Andrew Gordon Wilson

Qi Lei

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI.

[BibT_eX]

[DOI]

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Non-Vacuous Generalization Bounds for Large Language Models.

[BibT_eX]

[DOI]

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Modeling Caption Diversity in Contrastive Vision-Language Pretraining.

[BibT_eX]

[DOI]

Proceedings of the Forty-first International Conference on Machine Learning, 2024

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

[BibT_eX]

[DOI]

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Scalable and Flexible Causal Discovery with an Efficient Test for Adjacency.

[BibT_eX]

[DOI]

Alan Nawzad Amin

Proceedings of the Forty-first International Conference on Machine Learning, 2024

A Study of Bayesian Neural Network Surrogates for Bayesian Optimization.

[BibT_eX]

[DOI]

Yucen Lily Li

Tim G. J. Rudner

Proceedings of the Twelfth International Conference on Learning Representations, 2024

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

[BibT_eX]

[DOI]

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Mind the GAP: Improving Robustness to Subpopulation Shifts with Group-Aware Priors.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

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

[BibT_eX]

[DOI]

CoRR, 2023

Materials Expert-Artificial Intelligence for Materials Discovery.

[BibT_eX]

[DOI]

CoRR, 2023

A Cookbook of Self-Supervised Learning.

[BibT_eX]

[DOI]

CoRR, 2023

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

[BibT_eX]

[DOI]

CoRR, 2023

Visual Explanations of Image-Text Representations via Multi-Modal Information Bottleneck Attribution.

[BibT_eX]

[DOI]

Ying Wang

Tim G. J. Rudner

Shanmukha Ramakrishna Vedantam

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.

[BibT_eX]

[DOI]

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?

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

Hamed Firooz

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.

[BibT_eX]

[DOI]

Julien Lafrance-Vanasse

Arvind Rajpal

Kyunghyun Cho

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.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

Prithvijit Chattopadhyay

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.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2023

Simple and Fast Group Robustness by Automatic Feature Reweighting.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2023

User-defined Event Sampling and Uncertainty Quantification in Diffusion Models for Physical Dynamical Systems.

[BibT_eX]

[DOI]

Leonardo Zepeda-Núñez

Proceedings of the International Conference on Machine Learning, 2023

Learning Multimodal Data Augmentation in Feature Space.

[BibT_eX]

[DOI]

Anshumali Shrivastava

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Transfer Learning with Deep Tabular Models.

[BibT_eX]

[DOI]

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Last Layer Re-Training is Sufficient for Robustness to Spurious Correlations.

[BibT_eX]

[DOI]

Polina Kirichenko

Pavel Izmailov

Proceedings of the Eleventh International Conference on Learning Representations, 2023

The Lie Derivative for Measuring Learned Equivariance.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

Proceedings of the Eleventh International Conference on Learning Representations, 2023

A Stable and Scalable Method for Solving Initial Value PDEs with Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Automated Few-Shot Classification with Instruction-Finetuned Language Models.

[BibT_eX]

[DOI]

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2023, 2023

Bayesian Optimization with Conformal Prediction Sets.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

What do Vision Transformers Learn? A Visual Exploration.

[BibT_eX]

[DOI]

CoRR, 2022

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

[BibT_eX]

[DOI]

CoRR, 2022

Bayesian Optimization with Conformal Coverage Guarantees.

[BibT_eX]

[DOI]

CoRR, 2022

Low-precision arithmetic for fast Gaussian processes.

[BibT_eX]

[DOI]

Andres Potapczynski

Proceedings of the Uncertainty in Artificial Intelligence, 2022

Chroma-VAE: Mitigating Shortcut Learning with Generative Classifiers.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

Ruqi Zhang

Christopher De Sa

Proceedings of the International Conference on Machine Learning, 2022

Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders.

[BibT_eX]

[DOI]

Nate Gruver

Phillip M. Maffettone

Emily Delaney

Peyton Greenside

Proceedings of the International Conference on Machine Learning, 2022

Bayesian Model Selection, the Marginal Likelihood, and Generalization.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2022

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

[BibT_eX]

[DOI]

Gregory W. Benton

Proceedings of the International Conference on Machine Learning, 2022

Deconstructing the Inductive Biases of Hamiltonian Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

When are Iterative Gaussian Processes Reliably Accurate?

[BibT_eX]

[DOI]

Sanyam Kapoor

CoRR, 2021

Task-agnostic Continual Learning with Hybrid Probabilistic Models.

[BibT_eX]

[DOI]

Polina Kirichenko

Mehrdad Farajtabar

Dushyant Rao

Balaji Lakshminarayanan

CoRR, 2021

Evaluating Approximate Inference in Bayesian Deep Learning.

[BibT_eX]

[DOI]

Proceedings of the NeurIPS 2021 Competitions and Demonstrations Track, 2021

Does Knowledge Distillation Really Work?

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

Marc Finzi

Greg Benton

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.

[BibT_eX]

[DOI]

Proceedings of the 3rd Annual Conference on Learning for Dynamics and Control, 2021

Scalable Variational Gaussian Processes via Harmonic Kernel Decomposition.

[BibT_eX]

[DOI]

Proceedings of the 38th International Conference on Machine Learning, 2021

SKIing on Simplices: Kernel Interpolation on the Permutohedral Lattice for Scalable Gaussian Processes.

[BibT_eX]

[DOI]

Proceedings of the 38th International Conference on Machine Learning, 2021

What Are Bayesian Neural Network Posteriors Really Like?

[BibT_eX]

[DOI]

Proceedings of the 38th International Conference on Machine Learning, 2021

A Practical Method for Constructing Equivariant Multilayer Perceptrons for Arbitrary Matrix Groups.

[BibT_eX]

[DOI]

Marc Finzi

Max Welling

Proceedings of the 38th International Conference on Machine Learning, 2021

Loss Surface Simplexes for Mode Connecting Volumes and Fast Ensembling.

[BibT_eX]

[DOI]

Proceedings of the 38th International Conference on Machine Learning, 2021

Kernel Interpolation for Scalable Online Gaussian Processes.

[BibT_eX]

[DOI]

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

Fast Adaptation with Linearized Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Learning Invariances in Neural Networks.

[BibT_eX]

[DOI]

CoRR, 2020

Rethinking Parameter Counting in Deep Models: Effective Dimensionality Revisited.

[BibT_eX]

[DOI]

Gregory W. Benton

CoRR, 2020

The Case for Bayesian Deep Learning.

[BibT_eX]

[DOI]

CoRR, 2020

Improving GAN Training with Probability Ratio Clipping and Sample Reweighting.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

Pavel Izmailov

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.

[BibT_eX]

[DOI]

Polina Kirichenko

Pavel Izmailov

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.

[BibT_eX]

[DOI]

Marc Finzi

Ke Alexander Wang

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.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

Proceedings of the 37th International Conference on Machine Learning, 2020

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

[BibT_eX]

[DOI]

Proceedings of the 37th International Conference on Machine Learning, 2020

Randomly Projected Additive Gaussian Processes for Regression.

[BibT_eX]

[DOI]

Ian A. Delbridge

David Bindel

Dimitris S. Papailiopoulos

Proceedings of the 37th International Conference on Machine Learning, 2020

Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning.

[BibT_eX]

[DOI]

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

Change Surfaces for Expressive Multidimensional Changepoints and Counterfactual Prediction.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2019

BoTorch: Programmable Bayesian Optimization in PyTorch.

[BibT_eX]

[DOI]

CoRR, 2019

SysML: The New Frontier of Machine Learning Systems.

[BibT_eX]

[DOI]

Alexandros G. Dimakis

Anastasios Kyrillidis

Shivaram Venkataraman

CoRR, 2019

Practical Multi-fidelity Bayesian Optimization for Hyperparameter Tuning.

[BibT_eX]

[DOI]

Jian Wu

Saul Toscano-Palmerin

Peter I. Frazier

Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

Subspace Inference for Bayesian Deep Learning.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

Exact Gaussian Processes on a Million Data Points.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Function-Space Distributions over Kernels.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

Proceedings of the 36th International Conference on Machine Learning, 2019

Simple Black-box Adversarial Attacks.

[BibT_eX]

[DOI]

Proceedings of the 36th International Conference on Machine Learning, 2019

There Are Many Consistent Explanations of Unlabeled Data: Why You Should Average.

[BibT_eX]

[DOI]

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

Improving Consistency-Based Semi-Supervised Learning with Weight Averaging.

[BibT_eX]

[DOI]

CoRR, 2018

Averaging Weights Leads to Wider Optima and Better Generalization.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, 2018

Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018

Constant-Time Predictive Distributions for Gaussian Processes.

[BibT_eX]

[DOI]

Proceedings of the 35th International Conference on Machine Learning, 2018

Hierarchical Density Order Embeddings.

[BibT_eX]

[DOI]

Ben Athiwaratkun

Proceedings of the 6th International Conference on Learning Representations, 2018

Gaussian Process Subset Scanning for Anomalous Pattern Detection in Non-iid Data.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

Product Kernel Interpolation for Scalable Gaussian Processes.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

Probabilistic FastText for Multi-Sense Word Embeddings.

[BibT_eX]

[DOI]

Ben Athiwaratkun

Anima Anandkumar

Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, 2018

2017

Learning Scalable Deep Kernels with Recurrent Structure.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2017

Bayesian Optimization with Gradients.

[BibT_eX]

[DOI]

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

Bayesian GAN.

[BibT_eX]

[DOI]

Yunus Saatci

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.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

Proceedings of the 2017 Brazilian Conference on Intelligent Systems, 2017

Multimodal Word Distributions.

[BibT_eX]

[DOI]

Ben Athiwaratkun

Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, 2017

2016

Stochastic Variational Deep Kernel Learning.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Deep Kernel Learning.

[BibT_eX]

[DOI]

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

Bayesian Nonparametric Kernel-Learning.

[BibT_eX]

[DOI]

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

Scalable Gaussian Processes for Characterizing Multidimensional Change Surfaces.

[BibT_eX]

[DOI]

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015

Thoughts on Massively Scalable Gaussian Processes.

[BibT_eX]

[DOI]

Christoph Dann

Hannes Nickisch

CoRR, 2015

The Human Kernel.

[BibT_eX]

[DOI]

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).

[BibT_eX]

[DOI]

Hannes Nickisch

Proceedings of the 32nd International Conference on Machine Learning, 2015

Fast Kronecker Inference in Gaussian Processes with non-Gaussian Likelihoods.

[BibT_eX]

[DOI]

Proceedings of the 32nd International Conference on Machine Learning, 2015

A la Carte - Learning Fast Kernels.

[BibT_eX]

[DOI]

Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

2014

Fast Kernel Learning for Multidimensional Pattern Extrapolation.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

Proceedings of the 22nd European Signal Processing Conference, 2014

Student-t Processes as Alternatives to Gaussian Processes.

[BibT_eX]

[DOI]

Amar Shah

Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014

2013

Gaussian Process Covariance Kernels for Pattern Discovery and Extrapolation

[BibT_eX]

[DOI]

Ryan Prescott Adams

CoRR, 2013

GPatt: Fast Multidimensional Pattern Extrapolation with Gaussian Processes.

[BibT_eX]

[DOI]

CoRR, 2013

Gaussian Process Kernels for Pattern Discovery and Extrapolation.

[BibT_eX]

[DOI]

Ryan Prescott Adams

Proceedings of the 30th International Conference on Machine Learning, 2013

2012

Modelling Input Varying Correlations between Multiple Responses.

[BibT_eX]

[DOI]

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2012

Gaussian Process Regression Networks.

[BibT_eX]

[DOI]

David A. Knowles

Proceedings of the 29th International Conference on Machine Learning, 2012

2011

Generalised Wishart Processes.

[BibT_eX]

[DOI]

Proceedings of the UAI 2011, 2011

2010

Copula Processes.

[BibT_eX]

[DOI]