ML Anthology

Wilson, Andrew G

36 publications

NeurIPS 2023 A Performance-Driven Benchmark for Feature Selection in Tabular Deep Learning Valeriia Cherepanova, Roman Levin, Gowthami Somepalli, Jonas Geiping, C. Bayan Bruss, Andrew G Wilson, Tom Goldstein, Micah Goldblum
NeurIPS 2023 Battle of the Backbones: A Large-Scale Comparison of Pretrained Models Across Computer Vision Tasks Micah Goldblum, Hossein Souri, Renkun Ni, Manli Shu, Viraj Prabhu, Gowthami Somepalli, Prithvijit Chattopadhyay, Mark Ibrahim, Adrien Bardes, Judy Hoffman, Rama Chellappa, Andrew G Wilson, Tom Goldstein
NeurIPS 2023 CoLA: Exploiting Compositional Structure for Automatic and Efficient Numerical Linear Algebra Andres Potapczynski, Marc Finzi, Geoff Pleiss, Andrew G Wilson
NeurIPS 2023 Large Language Models Are Zero-Shot Time Series Forecasters Nate Gruver, Marc Finzi, Shikai Qiu, Andrew G Wilson
NeurIPS 2023 Protein Design with Guided Discrete Diffusion Nate Gruver, Samuel Stanton, Nathan Frey, Tim G. J. Rudner, Isidro Hotzel, Julien Lafrance-Vanasse, Arvind Rajpal, Kyunghyun Cho, Andrew G Wilson
NeurIPS 2023 Should We Learn Most Likely Functions or Parameters? Shikai Qiu, Tim G. J. Rudner, Sanyam Kapoor, Andrew G Wilson
NeurIPS 2023 Simplifying Neural Network Training Under Class Imbalance Ravid Shwartz-Ziv, Micah Goldblum, Yucen Li, C. Bayan Bruss, Andrew G Wilson
NeurIPS 2023 Understanding the Detrimental Class-Level Effects of Data Augmentation Polina Kirichenko, Mark Ibrahim, Randall Balestriero, Diane Bouchacourt, Shanmukha Ramakrishna Vedantam, Hamed Firooz, Andrew G Wilson
NeurIPS 2023 Visual Explanations of Image-Text Representations via Multi-Modal Information Bottleneck Attribution Ying Wang, Tim G. J. Rudner, Andrew G Wilson
NeurIPS 2022 Chroma-VAE: Mitigating Shortcut Learning with Generative Classifiers Wanqian Yang, Polina Kirichenko, Micah Goldblum, Andrew G Wilson
NeurIPS 2022 On Feature Learning in the Presence of Spurious Correlations Pavel Izmailov, Polina Kirichenko, Nate Gruver, Andrew G Wilson
NeurIPS 2022 On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification Sanyam Kapoor, Wesley J Maddox, Pavel Izmailov, Andrew G Wilson
NeurIPS 2022 PAC-Bayes Compression Bounds so Tight That They Can Explain Generalization Sanae Lotfi, Marc Finzi, Sanyam Kapoor, Andres Potapczynski, Micah Goldblum, Andrew G Wilson
NeurIPS 2022 Pre-Train Your Loss: Easy Bayesian Transfer Learning with Informative Priors Ravid Shwartz-Ziv, Micah Goldblum, Hossein Souri, Sanyam Kapoor, Chen Zhu, Yann LeCun, Andrew G Wilson
NeurIPS 2021 Bayesian Optimization with High-Dimensional Outputs Wesley J Maddox, Maximilian Balandat, Andrew G Wilson, Eytan Bakshy
NeurIPS 2021 Conditioning Sparse Variational Gaussian Processes for Online Decision-Making Wesley J Maddox, Samuel Stanton, Andrew G Wilson
NeurIPS 2021 Dangers of Bayesian Model Averaging Under Covariate Shift Pavel Izmailov, Patrick Nicholson, Sanae Lotfi, Andrew G Wilson
NeurIPS 2021 Does Knowledge Distillation Really Work? Samuel Stanton, Pavel Izmailov, Polina Kirichenko, Alexander A Alemi, Andrew G Wilson
NeurIPS 2021 Residual Pathway Priors for Soft Equivariance Constraints Marc Finzi, Gregory Benton, Andrew G Wilson
NeurIPS 2020 Bayesian Deep Learning and a Probabilistic Perspective of Generalization Andrew G Wilson, Pavel Izmailov
NeurIPS 2020 BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization Maximilian Balandat, Brian Karrer, Daniel Jiang, Samuel Daulton, Ben Letham, Andrew G Wilson, Eytan Bakshy
NeurIPS 2020 Improving GAN Training with Probability Ratio Clipping and Sample Reweighting Yue Wu, Pan Zhou, Andrew G Wilson, Eric P. Xing, Zhiting Hu
NeurIPS 2020 Learning Invariances in Neural Networks from Training Data Gregory Benton, Marc Finzi, Pavel Izmailov, Andrew G Wilson
NeurIPS 2020 Simplifying Hamiltonian and Lagrangian Neural Networks via Explicit Constraints Marc Finzi, Ke Alexander Wang, Andrew G Wilson
NeurIPS 2020 Why Normalizing Flows Fail to Detect Out-of-Distribution Data Polina Kirichenko, Pavel Izmailov, Andrew G Wilson
NeurIPS 2018 GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration Jacob Gardner, Geoff Pleiss, Kilian Q. Weinberger, David Bindel, Andrew G Wilson
NeurIPS 2018 Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs Timur Garipov, Pavel Izmailov, Dmitrii Podoprikhin, Dmitry P Vetrov, Andrew G Wilson
NeurIPS 2018 Scaling Gaussian Process Regression with Derivatives David Eriksson, Kun Dong, Eric Lee, David Bindel, Andrew G Wilson
NeurIPS 2017 Bayesian GAN Yunus Saatci, Andrew G Wilson
NeurIPS 2017 Bayesian Optimization with Gradients Jian Wu, Matthias Poloczek, Andrew G Wilson, Peter Frazier
NeurIPS 2017 Scalable Levy Process Priors for Spectral Kernel Learning Phillip A Jang, Andrew Loeb, Matthew Davidow, Andrew G Wilson
NeurIPS 2017 Scalable Log Determinants for Gaussian Process Kernel Learning Kun Dong, David Eriksson, Hannes Nickisch, David Bindel, Andrew G Wilson
NeurIPS 2016 Stochastic Variational Deep Kernel Learning Andrew G Wilson, Zhiting Hu, Ruslan Salakhutdinov, Eric P Xing
NeurIPS 2015 The Human Kernel Andrew G Wilson, Christoph Dann, Chris Lucas, Eric P Xing
NeurIPS 2014 Fast Kernel Learning for Multidimensional Pattern Extrapolation Andrew G Wilson, Elad Gilboa, Arye Nehorai, John P. Cunningham
NeurIPS 2010 Copula Processes Andrew G Wilson, Zoubin Ghahramani