02/09/2016 ∙ by Nicolás Della Penna, et al. ∙ 0 ∙ share

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Strongly-Typed Recurrent Neural Networks

Recurrent neural networks are increasing popular models for sequential l...
02/06/2016 ∙ by David Balduzzi, et al. ∙ 0 ∙ share

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Scatter Component Analysis: A Unified Framework for Domain Adaptation and Domain Generalization

This paper addresses classification tasks on a particular target domain ...
10/15/2015 ∙ by Muhammad Ghifary, et al. ∙ 0 ∙ share

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Semantics, Representations and Grammars for Deep Learning

Deep learning is currently the subject of intensive study. However, fund...
09/29/2015 ∙ by David Balduzzi, et al. ∙ 0 ∙ share

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Compatible Value Gradients for Reinforcement Learning of Continuous Deep Policies

This paper proposes GProp, a deep reinforcement learning algorithm for c...
09/10/2015 ∙ by David Balduzzi, et al. ∙ 0 ∙ share

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Deep Online Convex Optimization by Putting Forecaster to Sleep

Methods from convex optimization such as accelerated gradient descent ar...
09/06/2015 ∙ by David Balduzzi, et al. ∙ 0 ∙ share

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Domain Generalization for Object Recognition with Multi-task Autoencoders

The problem of domain generalization is to take knowledge acquired from ...
08/31/2015 ∙ by Muhammad Ghifary, et al. ∙ 0 ∙ share

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Cortical prediction markets

We investigate cortical learning from the perspective of mechanism desig...
01/07/2014 ∙ by David Balduzzi, et al. ∙ 0 ∙ share

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Correlated random features for fast semi-supervised learning

This paper presents Correlated Nystrom Views (XNV), a fast semi-supervis...
06/24/2013 ∙ by Brian McWilliams, et al. ∙ 0 ∙ share

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Domain Generalization via Invariant Feature Representation

This paper investigates domain generalization: How to take knowledge acq...
01/10/2013 ∙ by Krikamol Muandet, et al. ∙ 0 ∙ share

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Towards a learning-theoretic analysis of spike-timing dependent plasticity

This paper suggests a learning-theoretic perspective on how synaptic pla...
09/25/2012 ∙ by David Balduzzi, et al. ∙ 0 ∙ share

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A Nonparametric Conjugate Prior Distribution for the Maximizing Argument of a Noisy Function

We propose a novel Bayesian approach to solve stochastic optimization pr...
06/09/2012 ∙ by Pedro A. Ortega, et al. ∙ 0 ∙ share

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Falsification and future performance

We information-theoretically reformulate two measures of capacity from s...
11/23/2011 ∙ by David Balduzzi, et al. ∙ 0 ∙ share

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Information, learning and falsification

There are (at least) three approaches to quantifying information. The fi...
10/17/2011 ∙ by David Balduzzi, et al. ∙ 0 ∙ share

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On the information-theoretic structure of distributed measurements

The internal structure of a measuring device, which depends on what its ...
07/06/2011 ∙ by David Balduzzi, et al. ∙ 0 ∙ share

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David Balduzzi

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