Andrew Gordon Wilson

Simplifying Hamiltonian and Lagrangian Neural Networks via Explicit Constraints

Oct 26, 2020
Marc Finzi, Ke Alexander Wang, Andrew Gordon Wilson

* NeurIPS 2020. Code available at https://github.com/mfinzi/constrained-hamiltonian-neural-networks

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Learning Invariances in Neural Networks

Oct 22, 2020
Gregory Benton, Marc Finzi, Pavel Izmailov, Andrew Gordon Wilson

* NeurIPS 2020. Code available at https://github.com/g-benton/learning-invariances

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On the model-based stochastic value gradient for continuous reinforcement learning

Aug 28, 2020
Brandon Amos, Samuel Stanton, Denis Yarats, Andrew Gordon Wilson

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Improving GAN Training with Probability Ratio Clipping and Sample Reweighting

Jun 30, 2020
Yue Wu, Pan Zhou, Andrew Gordon Wilson, Eric P. Xing, Zhiting Hu

* Fixed typos. Code available at: https://github.com/Holmeswww/PPOGAN

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Why Normalizing Flows Fail to Detect Out-of-Distribution Data

Jun 15, 2020
Polina Kirichenko, Pavel Izmailov, Andrew Gordon Wilson

* Code is available at https://github.com/PolinaKirichenko/flows_ood

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Bayesian Deep Learning and a Probabilistic Perspective of Generalization

Mar 17, 2020
Andrew Gordon Wilson, Pavel Izmailov

* 28 pages, 17 figures

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Rethinking Parameter Counting in Deep Models: Effective Dimensionality Revisited

Mar 04, 2020
Wesley J. Maddox, Gregory Benton, Andrew Gordon Wilson

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Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data

Feb 25, 2020
Marc Finzi, Samuel Stanton, Pavel Izmailov, Andrew Gordon Wilson

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* Published at NeurIPS 2019

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BoTorch: Programmable Bayesian Optimization in PyTorch

Oct 14, 2019
Maximilian Balandat, Brian Karrer, Daniel R. Jiang, Samuel Daulton, Benjamin Letham, Andrew Gordon Wilson, Eytan Bakshy

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Subspace Inference for Bayesian Deep Learning

Jul 17, 2019
Pavel Izmailov, Wesley J. Maddox, Polina Kirichenko, Timur Garipov, Dmitry Vetrov, Andrew Gordon Wilson

* Published at UAI 2019

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SWALP : Stochastic Weight Averaging in Low-Precision Training

May 20, 2019
Guandao Yang, Tianyi Zhang, Polina Kirichenko, Junwen Bai, Andrew Gordon Wilson, Christopher De Sa

* Published at ICML 2019

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Simple Black-box Adversarial Attacks

May 17, 2019
Chuan Guo, Jacob R. Gardner, Yurong You, Andrew Gordon Wilson, Kilian Q. Weinberger

* Published at ICML 2019

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SysML: The New Frontier of Machine Learning Systems

May 01, 2019
Alexander Ratner, Dan Alistarh, Gustavo Alonso, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Jennifer Chayes, Eric Chung, Bill Dally, Jeff Dean, Inderjit S. Dhillon, Alexandros Dimakis, Pradeep Dubey, Charles Elkan, Grigori Fursin, Gregory R. Ganger, Lise Getoor, Phillip B. Gibbons, Garth A. Gibson, Joseph E. Gonzalez, Justin Gottschlich, Song Han, Kim Hazelwood, Furong Huang, Martin Jaggi, Kevin Jamieson, Michael I. Jordan, Gauri Joshi, Rania Khalaf, Jason Knight, Jakub Konečný, Tim Kraska, Arun Kumar, Anastasios Kyrillidis, Aparna Lakshmiratan, Jing Li, Samuel Madden, H. Brendan McMahan, Erik Meijer, Ioannis Mitliagkas, Rajat Monga, Derek Murray, Kunle Olukotun, Dimitris Papailiopoulos, Gennady Pekhimenko, Theodoros Rekatsinas, Afshin Rostamizadeh, Christopher Ré, Christopher De Sa, Hanie Sedghi, Siddhartha Sen, Virginia Smith, Alex Smola, Dawn Song, Evan Sparks, Ion Stoica, Vivienne Sze, Madeleine Udell, Joaquin Vanschoren, Shivaram Venkataraman, Rashmi Vinayak, Markus Weimer, Andrew Gordon Wilson, Eric Xing, Matei Zaharia, Ce Zhang, Ameet Talwalkar

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Exact Gaussian Processes on a Million Data Points

Mar 19, 2019
Ke Alexander Wang, Geoff Pleiss, Jacob R. Gardner, Stephen Tyree, Kilian Q. Weinberger, Andrew Gordon Wilson

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Practical Multi-fidelity Bayesian Optimization for Hyperparameter Tuning

Mar 12, 2019
Jian Wu, Saul Toscano-Palmerin, Peter I. Frazier, Andrew Gordon Wilson

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Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning

Feb 11, 2019
Ruqi Zhang, Chunyuan Li, Jianyi Zhang, Changyou Chen, Andrew Gordon Wilson

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A Simple Baseline for Bayesian Uncertainty in Deep Learning

Feb 07, 2019
Wesley Maddox, Timur Garipov, Pavel Izmailov, Dmitry Vetrov, Andrew Gordon Wilson

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Change Surfaces for Expressive Multidimensional Changepoints and Counterfactual Prediction

Oct 30, 2018
William Herlands, Daniel B. Neill, Hannes Nickisch, Andrew Gordon Wilson

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Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs

Oct 30, 2018
Timur Garipov, Pavel Izmailov, Dmitrii Podoprikhin, Dmitry Vetrov, Andrew Gordon Wilson

* Appears at Advances in Neural Information Processing Systems (NIPS), 2018

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Scaling Gaussian Process Regression with Derivatives

Oct 29, 2018
David Eriksson, Kun Dong, Eric Hans Lee, David Bindel, Andrew Gordon Wilson

* Advances in Neural Information Processing Systems 32 (NIPS), 2018
* Appears at Advances in Neural Information Processing Systems 32 (NIPS), 2018

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GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration

Oct 29, 2018
Jacob R. Gardner, Geoff Pleiss, David Bindel, Kilian Q. Weinberger, Andrew Gordon Wilson

* NIPS 2018

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Averaging Weights Leads to Wider Optima and Better Generalization

Aug 08, 2018
Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry Vetrov, Andrew Gordon Wilson

* Appears at the Conference on Uncertainty in Artificial Intelligence (UAI), 2018

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Constant-Time Predictive Distributions for Gaussian Processes

Jun 20, 2018
Geoff Pleiss, Jacob R. Gardner, Kilian Q. Weinberger, Andrew Gordon Wilson

* ICML 2018

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Andrew Gordon Wilson

Simplifying Hamiltonian and Lagrangian Neural Networks via Explicit Constraints

Learning Invariances in Neural Networks

On the model-based stochastic value gradient for continuous reinforcement learning

Improving GAN Training with Probability Ratio Clipping and Sample Reweighting

Why Normalizing Flows Fail to Detect Out-of-Distribution Data

Bayesian Deep Learning and a Probabilistic Perspective of Generalization

Rethinking Parameter Counting in Deep Models: Effective Dimensionality Revisited

Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data

The Case for Bayesian Deep Learning

Semi-Supervised Learning with Normalizing Flows

Randomly Projected Additive Gaussian Processes for Regression

Function-Space Distributions over Kernels

BoTorch: Programmable Bayesian Optimization in PyTorch

Subspace Inference for Bayesian Deep Learning

SWALP : Stochastic Weight Averaging in Low-Precision Training

Simple Black-box Adversarial Attacks

SysML: The New Frontier of Machine Learning Systems

Exact Gaussian Processes on a Million Data Points

Practical Multi-fidelity Bayesian Optimization for Hyperparameter Tuning

Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning

A Simple Baseline for Bayesian Uncertainty in Deep Learning

Change Surfaces for Expressive Multidimensional Changepoints and Counterfactual Prediction

Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs

Scaling Gaussian Process Regression with Derivatives

GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration

Averaging Weights Leads to Wider Optima and Better Generalization

Constant-Time Predictive Distributions for Gaussian Processes