A. G. Wilson

发表

Complex spatial systems: Challenges for modellers

A. G. Wilson, A. Wilson, 2002 .

SysML: The New Frontier of Machine Learning Systems pdf

Gustavo Alonso, Dan Alistarh, Dimitris S. Papailiopoulos, 2019, ArXiv.

Scalable Log Determinants for Gaussian Process Kernel Learning

Andrew Gordon Wilson, Hannes Nickisch, David Eriksson, 2017, NIPS.

Deep Kernel Learning pdf

Andrew Gordon Wilson, Eric P. Xing, Zhiting Hu, 2015, AISTATS.

Stochastic Variational Deep Kernel Learning

Andrew Gordon Wilson, Eric P. Xing, Zhiting Hu, 2016, NIPS.

Kernel Interpolation for Scalable Structured Gaussian Processes (KISS-GP)

Andrew Gordon Wilson, Hannes Nickisch, A. G. Wilson, 2015, ICML.

MLSys: The New Frontier of Machine Learning Systems

Christopher De Sa, Michael I. Jordan, Phillip B. Gibbons, 2019, 1904.03257.

Bayesian Nonparametric Kernel-Learning

Barnabás Póczos, Andrew Gordon Wilson, Eric P. Xing, 2015, AISTATS.

A Process over all Stationary Covariance Kernels

A. G. Wilson, 2012 .

Function-Space Distributions over Kernels pdf

Andrew Gordon Wilson, Gregory Benton, Julio Albinati, 2019, NeurIPS.

Scalable Gaussian Processes for Characterizing Multidimensional Change Surfaces

Andrew Gordon Wilson, Eric P. Xing, Daniel B. Neill, 2015, AISTATS.

Fast Kronecker Inference in Gaussian Processes with non-Gaussian Likelihoods

Alexander J. Smola, Andrew Gordon Wilson, Daniel B. Neill, 2015, ICML.

The Human Kernel

Andrew Gordon Wilson, Eric P. Xing, Christopher G. Lucas, 2015, NIPS.

Averaging Weights Leads to Wider Optima and Better Generalization pdf

Andrew Gordon Wilson, Dmitry P. Vetrov, Pavel Izmailov, 2018, UAI.

Bayesian GANs

A. G. Wilson, Yunus Saatci, 2017, NIPS 2017.

Gaussian Process Kernels for Pattern Discovery and Extrapolation

Andrew Gordon Wilson, Ryan P. Adams, A. G. Wilson, 2013, ICML.

Fast Kernel Learning for Multidimensional Pattern Extrapolation

Andrew Gordon Wilson, Elad Gilboa, John P. Cunningham, 2014, NIPS.

Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs

Andrew Gordon Wilson, Dmitry P. Vetrov, Pavel Izmailov, 2018, NeurIPS.

Invertible Convolutional Networks

Wesley J. Maddox, A. G. Wilson, Wesley Maddox, 2019 .

Learning Scalable Deep Kernels with Recurrent Structure

Andrew Gordon Wilson, Eric P. Xing, Zhiting Hu, 2016, J. Mach. Learn. Res..

Fast Uncertainty Estimates and Bayesian Model Averaging of DNNs

A. G. Wilson, D. Vetrov, Wesley Maddox, 2018 .

Improving Stability in Deep Reinforcement Learning with Weight Averaging

A. G. Wilson, D. Vetrov, Pavel Izmailov, 2018 .

Effective Surrogate Models for Protein Design with Bayesian Optimization

A. G. Wilson, Phillip M. Maffettone, P. Greenside, 2021 .

BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization

Daniel R. Jiang, A. G. Wilson, B. Karrer, 2020, NeurIPS.

BOTORCH: PROGRAMMABLE BAYESIAN OPTIMIZATION

Daniel R. Jiang, A. G. Wilson, B. Karrer, 2019 .

BoTorch: Programmable Bayesian Optimization in PyTorch

Daniel R. Jiang, A. G. Wilson, B. Karrer, 2019, ArXiv.

Scalable GP-LSTMs with Semi-Stochastic Gradients

A. G. Wilson, E. Xing, Zhiting Hu, 2016 .

Towards understanding the true loss surface of deep neural networks using random matrix theory and iterative spectral methods

Stephen J. Roberts, A. G. Wilson, Diego Granziol, 2019 .

How does mini-batching affect curvature information for second order deep learning optimization?

Stephen J. Roberts, A. G. Wilson, Diego Granziol, 2019 .

The Deep Learning Limit: are negative neural network eigenvalues just noise?

Stephen J. Roberts, A. G. Wilson, Diego Granziol, 2019 .

M L ] 3 0 Ja n 20 18 Bayesian Nonparametric Kernel-Learning

Junier B. Oliva, A. G. Wilson, E. Xing, 2018 .

Proceedings for 20th Annual Conference on Neural Information Processing Systems (NIPS)

Andrew Gordon Wilson, Eric P. Xing, Christopher G. Lucas, 2015 .

Learning Invariances in Neural Networks from Training Data

Andrew Gordon Wilson, Marc Finzi, Pavel Izmailov, 2020, NeurIPS.

Bayesian Inference for NMR Spectroscopy with Applications to Chemical Quantification

Sebastian Nowozin, Andrew Gordon Wilson, Lynn F. Gladden, 2014, 1402.3580.

Sparse Orthogonal Variational Inference for Gaussian Processes

Kilian Q. Weinberger, A. G. Wilson, Stephen Tyree, 2019 .

Bayesian Model Selection, the Marginal Likelihood, and Generalization

Gregory W. Benton, A. G. Wilson, Pavel Izmailov, 2022, ICML.

Transfer Learning with Deep Tabular Models

A. G. Wilson, T. Goldstein, Avi Schwarzschild, 2022, ICLR.

Low-Precision Arithmetic for Fast Gaussian Processes

Wesley J. Maddox, A. G. Wilson, Andres Potapczynski, 2022, UAI.

Volatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian Processes

Gregory W. Benton, Wesley J. Maddox, A. G. Wilson, 2022, ICML.