Andrew Gordon Wilson

Publication Activity (10 Years)

Years Active: 2010-2024
Publications (10 Years): 193

Top Venues

CoRR

NeurIPS

ICML

AISTATS

Publications

Abdul Fatir Ansari, Lorenzo Stella, Ali Caner Türkmen, Xiyuan Zhang, Pedro Mercado, Huibin Shen, Oleksandr Shchur, Syama Sundar Rangapuram, Sebastian Pineda-Arango, Shubham Kapoor, Jasper Zschiegner, Danielle C. Maddix, Michael W. Mahoney, Kari Torkkola, Andrew Gordon Wilson, Michael Bohlke-Schneider, Yuyang Wang
Chronos: Learning the Language of Time Series. CoRR (2024)
Hossein Souri, Arpit Bansal, Hamid Kazemi, Liam Fowl, Aniruddha Saha, Jonas Geiping, Andrew Gordon Wilson, Rama Chellappa, Tom Goldstein, Micah Goldblum
Generating Potent Poisons and Backdoors from Scratch with Guided Diffusion. CoRR (2024)
Sanyam Kapoor, Nate Gruver, Manley Roberts, Katherine M. Collins, Arka Pal, Umang Bhatt, Adrian Weller, Samuel Dooley, Micah Goldblum, Andrew Gordon Wilson
Large Language Models Must Be Taught to Know What They Don't Know. CoRR (2024)
Tim G. J. Rudner, Ya Shi Zhang, Andrew Gordon Wilson, Julia Kempe
Mind the GAP: Improving Robustness to Subpopulation Shifts with Group-Aware Priors. CoRR (2024)
Alan Nawzad Amin, Andrew Gordon Wilson
Scalable and Flexible Causal Discovery with an Efficient Test for Adjacency. CoRR (2024)
Hoang Phan, Andrew Gordon Wilson, Qi Lei
Controllable Prompt Tuning For Balancing Group Distributional Robustness. CoRR (2024)
Polina Kirichenko, Mark Ibrahim, Randall Balestriero, Diane Bouchacourt, Ramakrishna Vedantam, Hamed Firooz, Andrew Gordon Wilson
Understanding the Detrimental Class-level Effects of Data Augmentation. CoRR (2024)
Shikai Qiu, Boran Han, Danielle C. Maddix, Shuai Zhang, Yuyang Wang, Andrew Gordon Wilson
Transferring Knowledge from Large Foundation Models to Small Downstream Models. CoRR (2024)
Theodore Papamarkou, Maria Skoularidou, Konstantina Palla, Laurence Aitchison, Julyan Arbel, David B. Dunson, Maurizio Filippone, Vincent Fortuin, Philipp Hennig, José Miguel Hernández-Lobato, Aliaksandr Hubin, Alexander Immer, Theofanis Karaletsos, Mohammad Emtiyaz Khan, Agustinus Kristiadi, Yingzhen Li, Stephan Mandt, Christopher Nemeth, Michael A. Osborne, Tim G. J. Rudner, David Rügamer, Yee Whye Teh, Max Welling, Andrew Gordon Wilson, Ruqi Zhang
Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI. CoRR (2024)
Nate Gruver, Anuroop Sriram, Andrea Madotto, Andrew Gordon Wilson, C. Lawrence Zitnick, Zachary W. Ulissi
Fine-Tuned Language Models Generate Stable Inorganic Materials as Text. ICLR (2024)
Tim G. J. Rudner, Ya Shi Zhang, Andrew Gordon Wilson, Julia Kempe
Mind the GAP: Improving Robustness to Subpopulation Shifts with Group-Aware Priors. AISTATS (2024)
Yucen Lily Li, Tim G. J. Rudner, Andrew Gordon Wilson
A Study of Bayesian Neural Network Surrogates for Bayesian Optimization. ICLR (2024)
Ravid Shwartz-Ziv, Micah Goldblum, Arpit Bansal, C. Bayan Bruss, Yann LeCun, Andrew Gordon Wilson
Just How Flexible are Neural Networks in Practice? CoRR (2024)
Nate Gruver, Anuroop Sriram, Andrea Madotto, Andrew Gordon Wilson, C. Lawrence Zitnick, Zachary W. Ulissi
Fine-Tuned Language Models Generate Stable Inorganic Materials as Text. CoRR (2024)
Samuel Lavoie, Polina Kirichenko, Mark Ibrahim, Mahmoud Assran, Andrew Gordon Wilson, Aaron C. Courville, Nicolas Ballas
Modeling Caption Diversity in Contrastive Vision-Language Pretraining. CoRR (2024)
Sanae Lotfi, Yilun Kuang, Brandon Amos, Micah Goldblum, Marc Finzi, Andrew Gordon Wilson
Unlocking Tokens as Data Points for Generalization Bounds on Larger Language Models. CoRR (2024)
Shikai Qiu, Andres Potapczynski, Marc Finzi, Micah Goldblum, Andrew Gordon Wilson
Compute Better Spent: Replacing Dense Layers with Structured Matrices. CoRR (2024)
Andres Potapczynski, Marc Finzi, Geoff Pleiss, Andrew Gordon Wilson
CoLA: Exploiting Compositional Structure for Automatic and Efficient Numerical Linear Algebra. NeurIPS (2023)
Jonas Geiping, Micah Goldblum, Gowthami Somepalli, Ravid Shwartz-Ziv, Tom Goldstein, Andrew Gordon Wilson
How Much Data Are Augmentations Worth? An Investigation into Scaling Laws, Invariance, and Implicit Regularization. ICLR (2023)
Tim G. J. Rudner, Sanyam Kapoor, Shikai Qiu, Andrew Gordon Wilson
Function-Space Regularization in Neural Networks: A Probabilistic Perspective. ICML (2023)
Nate Gruver, Samuel Stanton, Nathan C. Frey, Tim G. J. Rudner, Isidro Hötzel, Julien Lafrance-Vanasse, Arvind Rajpal, Kyunghyun Cho, Andrew Gordon Wilson
Protein Design with Guided Discrete Diffusion. NeurIPS (2023)
Shikai Qiu, Tim G. J. Rudner, Sanyam Kapoor, Andrew Gordon Wilson
Should We Learn Most Likely Functions or Parameters? NeurIPS (2023)
Roman Levin, Valeriia Cherepanova, Avi Schwarzschild, Arpit Bansal, C. Bayan Bruss, Tom Goldstein, Andrew Gordon Wilson, Micah Goldblum
Transfer Learning with Deep Tabular Models. ICLR (2023)
Shikai Qiu, Tim G. J. Rudner, Sanyam Kapoor, Andrew Gordon Wilson
Should We Learn Most Likely Functions or Parameters? CoRR (2023)
Nate Gruver, Samuel Stanton, Nathan C. Frey, Tim G. J. Rudner, Isidro Hötzel, Julien Lafrance-Vanasse, Arvind Rajpal, Kyunghyun Cho, Andrew Gordon Wilson
Protein Design with Guided Discrete Diffusion. CoRR (2023)
Rami Aly, Xingjian Shi, Kaixiang Lin, Aston Zhang, Andrew Gordon Wilson
Automated Few-Shot Classification with Instruction-Finetuned Language Models. EMNLP (Findings) (2023)
Polina Kirichenko, Mark Ibrahim, Randall Balestriero, Diane Bouchacourt, Shanmukha Ramakrishna Vedantam, Hamed Firooz, Andrew Gordon Wilson
Understanding the detrimental class-level effects of data augmentation. NeurIPS (2023)
Micah Goldblum, Hossein Souri, Renkun Ni, Manli Shu, Viraj Prabhu, Gowthami Somepalli, Prithvijit Chattopadhyay, Mark Ibrahim, Adrien Bardes, Judy Hoffman, Rama Chellappa, Andrew Gordon Wilson, Tom Goldstein
Battle of the Backbones: A Large-Scale Comparison of Pretrained Models across Computer Vision Tasks. CoRR (2023)
Micah Goldblum, Marc Finzi, Keefer Rowan, Andrew Gordon Wilson
The No Free Lunch Theorem, Kolmogorov Complexity, and the Role of Inductive Biases in Machine Learning. CoRR (2023)
Andres Potapczynski, Marc Finzi, Geoff Pleiss, Andrew Gordon Wilson
CoLA: Exploiting Compositional Structure for Automatic and Efficient Numerical Linear Algebra. CoRR (2023)
Sanae Lotfi, Marc Finzi, Yilun Kuang, Tim G. J. Rudner, Micah Goldblum, Andrew Gordon Wilson
Non-Vacuous Generalization Bounds for Large Language Models. CoRR (2023)
Rami Aly, Xingjian Shi, Kaixiang Lin, Aston Zhang, Andrew Gordon Wilson
Automated Few-shot Classification with Instruction-Finetuned Language Models. CoRR (2023)
Yanjun Liu, Milena Jovanovic, Krishnanand Mallayya, Wesley J. Maddox, Andrew Gordon Wilson, Sebastian Klemenz, Leslie M. Schoop, Eun-Ah Kim
Materials Expert-Artificial Intelligence for Materials Discovery. CoRR (2023)
Nate Gruver, Marc Anton Finzi, Micah Goldblum, Andrew Gordon Wilson
The Lie Derivative for Measuring Learned Equivariance. ICLR (2023)
Shikai Qiu, Andres Potapczynski, Pavel Izmailov, Andrew Gordon Wilson
Simple and Fast Group Robustness by Automatic Feature Reweighting. ICML (2023)
Nate Gruver, Marc Finzi, Shikai Qiu, Andrew Gordon Wilson
Large Language Models Are Zero-Shot Time Series Forecasters. NeurIPS (2023)
Valeriia Cherepanova, Roman Levin, Gowthami Somepalli, Jonas Geiping, C. Bayan Bruss, Andrew Gordon Wilson, Tom Goldstein, Micah Goldblum
A Performance-Driven Benchmark for Feature Selection in Tabular Deep Learning. CoRR (2023)
Marc Finzi, Anudhyan Boral, Andrew Gordon Wilson, Fei Sha, Leonardo Zepeda-Núñez
User-defined Event Sampling and Uncertainty Quantification in Diffusion Models for Physical Dynamical Systems. CoRR (2023)
Ravid Shwartz-Ziv, Micah Goldblum, Yucen Lily Li, C. Bayan Bruss, Andrew Gordon Wilson
Simplifying Neural Network Training Under Class Imbalance. NeurIPS (2023)
Marc Anton Finzi, Anudhyan Boral, Andrew Gordon Wilson, Fei Sha, Leonardo Zepeda-Núñez
User-defined Event Sampling and Uncertainty Quantification in Diffusion Models for Physical Dynamical Systems. ICML (2023)
Marc Finzi, Andres Potapczynski, Matthew Choptuik, Andrew Gordon Wilson
A Stable and Scalable Method for Solving Initial Value PDEs with Neural Networks. CoRR (2023)
Randall Balestriero, Mark Ibrahim, Vlad Sobal, Ari Morcos, Shashank Shekhar, Tom Goldstein, Florian Bordes, Adrien Bardes, Grégoire Mialon, Yuandong Tian, Avi Schwarzschild, Andrew Gordon Wilson, Jonas Geiping, Quentin Garrido, Pierre Fernandez, Amir Bar, Hamed Pirsiavash, Yann LeCun, Micah Goldblum
A Cookbook of Self-Supervised Learning. CoRR (2023)
Micah Goldblum, Anima Anandkumar, Richard G. Baraniuk, Tom Goldstein, Kyunghyun Cho, Zachary C. Lipton, Melanie Mitchell, Preetum Nakkiran, Max Welling, Andrew Gordon Wilson
Perspectives on the State and Future of Deep Learning - 2023. CoRR (2023)
Zichang Liu, Zhiqiang Tang, Xingjian Shi, Aston Zhang, Mu Li, Anshumali Shrivastava, Andrew Gordon Wilson
Learning Multimodal Data Augmentation in Feature Space. ICLR (2023)
Samuel Stanton, Wesley J. Maddox, Andrew Gordon Wilson
Bayesian Optimization with Conformal Prediction Sets. AISTATS (2023)
Ying Wang, Tim G. J. Rudner, Andrew Gordon Wilson
Visual Explanations of Image-Text Representations via Multi-Modal Information Bottleneck Attribution. NeurIPS (2023)
Nate Gruver, Marc Finzi, Shikai Qiu, Andrew Gordon Wilson
Large Language Models Are Zero-Shot Time Series Forecasters. CoRR (2023)
Valeriia Cherepanova, Roman Levin, Gowthami Somepalli, Jonas Geiping, C. Bayan Bruss, Andrew Gordon Wilson, Tom Goldstein, Micah Goldblum
A Performance-Driven Benchmark for Feature Selection in Tabular Deep Learning. NeurIPS (2023)
Polina Kirichenko, Pavel Izmailov, Andrew Gordon Wilson
Last Layer Re-Training is Sufficient for Robustness to Spurious Correlations. ICLR (2023)
Ravid Shwartz-Ziv, Micah Goldblum, Yucen Lily Li, C. Bayan Bruss, Andrew Gordon Wilson
Simplifying Neural Network Training Under Class Imbalance. CoRR (2023)
Micah Goldblum, Hossein Souri, Renkun Ni, Manli Shu, Viraj Prabhu, Gowthami Somepalli, Prithvijit Chattopadhyay, Mark Ibrahim, Adrien Bardes, Judy Hoffman, Rama Chellappa, Andrew Gordon Wilson, Tom Goldstein
Battle of the Backbones: A Large-Scale Comparison of Pretrained Models across Computer Vision Tasks. NeurIPS (2023)
Gianluca Detommaso, Alberto Gasparin, Michele Donini, Matthias W. Seeger, Andrew Gordon Wilson, Cédric Archambeau
Fortuna: A Library for Uncertainty Quantification in Deep Learning. CoRR (2023)
Yucen Lily Li, Tim G. J. Rudner, Andrew Gordon Wilson
A Study of Bayesian Neural Network Surrogates for Bayesian Optimization. CoRR (2023)
Marc Anton Finzi, Andres Potapczynski, Matthew Choptuik, Andrew Gordon Wilson
A Stable and Scalable Method for Solving Initial Value PDEs with Neural Networks. ICLR (2023)
Shikai Qiu, Andres Potapczynski, Pavel Izmailov, Andrew Gordon Wilson
Simple and Fast Group Robustness by Automatic Feature Reweighting. CoRR (2023)
Ying Wang, Tim G. J. Rudner, Andrew Gordon Wilson
Visual Explanations of Image-Text Representations via Multi-Modal Information Bottleneck Attribution. CoRR (2023)
Tim G. J. Rudner, Sanyam Kapoor, Shikai Qiu, Andrew Gordon Wilson
Function-Space Regularization in Neural Networks: A Probabilistic Perspective. CoRR (2023)
Wesley J. Maddox, Andres Potapczynski, Andrew Gordon Wilson
Low-precision arithmetic for fast Gaussian processes. UAI (2022)
Nate Gruver, Marc Anton Finzi, Samuel Don Stanton, Andrew Gordon Wilson
Deconstructing the Inductive Biases of Hamiltonian Neural Networks. ICLR (2022)
Sanae Lotfi, Pavel Izmailov, Gregory W. Benton, Micah Goldblum, Andrew Gordon Wilson
Bayesian Model Selection, the Marginal Likelihood, and Generalization. CoRR (2022)
Ravid Shwartz-Ziv, Micah Goldblum, Hossein Souri, Sanyam Kapoor, Chen Zhu, Yann LeCun, Andrew Gordon Wilson
Pre-Train Your Loss: Easy Bayesian Transfer Learning with Informative Priors. CoRR (2022)
Ruqi Zhang, Andrew Gordon Wilson, Christopher De Sa
Low-Precision Stochastic Gradient Langevin Dynamics. ICML (2022)
Sanae Lotfi, Pavel Izmailov, Gregory W. Benton, Micah Goldblum, Andrew Gordon Wilson
Bayesian Model Selection, the Marginal Likelihood, and Generalization. ICML (2022)
Gregory W. Benton, Wesley J. Maddox, Andrew Gordon Wilson
Volatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian Processes. ICML (2022)
Samuel Stanton, Wesley J. Maddox, Andrew Gordon Wilson
Bayesian Optimization with Conformal Coverage Guarantees. CoRR (2022)
Gregory W. Benton, Wesley J. Maddox, Andrew Gordon Wilson
Volatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian Processes. CoRR (2022)
Amin Ghiasi, Hamid Kazemi, Eitan Borgnia, Steven Reich, Manli Shu, Micah Goldblum, Andrew Gordon Wilson, Tom Goldstein
What do Vision Transformers Learn? A Visual Exploration. CoRR (2022)
Wesley J. Maddox, Andres Potapczynski, Andrew Gordon Wilson
Low-Precision Arithmetic for Fast Gaussian Processes. CoRR (2022)
Pavel Izmailov, Polina Kirichenko, Nate Gruver, Andrew Gordon Wilson
On Feature Learning in the Presence of Spurious Correlations. CoRR (2022)
Samuel Stanton, Wesley J. Maddox, Nate Gruver, Phillip Maffettone, Emily Delaney, Peyton Greenside, Andrew Gordon Wilson
Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders. ICML (2022)
Renkun Ni, Ping-yeh Chiang, Jonas Geiping, Micah Goldblum, Andrew Gordon Wilson, Tom Goldstein
K-SAM: Sharpness-Aware Minimization at the Speed of SGD. CoRR (2022)
Roman Levin, Valeriia Cherepanova, Avi Schwarzschild, Arpit Bansal, C. Bayan Bruss, Tom Goldstein, Andrew Gordon Wilson, Micah Goldblum
Transfer Learning with Deep Tabular Models. CoRR (2022)
Zichang Liu, Zhiqiang Tang, Xingjian Shi, Aston Zhang, Mu Li, Anshumali Shrivastava, Andrew Gordon Wilson
Learning Multimodal Data Augmentation in Feature Space. CoRR (2022)
Jonas Geiping, Micah Goldblum, Gowthami Somepalli, Ravid Shwartz-Ziv, Tom Goldstein, Andrew Gordon Wilson
How Much Data Are Augmentations Worth? An Investigation into Scaling Laws, Invariance, and Implicit Regularization. CoRR (2022)
Sanae Lotfi, Marc Finzi, Sanyam Kapoor, Andres Potapczynski, Micah Goldblum, Andrew Gordon Wilson
PAC-Bayes Compression Bounds So Tight That They Can Explain Generalization. NeurIPS (2022)
Nate Gruver, Marc Finzi, Micah Goldblum, Andrew Gordon Wilson
The Lie Derivative for Measuring Learned Equivariance. CoRR (2022)
Ruqi Zhang, Andrew Gordon Wilson, Christopher De Sa
Low-Precision Stochastic Gradient Langevin Dynamics. CoRR (2022)
Wanqian Yang, Polina Kirichenko, Micah Goldblum, Andrew Gordon Wilson
Chroma-VAE: Mitigating Shortcut Learning with Generative Classifiers. CoRR (2022)
Sanae Lotfi, Marc Finzi, Sanyam Kapoor, Andres Potapczynski, Micah Goldblum, Andrew Gordon Wilson
PAC-Bayes Compression Bounds So Tight That They Can Explain Generalization. CoRR (2022)
Nate Gruver, Marc Finzi, Samuel Stanton, Andrew Gordon Wilson
Deconstructing the Inductive Biases of Hamiltonian Neural Networks. CoRR (2022)
Polina Kirichenko, Pavel Izmailov, Andrew Gordon Wilson
Last Layer Re-Training is Sufficient for Robustness to Spurious Correlations. CoRR (2022)
Wanqian Yang, Polina Kirichenko, Micah Goldblum, Andrew Gordon Wilson
Chroma-VAE: Mitigating Shortcut Learning with Generative Classifiers. NeurIPS (2022)
Pavel Izmailov, Polina Kirichenko, Nate Gruver, Andrew Gordon Wilson
On Feature Learning in the Presence of Spurious Correlations. NeurIPS (2022)
Sanyam Kapoor, Wesley J. Maddox, Pavel Izmailov, Andrew Gordon Wilson
On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification. NeurIPS (2022)
Samuel Stanton, Wesley J. Maddox, Nate Gruver, Phillip Maffettone, Emily Delaney, Peyton Greenside, Andrew Gordon Wilson
Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders. CoRR (2022)
Sanyam Kapoor, Wesley J. Maddox, Pavel Izmailov, Andrew Gordon Wilson
On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification. CoRR (2022)
Pavel Izmailov, Sharad Vikram, Matthew D. Hoffman, Andrew Gordon Wilson
What Are Bayesian Neural Network Posteriors Really Like? CoRR (2021)
Pavel Izmailov, Patrick Nicholson, Sanae Lotfi, Andrew Gordon Wilson
Dangers of Bayesian Model Averaging under Covariate Shift. NeurIPS (2021)
Shengyang Sun, Jiaxin Shi, Andrew Gordon Wilson, Roger B. Grosse
Scalable Variational Gaussian Processes via Harmonic Kernel Decomposition. ICML (2021)
Samuel Stanton, Wesley Maddox, Ian A. Delbridge, Andrew Gordon Wilson
Kernel Interpolation for Scalable Online Gaussian Processes. AISTATS (2021)
Wesley J. Maddox, Maximilian Balandat, Andrew Gordon Wilson, Eytan Bakshy
Bayesian Optimization with High-Dimensional Outputs. NeurIPS (2021)
Wesley J. Maddox, Maximilian Balandat, Andrew Gordon Wilson, Eytan Bakshy
Bayesian Optimization with High-Dimensional Outputs. CoRR (2021)
Sanyam Kapoor, Marc Finzi, Ke Alexander Wang, Andrew Gordon Wilson
SKIing on Simplices: Kernel Interpolation on the Permutohedral Lattice for Scalable Gaussian Processes. ICML (2021)
Marc Finzi, Max Welling, Andrew Gordon Wilson
A Practical Method for Constructing Equivariant Multilayer Perceptrons for Arbitrary Matrix Groups. CoRR (2021)
Wesley J. Maddox, Shuai Tang, Pablo Garcia Moreno, Andrew Gordon Wilson, Andreas C. Damianou
Fast Adaptation with Linearized Neural Networks. CoRR (2021)
Wesley Maddox, Shuai Tang, Pablo Garcia Moreno, Andrew Gordon Wilson, Andreas C. Damianou
Fast Adaptation with Linearized Neural Networks. AISTATS (2021)
Wesley J. Maddox, Samuel Stanton, Andrew Gordon Wilson
Conditioning Sparse Variational Gaussian Processes for Online Decision-making. CoRR (2021)
Marc Finzi, Gregory W. Benton, Andrew Gordon Wilson
Residual Pathway Priors for Soft Equivariance Constraints. CoRR (2021)
Samuel Stanton, Pavel Izmailov, Polina Kirichenko, Alexander A. Alemi, Andrew Gordon Wilson
Does Knowledge Distillation Really Work? CoRR (2021)
Wesley J. Maddox, Samuel Stanton, Andrew Gordon Wilson
Conditioning Sparse Variational Gaussian Processes for Online Decision-making. NeurIPS (2021)

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Publication Activity (10 Years)

Top Topics

Top Venues

Andrew Gordon Wilson