Andrew Gordon Wilson

Affiliations:
  • New York University, New York, NY, USA
  • Cornell University, Ithaca, NY, USA (former)
  • Carnegie Mellon University, Machine Learning Department, Pittsburgh, PA, USA (former)
  • University of Cambridge, Department of Engineering, UK (former)


According to our database1, Andrew Gordon Wilson authored at least 120 papers between 2010 and 2024.

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Bibliography

2024
Fine-Tuned Language Models Generate Stable Inorganic Materials as Text.
CoRR, 2024

Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI.
CoRR, 2024

2023
Non-Vacuous Generalization Bounds for Large Language Models.
CoRR, 2023

Perspectives on the State and Future of Deep Learning - 2023.
CoRR, 2023

Materials Expert-Artificial Intelligence for Materials Discovery.
CoRR, 2023

A Study of Bayesian Neural Network Surrogates for Bayesian Optimization.
CoRR, 2023

A Cookbook of Self-Supervised Learning.
CoRR, 2023

The No Free Lunch Theorem, Kolmogorov Complexity, and the Role of Inductive Biases in Machine Learning.
CoRR, 2023

Fortuna: A Library for Uncertainty Quantification in Deep Learning.
CoRR, 2023

Visual Explanations of Image-Text Representations via Multi-Modal Information Bottleneck Attribution.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Simplifying Neural Network Training Under Class Imbalance.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Should We Learn Most Likely Functions or Parameters?
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

CoLA: Exploiting Compositional Structure for Automatic and Efficient Numerical Linear Algebra.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Understanding the detrimental class-level effects of data augmentation.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Protein Design with Guided Discrete Diffusion.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Large Language Models Are Zero-Shot Time Series Forecasters.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Battle of the Backbones: A Large-Scale Comparison of Pretrained Models across Computer Vision Tasks.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

A Performance-Driven Benchmark for Feature Selection in Tabular Deep Learning.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Function-Space Regularization in Neural Networks: A Probabilistic Perspective.
Proceedings of the International Conference on Machine Learning, 2023

Simple and Fast Group Robustness by Automatic Feature Reweighting.
Proceedings of the International Conference on Machine Learning, 2023

User-defined Event Sampling and Uncertainty Quantification in Diffusion Models for Physical Dynamical Systems.
Proceedings of the International Conference on Machine Learning, 2023

Learning Multimodal Data Augmentation in Feature Space.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Transfer Learning with Deep Tabular Models.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Last Layer Re-Training is Sufficient for Robustness to Spurious Correlations.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

The Lie Derivative for Measuring Learned Equivariance.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

How Much Data Are Augmentations Worth? An Investigation into Scaling Laws, Invariance, and Implicit Regularization.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

A Stable and Scalable Method for Solving Initial Value PDEs with Neural Networks.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Automated Few-Shot Classification with Instruction-Finetuned Language Models.
Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2023, 2023

Bayesian Optimization with Conformal Prediction Sets.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022
What do Vision Transformers Learn? A Visual Exploration.
CoRR, 2022

K-SAM: Sharpness-Aware Minimization at the Speed of SGD.
CoRR, 2022

Bayesian Optimization with Conformal Coverage Guarantees.
CoRR, 2022

Low-precision arithmetic for fast Gaussian processes.
Proceedings of the Uncertainty in Artificial Intelligence, 2022

Chroma-VAE: Mitigating Shortcut Learning with Generative Classifiers.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Pre-Train Your Loss: Easy Bayesian Transfer Learning with Informative Priors.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

PAC-Bayes Compression Bounds So Tight That They Can Explain Generalization.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

On Feature Learning in the Presence of Spurious Correlations.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Low-Precision Stochastic Gradient Langevin Dynamics.
Proceedings of the International Conference on Machine Learning, 2022

Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders.
Proceedings of the International Conference on Machine Learning, 2022

Bayesian Model Selection, the Marginal Likelihood, and Generalization.
Proceedings of the International Conference on Machine Learning, 2022

Volatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian Processes.
Proceedings of the International Conference on Machine Learning, 2022

Deconstructing the Inductive Biases of Hamiltonian Neural Networks.
Proceedings of the Tenth International Conference on Learning Representations, 2022

2021
When are Iterative Gaussian Processes Reliably Accurate?
CoRR, 2021

Task-agnostic Continual Learning with Hybrid Probabilistic Models.
CoRR, 2021

Evaluating Approximate Inference in Bayesian Deep Learning.
Proceedings of the NeurIPS 2021 Competitions and Demonstrations Track, 2021

Does Knowledge Distillation Really Work?
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Conditioning Sparse Variational Gaussian Processes for Online Decision-making.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Bayesian Optimization with High-Dimensional Outputs.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Dangers of Bayesian Model Averaging under Covariate Shift.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Residual Pathway Priors for Soft Equivariance Constraints.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

On the model-based stochastic value gradient for continuous reinforcement learning.
Proceedings of the 3rd Annual Conference on Learning for Dynamics and Control, 2021

Scalable Variational Gaussian Processes via Harmonic Kernel Decomposition.
Proceedings of the 38th International Conference on Machine Learning, 2021

SKIing on Simplices: Kernel Interpolation on the Permutohedral Lattice for Scalable Gaussian Processes.
Proceedings of the 38th International Conference on Machine Learning, 2021

What Are Bayesian Neural Network Posteriors Really Like?
Proceedings of the 38th International Conference on Machine Learning, 2021

A Practical Method for Constructing Equivariant Multilayer Perceptrons for Arbitrary Matrix Groups.
Proceedings of the 38th International Conference on Machine Learning, 2021

Loss Surface Simplexes for Mode Connecting Volumes and Fast Ensembling.
Proceedings of the 38th International Conference on Machine Learning, 2021

Kernel Interpolation for Scalable Online Gaussian Processes.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

Fast Adaptation with Linearized Neural Networks.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020
Learning Invariances in Neural Networks.
CoRR, 2020

Rethinking Parameter Counting in Deep Models: Effective Dimensionality Revisited.
CoRR, 2020

The Case for Bayesian Deep Learning.
CoRR, 2020

Improving GAN Training with Probability Ratio Clipping and Sample Reweighting.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Bayesian Deep Learning and a Probabilistic Perspective of Generalization.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Why Normalizing Flows Fail to Detect Out-of-Distribution Data.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Simplifying Hamiltonian and Lagrangian Neural Networks via Explicit Constraints.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Learning Invariances in Neural Networks from Training Data.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Semi-Supervised Learning with Normalizing Flows.
Proceedings of the 37th International Conference on Machine Learning, 2020

Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data.
Proceedings of the 37th International Conference on Machine Learning, 2020

Randomly Projected Additive Gaussian Processes for Regression.
Proceedings of the 37th International Conference on Machine Learning, 2020

Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning.
Proceedings of the 8th International Conference on Learning Representations, 2020

2019
Change Surfaces for Expressive Multidimensional Changepoints and Counterfactual Prediction.
J. Mach. Learn. Res., 2019

BoTorch: Programmable Bayesian Optimization in PyTorch.
CoRR, 2019

SysML: The New Frontier of Machine Learning Systems.
CoRR, 2019

Practical Multi-fidelity Bayesian Optimization for Hyperparameter Tuning.
Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

Subspace Inference for Bayesian Deep Learning.
Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

Exact Gaussian Processes on a Million Data Points.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

A Simple Baseline for Bayesian Uncertainty in Deep Learning.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Function-Space Distributions over Kernels.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

SWALP : Stochastic Weight Averaging in Low Precision Training.
Proceedings of the 36th International Conference on Machine Learning, 2019

Simple Black-box Adversarial Attacks.
Proceedings of the 36th International Conference on Machine Learning, 2019

There Are Many Consistent Explanations of Unlabeled Data: Why You Should Average.
Proceedings of the 7th International Conference on Learning Representations, 2019

2018
Improving Consistency-Based Semi-Supervised Learning with Weight Averaging.
CoRR, 2018

Averaging Weights Leads to Wider Optima and Better Generalization.
Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, 2018

Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Scaling Gaussian Process Regression with Derivatives.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Automated Local Regression Discontinuity Design Discovery.
Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018

Constant-Time Predictive Distributions for Gaussian Processes.
Proceedings of the 35th International Conference on Machine Learning, 2018

Hierarchical Density Order Embeddings.
Proceedings of the 6th International Conference on Learning Representations, 2018

Gaussian Process Subset Scanning for Anomalous Pattern Detection in Non-iid Data.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

Product Kernel Interpolation for Scalable Gaussian Processes.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

Probabilistic FastText for Multi-Sense Word Embeddings.
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, 2018

2017
Learning Scalable Deep Kernels with Recurrent Structure.
J. Mach. Learn. Res., 2017

Bayesian Optimization with Gradients.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Bayesian GAN.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Scalable Levy Process Priors for Spectral Kernel Learning.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Scalable Log Determinants for Gaussian Process Kernel Learning.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Efficient Gaussian Process-Based Inference for Modelling Spatio-Temporal Dengue Fever.
Proceedings of the 2017 Brazilian Conference on Intelligent Systems, 2017

Multimodal Word Distributions.
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, 2017

2016
Stochastic Variational Deep Kernel Learning.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Deep Kernel Learning.
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

Bayesian Nonparametric Kernel-Learning.
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

Scalable Gaussian Processes for Characterizing Multidimensional Change Surfaces.
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015
Thoughts on Massively Scalable Gaussian Processes.
CoRR, 2015

The Human Kernel.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Kernel Interpolation for Scalable Structured Gaussian Processes (KISS-GP).
Proceedings of the 32nd International Conference on Machine Learning, 2015

Fast Kronecker Inference in Gaussian Processes with non-Gaussian Likelihoods.
Proceedings of the 32nd International Conference on Machine Learning, 2015

A la Carte - Learning Fast Kernels.
Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

2014
Fast Kernel Learning for Multidimensional Pattern Extrapolation.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

A Bayesian method to quantifying chemical composition using NMR: Application to porous media systems.
Proceedings of the 22nd European Signal Processing Conference, 2014

Student-t Processes as Alternatives to Gaussian Processes.
Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014

2013
Gaussian Process Covariance Kernels for Pattern Discovery and Extrapolation
CoRR, 2013

GPatt: Fast Multidimensional Pattern Extrapolation with Gaussian Processes.
CoRR, 2013

Gaussian Process Kernels for Pattern Discovery and Extrapolation.
Proceedings of the 30th International Conference on Machine Learning, 2013

2012
Modelling Input Varying Correlations between Multiple Responses.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2012

Gaussian Process Regression Networks.
Proceedings of the 29th International Conference on Machine Learning, 2012

2011
Generalised Wishart Processes.
Proceedings of the UAI 2011, 2011

2010
Copula Processes.
Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010


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