Brian McWilliams

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ETH Zurich ∙ Disney Research

Research Scientist at Disney Research

Representation Learning via Invariant Causal Mechanisms

Self-supervised learning has emerged as a strategy to reduce the relianc...
10/15/2020 ∙ by Jovana Mitrovic, et al. ∙ 3 ∙ share

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EigenGame: PCA as a Nash Equilibrium

We present a novel view on principal component analysis (PCA) as a compe...
10/01/2020 ∙ by Ian Gemp, et al. ∙ 6 ∙ share

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Social Diversity and Social Preferences in Mixed-Motive Reinforcement Learning

Recent research on reinforcement learning in pure-conflict and pure-comm...
02/06/2020 ∙ by Kevin R. McKee, et al. ∙ 4 ∙ share

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Spectrogram Feature Losses for Music Source Separation

In this paper we study deep learning-based music source separation, and ...
01/15/2019 ∙ by Abhimanyu Sahai, et al. ∙ 0 ∙ share

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Neural Importance Sampling

We propose to use deep neural networks for generating samples in Monte C...
08/11/2018 ∙ by Thomas Müller, et al. ∙ 0 ∙ share

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A Fully Progressive Approach to Single-Image Super-Resolution

Recent deep learning approaches to single image super-resolution have ac...
04/09/2018 ∙ by Yifan Wang, et al. ∙ 0 ∙ share

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PhaseNet for Video Frame Interpolation

Most approaches for video frame interpolation require accurate dense cor...
04/03/2018 ∙ by Simone Meyer, et al. ∙ 0 ∙ share

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Deep Scattering: Rendering Atmospheric Clouds with Radiance-Predicting Neural Networks

We present a technique for efficiently synthesizing images of atmospheri...
09/15/2017 ∙ by Simon Kallweit, et al. ∙ 0 ∙ share

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Preserving Differential Privacy Between Features in Distributed Estimation

Privacy is crucial in many applications of machine learning. Legal, ethi...
03/01/2017 ∙ by Christina Heinze-Deml, 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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Scalable Adaptive Stochastic Optimization Using Random Projections

Adaptive stochastic gradient methods such as AdaGrad have gained popular...
11/21/2016 ∙ by Gabriel Krummenacher, 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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Variance Reduced Stochastic Gradient Descent with Neighbors

Stochastic Gradient Descent (SGD) is a workhorse in machine learning, ye...
06/11/2015 ∙ by Thomas Hofmann, et al. ∙ 0 ∙ share

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DUAL-LOCO: Distributing Statistical Estimation Using Random Projections

We present DUAL-LOCO, a communication-efficient algorithm for distribute...
06/08/2015 ∙ by Christina Heinze, 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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Subspace clustering of high-dimensional data: a predictive approach

In several application domains, high-dimensional observations are collec...
03/05/2012 ∙ by Brian McWilliams, et al. ∙ 0 ∙ share

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Multi-view predictive partitioning in high dimensions

Many modern data mining applications are concerned with the analysis of ...
02/02/2012 ∙ by Brian McWilliams, et al. ∙ 0 ∙ share

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Brian McWilliams

Featured Co-authors

Simon Lacoste-Julien

Thomas Hofmann

Giovanni Montana

Joel Z. Leibo

Thore Graepel

Aurelien Lucchi

David Balduzzi

Charles Blundell

Joachim M. Buhmann

Edward Hughes

Nicolai Meinshausen

Representation Learning via Invariant Causal Mechanisms

EigenGame: PCA as a Nash Equilibrium

Social Diversity and Social Preferences in Mixed-Motive Reinforcement Learning

Spectrogram Feature Losses for Music Source Separation

Neural Importance Sampling

A Fully Progressive Approach to Single-Image Super-Resolution

PhaseNet for Video Frame Interpolation

Deep Scattering: Rendering Atmospheric Clouds with Radiance-Predicting Neural Networks

Preserving Differential Privacy Between Features in Distributed Estimation

The Shattered Gradients Problem: If resnets are the answer, then what is the question?

Scalable Adaptive Stochastic Optimization Using Random Projections

Neural Taylor Approximations: Convergence and Exploration in Rectifier Networks

Variance Reduced Stochastic Gradient Descent with Neighbors

DUAL-LOCO: Distributing Statistical Estimation Using Random Projections

Correlated random features for fast semi-supervised learning

Subspace clustering of high-dimensional data: a predictive approach

Multi-view predictive partitioning in high dimensions