Copula Processes. - Andrew Gordon Wilson; Zoubin Ghahramani (NIPS, 2010)

Generalised Wishart Processes. - Andrew Gordon Wilson; Zoubin Ghahramani (UAI, 2011)

Gaussian Process Regression Networks. - Andrew Gordon Wilson; David A. Knowles; Zoubin Ghahramani (ICML, 2012)

Modelling Input Varying Correlations between Multiple Responses. - Andrew Gordon Wilson; Zoubin Ghahramani (ECML/PKDD (2), 2012)

Gaussian Process Kernels for Pattern Discovery and Extrapolation. - Andrew Gordon Wilson; Ryan Prescott Adams (ICML (3), 2013)

GPatt: Fast Multidimensional Pattern Extrapolation with Gaussian Processes. - Andrew Gordon Wilson; Elad Gilboa; Arye Nehorai; John P. Cunningham (CoRR, 2013)

Gaussian Process Covariance Kernels for Pattern Discovery and Extrapolation - Andrew Gordon Wilson; Ryan Prescott Adams (CoRR, 2013)

Student-t Processes as Alternatives to Gaussian Processes. - Amar Shah 001; Andrew Gordon Wilson; Zoubin Ghahramani (AISTATS, 2014)

A Bayesian method to quantifying chemical composition using NMR: Application to porous media systems. - Yuting Wu; Daniel J. Holland; Mick D. Mantle; Andrew Gordon Wilson; Sebastian Nowozin; Andrew Blake 001; Lynn F. Gladden (EUSIPCO, 2014)

Fast Kernel Learning for Multidimensional Pattern Extrapolation. - Andrew Gordon Wilson; Elad Gilboa; John P. Cunningham; Arye Nehorai (NIPS, 2014)

A la Carte - Learning Fast Kernels. - Zichao Yang; Andrew Gordon Wilson; Alexander J. Smola; Le Song (AISTATS, 2015)

Fast Kronecker Inference in Gaussian Processes with non-Gaussian Likelihoods. - Seth R. Flaxman; Andrew Gordon Wilson; Daniel B. Neill; Hannes Nickisch; Alexander J. Smola (ICML, 2015)

Kernel Interpolation for Scalable Structured Gaussian Processes (KISS-GP). - Andrew Gordon Wilson; Hannes Nickisch (ICML, 2015)

The Human Kernel. - Andrew Gordon Wilson; Christoph Dann; Christopher G. Lucas; Eric P. Xing (NIPS, 2015)

Thoughts on Massively Scalable Gaussian Processes. - Andrew Gordon Wilson; Christoph Dann; Hannes Nickisch (CoRR, 2015)

Scalable Gaussian Processes for Characterizing Multidimensional Change Surfaces. - William Herlands; Andrew Gordon Wilson; Hannes Nickisch; Seth R. Flaxman; Daniel B. Neill; Wilbert Van Panhuis; Eric P. Xing (AISTATS, 2016)

Bayesian Nonparametric Kernel-Learning. - Junier B. Oliva; Avinava Dubey; Andrew Gordon Wilson; Barnabás Póczos; Jeff G. Schneider; Eric P. Xing (AISTATS, 2016)

Deep Kernel Learning. - Andrew Gordon Wilson; Zhiting Hu; Ruslan Salakhutdinov; Eric P. Xing (AISTATS, 2016)

Stochastic Variational Deep Kernel Learning. - Andrew Gordon Wilson; Zhiting Hu; Ruslan Salakhutdinov; Eric P. Xing (NIPS, 2016)

Multimodal Word Distributions. - Ben Athiwaratkun; Andrew Gordon Wilson (ACL (1), 2017)

Efficient Gaussian Process-Based Inference for Modelling Spatio-Temporal Dengue Fever. - Julio Albinati; Wagner Meira Jr.; Gisele L. Pappa; Andrew Gordon Wilson (BRACIS, 2017)

Scalable Log Determinants for Gaussian Process Kernel Learning. - Kun Dong; David Eriksson; Hannes Nickisch; David Bindel; Andrew Gordon Wilson (NIPS, 2017)

Scalable Levy Process Priors for Spectral Kernel Learning. - Phillip A. Jang; Andrew E. Loeb; Matthew B. Davidow; Andrew Gordon Wilson (NIPS, 2017)

Bayesian GAN. - Yunus Saatci; Andrew Gordon Wilson (NIPS, 2017)

Bayesian Optimization with Gradients. - Jian Wu; Matthias Poloczek; Andrew Gordon Wilson; Peter I. Frazier (NIPS, 2017)

Learning Scalable Deep Kernels with Recurrent Structure. - Maruan Al-Shedivat; Andrew Gordon Wilson; Yunus Saatchi; Zhiting Hu; Eric P. Xing (J. Mach. Learn. Res., 2017)

Probabilistic FastText for Multi-Sense Word Embeddings. - Ben Athiwaratkun; Andrew Gordon Wilson; Anima Anandkumar (ACL (1), 2018)

Product Kernel Interpolation for Scalable Gaussian Processes. - Jacob R. Gardner; Geoff Pleiss; Ruihan Wu; Kilian Q. Weinberger; Andrew Gordon Wilson (AISTATS, 2018)

Gaussian Process Subset Scanning for Anomalous Pattern Detection in Non-iid Data. - William Herlands; Edward McFowland; Andrew Gordon Wilson; Daniel B. Neill (AISTATS, 2018)

Hierarchical Density Order Embeddings. - Ben Athiwaratkun; Andrew Gordon Wilson (ICLR (Poster), 2018)

Constant-Time Predictive Distributions for Gaussian Processes. - Geoff Pleiss; Jacob R. Gardner; Kilian Q. Weinberger; Andrew Gordon Wilson (ICML, 2018)

Automated Local Regression Discontinuity Design Discovery. - William Herlands; Edward McFowland III; Andrew Gordon Wilson; Daniel B. Neill (KDD, 2018)

Scaling Gaussian Process Regression with Derivatives. - David Eriksson; Kun Dong; Eric Hans Lee; David Bindel; Andrew Gordon Wilson (NeurIPS, 2018)

GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration. - Jacob R. Gardner; Geoff Pleiss; Kilian Q. Weinberger; David Bindel; Andrew Gordon Wilson (NeurIPS, 2018)

Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs. - Timur Garipov; Pavel Izmailov; Dmitrii Podoprikhin; Dmitry P. Vetrov; Andrew Gordon Wilson (NeurIPS, 2018)

Averaging Weights Leads to Wider Optima and Better Generalization. - Pavel Izmailov; Dmitrii Podoprikhin; Timur Garipov; Dmitry P. Vetrov; Andrew Gordon Wilson (UAI, 2018)

Improving Consistency-Based Semi-Supervised Learning with Weight Averaging. - Ben Athiwaratkun; Marc Finzi; Pavel Izmailov; Andrew Gordon Wilson (CoRR, 2018)

There Are Many Consistent Explanations of Unlabeled Data: Why You Should Average. - Ben Athiwaratkun; Marc Finzi; Pavel Izmailov; Andrew Gordon Wilson (ICLR (Poster), 2019)

Simple Black-box Adversarial Attacks. - Chuan Guo 001; Jacob R. Gardner; Yurong You; Andrew Gordon Wilson; Kilian Q. Weinberger (ICML, 2019)

SWALP : Stochastic Weight Averaging in Low Precision Training. - Guandao Yang; Tianyi Zhang; Polina Kirichenko; Junwen Bai; Andrew Gordon Wilson; Christopher De Sa (ICML, 2019)

Function-Space Distributions over Kernels. - Gregory W. Benton; Wesley J. Maddox; Jayson P. Salkey; Julio Albinati; Andrew Gordon Wilson (NeurIPS, 2019)

A Simple Baseline for Bayesian Uncertainty in Deep Learning. - Wesley J. Maddox; Pavel Izmailov; Timur Garipov; Dmitry P. Vetrov; Andrew Gordon Wilson (NeurIPS, 2019)

Exact Gaussian Processes on a Million Data Points. - Ke Alexander Wang; Geoff Pleiss; Jacob R. Gardner; Stephen Tyree; Kilian Q. Weinberger; Andrew Gordon Wilson (NeurIPS, 2019)

Subspace Inference for Bayesian Deep Learning. - Pavel Izmailov; Wesley J. Maddox; Polina Kirichenko; Timur Garipov; Dmitry P. Vetrov; Andrew Gordon Wilson (UAI, 2019)

Practical Multi-fidelity Bayesian Optimization for Hyperparameter Tuning. - Jian Wu; Saul Toscano-Palmerin; Peter I. Frazier; Andrew Gordon Wilson (UAI, 2019)

SysML: The New Frontier of Machine Learning Systems. - Alexander Ratner; Dan Alistarh; Gustavo Alonso; David G. Andersen; Peter Bailis; Sarah Bird; Nicholas Carlini; Bryan Catanzaro; Eric S. Chung; Bill Dally; Jeff Dean; Inderjit S. Dhillon; et al. (CoRR, 2019)

BoTorch: Programmable Bayesian Optimization in PyTorch. - Maximilian Balandat; Brian Karrer; Daniel R. Jiang; Samuel Daulton; Benjamin Letham; Andrew Gordon Wilson; Eytan Bakshy (CoRR, 2019)

Change Surfaces for Expressive Multidimensional Changepoints and Counterfactual Prediction. - William Herlands; Daniel B. Neill; Hannes Nickisch; Andrew Gordon Wilson (J. Mach. Learn. Res., 2019)

Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning. - Ruqi Zhang; Chunyuan Li; Jianyi Zhang; Changyou Chen; Andrew Gordon Wilson (ICLR, 2020)

Randomly Projected Additive Gaussian Processes for Regression. - Ian A. Delbridge; David Bindel; Andrew Gordon Wilson (ICML, 2020)

Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data. - Marc Finzi; Samuel Stanton; Pavel Izmailov; Andrew Gordon Wilson (ICML, 2020)

Semi-Supervised Learning with Normalizing Flows. - Pavel Izmailov; Polina Kirichenko; Marc Finzi; Andrew Gordon Wilson (ICML, 2020)

BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization. - Maximilian Balandat; Brian Karrer; Daniel R. Jiang; Samuel Daulton; Benjamin Letham; Andrew Gordon Wilson; Eytan Bakshy (NeurIPS, 2020)

Learning Invariances in Neural Networks from Training Data. - Gregory W. Benton; Marc Finzi; Pavel Izmailov; Andrew Gordon Wilson (NeurIPS, 2020)

Simplifying Hamiltonian and Lagrangian Neural Networks via Explicit Constraints. - Marc Finzi; Ke Alexander Wang; Andrew Gordon Wilson (NeurIPS, 2020)

Why Normalizing Flows Fail to Detect Out-of-Distribution Data. - Polina Kirichenko; Pavel Izmailov; Andrew Gordon Wilson (NeurIPS, 2020)

Bayesian Deep Learning and a Probabilistic Perspective of Generalization. - Andrew Gordon Wilson; Pavel Izmailov (NeurIPS, 2020)

Improving GAN Training with Probability Ratio Clipping and Sample Reweighting. - Yue Wu; Pan Zhou 002; Andrew Gordon Wilson; Eric P. Xing; Zhiting Hu (NeurIPS, 2020)

The Case for Bayesian Deep Learning. - Andrew Gordon Wilson (CoRR, 2020)

Rethinking Parameter Counting in Deep Models: Effective Dimensionality Revisited. - Wesley J. Maddox; Gregory W. Benton; Andrew Gordon Wilson (CoRR, 2020)

Learning Invariances in Neural Networks. - Gregory W. Benton; Marc Finzi; Pavel Izmailov; Andrew Gordon Wilson (CoRR, 2020)

Fast Adaptation with Linearized Neural Networks. - Wesley J. Maddox; Shuai Tang; Pablo Garcia Moreno; Andrew Gordon Wilson; Andreas C. Damianou (AISTATS, 2021)

Kernel Interpolation for Scalable Online Gaussian Processes. - Samuel Stanton; Wesley J. Maddox; Ian A. Delbridge; Andrew Gordon Wilson (AISTATS, 2021)

Loss Surface Simplexes for Mode Connecting Volumes and Fast Ensembling. - Gregory W. Benton; Wesley J. Maddox; Sanae Lotfi; Andrew Gordon Wilson (ICML, 2021)

A Practical Method for Constructing Equivariant Multilayer Perceptrons for Arbitrary Matrix Groups. - Marc Finzi; Max Welling; Andrew Gordon Wilson (ICML, 2021)

What Are Bayesian Neural Network Posteriors Really Like? - Pavel Izmailov; Sharad Vikram; Matthew D. Hoffman; Andrew Gordon Wilson (ICML, 2021)

SKIing on Simplices: Kernel Interpolation on the Permutohedral Lattice for Scalable Gaussian Processes. - Sanyam Kapoor; Marc Finzi; Ke Alexander Wang; Andrew Gordon Wilson (ICML, 2021)

Scalable Variational Gaussian Processes via Harmonic Kernel Decomposition. - Shengyang Sun; Jiaxin Shi; Andrew Gordon Wilson; Roger B. Grosse (ICML, 2021)

On the model-based stochastic value gradient for continuous reinforcement learning. - Brandon Amos; Samuel Stanton; Denis Yarats; Andrew Gordon Wilson (L4DC, 2021)

Residual Pathway Priors for Soft Equivariance Constraints. - Marc Finzi; Greg Benton; Andrew Gordon Wilson (NeurIPS, 2021)

Dangers of Bayesian Model Averaging under Covariate Shift. - Pavel Izmailov; Patrick Nicholson; Sanae Lotfi; Andrew Gordon Wilson (NeurIPS, 2021)

Bayesian Optimization with High-Dimensional Outputs. - Wesley J. Maddox; Maximilian Balandat; Andrew Gordon Wilson; Eytan Bakshy (NeurIPS, 2021)

Conditioning Sparse Variational Gaussian Processes for Online Decision-making. - Wesley J. Maddox; Samuel Stanton; Andrew Gordon Wilson (NeurIPS, 2021)

Does Knowledge Distillation Really Work? - Samuel Stanton; Pavel Izmailov; Polina Kirichenko; Alexander A. Alemi; Andrew Gordon Wilson (NeurIPS, 2021)

Evaluating Approximate Inference in Bayesian Deep Learning. - Andrew Gordon Wilson; Pavel Izmailov; Matthew D. Hoffman; Yarin Gal; Yingzhen Li; Melanie F. Pradier; Sharad Vikram; Andrew Y. K. Foong; Sanae Lotfi; Sebastian Farquhar (NeurIPS (Competition and Demos), 2021)

Task-agnostic Continual Learning with Hybrid Probabilistic Models. - Polina Kirichenko; Mehrdad Farajtabar; Dushyant Rao; Balaji Lakshminarayanan; Nir Levine; Ang Li; Huiyi Hu; Andrew Gordon Wilson; Razvan Pascanu (CoRR, 2021)

When are Iterative Gaussian Processes Reliably Accurate? - Wesley J. Maddox; Sanyam Kapoor; Andrew Gordon Wilson (CoRR, 2021)

Deconstructing the Inductive Biases of Hamiltonian Neural Networks. - Nate Gruver; Marc Anton Finzi; Samuel Don Stanton; Andrew Gordon Wilson (ICLR, 2022)

Volatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian Processes. - Gregory W. Benton; Wesley J. Maddox; Andrew Gordon Wilson (ICML, 2022)

Bayesian Model Selection, the Marginal Likelihood, and Generalization. - Sanae Lotfi; Pavel Izmailov; Gregory W. Benton; Micah Goldblum; Andrew Gordon Wilson (ICML, 2022)

Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders. - Samuel Stanton; Wesley J. Maddox; Nate Gruver; Phillip M. Maffettone; Emily Delaney; Peyton Greenside; Andrew Gordon Wilson (ICML, 2022)

Low-Precision Stochastic Gradient Langevin Dynamics. - Ruqi Zhang; Andrew Gordon Wilson; Christopher De Sa (ICML, 2022)

On Feature Learning in the Presence of Spurious Correlations. - Pavel Izmailov; Polina Kirichenko; Nate Gruver; Andrew Gordon Wilson (NeurIPS, 2022)

On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification. - Sanyam Kapoor; Wesley J. Maddox; Pavel Izmailov; Andrew Gordon 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 Gordon 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 001; Yann LeCun; Andrew Gordon Wilson (NeurIPS, 2022)

Chroma-VAE: Mitigating Shortcut Learning with Generative Classifiers. - Wanqian Yang; Polina Kirichenko; Micah Goldblum; Andrew Gordon Wilson (NeurIPS, 2022)

Low-precision arithmetic for fast Gaussian processes. - Wesley J. Maddox; Andres Potapczynski; Andrew Gordon Wilson (UAI, 2022)

Bayesian Optimization with Conformal Coverage Guarantees. - Samuel Stanton; Wesley J. Maddox; Andrew Gordon Wilson (CoRR, 2022)

K-SAM: Sharpness-Aware Minimization at the Speed of SGD. - Renkun Ni; Ping-yeh Chiang; Jonas Geiping; Micah Goldblum; Andrew Gordon Wilson; Tom Goldstein (CoRR, 2022)

What do Vision Transformers Learn? A Visual Exploration. - Amin Ghiasi; Hamid Kazemi; Eitan Borgnia; Steven Reich; Manli Shu; Micah Goldblum; Andrew Gordon Wilson; Tom Goldstein (CoRR, 2022)

Bayesian Optimization with Conformal Prediction Sets. - Samuel Stanton; Wesley J. Maddox; Andrew Gordon Wilson (AISTATS, 2023)

Automated Few-Shot Classification with Instruction-Finetuned Language Models. - Rami Aly; Xingjian Shi; Kaixiang Lin; Aston Zhang; Andrew Gordon Wilson (EMNLP (Findings), 2023)

A Stable and Scalable Method for Solving Initial Value PDEs with Neural Networks. - Marc Anton Finzi; Andres Potapczynski; Matthew Choptuik; Andrew Gordon Wilson (ICLR, 2023)

How Much Data Are Augmentations Worth? An Investigation into Scaling Laws, Invariance, and Implicit Regularization. - Jonas Geiping; Micah Goldblum; Gowthami Somepalli; Ravid Shwartz-Ziv; Tom Goldstein; Andrew Gordon Wilson (ICLR, 2023)

The Lie Derivative for Measuring Learned Equivariance. - Nate Gruver; Marc Anton Finzi; Micah Goldblum; Andrew Gordon Wilson (ICLR, 2023)

Last Layer Re-Training is Sufficient for Robustness to Spurious Correlations. - Polina Kirichenko; Pavel Izmailov; Andrew Gordon Wilson (ICLR, 2023)

Transfer Learning with Deep Tabular Models. - Roman Levin; Valeriia Cherepanova; Avi Schwarzschild; Arpit Bansal; C. Bayan Bruss; Tom Goldstein; Andrew Gordon Wilson; Micah Goldblum (ICLR, 2023)

Learning Multimodal Data Augmentation in Feature Space. - Zichang Liu; Zhiqiang Tang 001; Xingjian Shi; Aston Zhang; Mu Li 003; Anshumali Shrivastava; Andrew Gordon Wilson (ICLR, 2023)

User-defined Event Sampling and Uncertainty Quantification in Diffusion Models for Physical Dynamical Systems. - Marc Anton Finzi; Anudhyan Boral; Andrew Gordon Wilson; Fei Sha; Leonardo Zepeda-Núñez (ICML, 2023)

Simple and Fast Group Robustness by Automatic Feature Reweighting. - Shikai Qiu; Andres Potapczynski; Pavel Izmailov; Andrew Gordon Wilson (ICML, 2023)

Function-Space Regularization in Neural Networks: A Probabilistic Perspective. - Tim G. J. Rudner; Sanyam Kapoor; Shikai Qiu; Andrew Gordon Wilson (ICML, 2023)

A Performance-Driven Benchmark for Feature Selection in Tabular Deep Learning. - Valeriia Cherepanova; Roman Levin; Gowthami Somepalli; Jonas Geiping; C. Bayan Bruss; Andrew Gordon 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 Gordon Wilson; et al. (NeurIPS, 2023)

Large Language Models Are Zero-Shot Time Series Forecasters. - Nate Gruver; Marc Finzi; Shikai Qiu; Andrew Gordon Wilson (NeurIPS, 2023)

Protein Design with Guided Discrete Diffusion. - Nate Gruver; Samuel Stanton; Nathan C. Frey; Tim G. J. Rudner; Isidro Hötzel; Julien Lafrance-Vanasse; Arvind Rajpal; Kyunghyun Cho; Andrew Gordon 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 Gordon Wilson (NeurIPS, 2023)

CoLA: Exploiting Compositional Structure for Automatic and Efficient Numerical Linear Algebra. - Andres Potapczynski; Marc Finzi; Geoff Pleiss; Andrew Gordon Wilson (NeurIPS, 2023)

Should We Learn Most Likely Functions or Parameters? - Shikai Qiu; Tim G. J. Rudner; Sanyam Kapoor; Andrew Gordon Wilson (NeurIPS, 2023)

Simplifying Neural Network Training Under Class Imbalance. - Ravid Shwartz-Ziv; Micah Goldblum; Yucen Lily Li; C. Bayan Bruss; Andrew Gordon Wilson (NeurIPS, 2023)

Visual Explanations of Image-Text Representations via Multi-Modal Information Bottleneck Attribution. - Ying Wang 063; Tim G. J. Rudner; Andrew Gordon Wilson (NeurIPS, 2023)

The No Free Lunch Theorem, Kolmogorov Complexity, and the Role of Inductive Biases in Machine Learning. - Micah Goldblum; Marc Finzi; Keefer Rowan; Andrew Gordon Wilson (CoRR, 2023)

A Cookbook of Self-Supervised Learning. - 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; et al. (CoRR, 2023)

Materials Expert-Artificial Intelligence for Materials Discovery. - Yanjun Liu; Milena Jovanovic; Krishnanand Mallayya; Wesley J. Maddox; Andrew Gordon Wilson; Sebastian Klemenz; Leslie M. Schoop; Eun-Ah Kim (CoRR, 2023)

Perspectives on the State and Future of Deep Learning - 2023. - Micah Goldblum; Anima Anandkumar; Richard G. Baraniuk; Tom Goldstein; Kyunghyun Cho; Zachary C. Lipton; Melanie Mitchell; Preetum Nakkiran; Max Welling; Andrew Gordon Wilson (CoRR, 2023)

Mind the GAP: Improving Robustness to Subpopulation Shifts with Group-Aware Priors. - Tim G. J. Rudner; Ya Shi Zhang; Andrew Gordon Wilson; Julia Kempe (AISTATS, 2024)

Fine-Tuned Language Models Generate Stable Inorganic Materials as Text. - Nate Gruver; Anuroop Sriram; Andrea Madotto; Andrew Gordon Wilson; C. Lawrence Zitnick; Zachary W. Ulissi (ICLR, 2024)

A Study of Bayesian Neural Network Surrogates for Bayesian Optimization. - Yucen Lily Li; Tim G. J. Rudner; Andrew Gordon Wilson (ICLR, 2024)

Scalable and Flexible Causal Discovery with an Efficient Test for Adjacency. - Alan Nawzad Amin; Andrew Gordon Wilson (ICML, 2024)

Position: The No Free Lunch Theorem, Kolmogorov Complexity, and the Role of Inductive Biases in Machine Learning. - Micah Goldblum; Marc Anton Finzi; Keefer Rowan; Andrew Gordon Wilson (ICML, 2024)

Modeling Caption Diversity in Contrastive Vision-Language Pretraining. - Samuel Lavoie; Polina Kirichenko; Mark Ibrahim; Mido Assran; Andrew Gordon Wilson; Aaron C. Courville; Nicolas Ballas (ICML, 2024)

Non-Vacuous Generalization Bounds for Large Language Models. - Sanae Lotfi; Marc Anton Finzi; Yilun Kuang; Tim G. J. Rudner; Micah Goldblum; Andrew Gordon Wilson (ICML, 2024)

Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI. - 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; et al. (ICML, 2024)

Controllable Prompt Tuning For Balancing Group Distributional Robustness. - Hoang Phan; Andrew Gordon Wilson; Qi Lei (ICML, 2024)

Transferring Knowledge From Large Foundation Models to Small Downstream Models. - Shikai Qiu; Boran Han; Danielle C. Maddix; Shuai Zhang 007; Bernie Wang 001; Andrew Gordon Wilson (ICML, 2024)

Compute Better Spent: Replacing Dense Layers with Structured Matrices. - Shikai Qiu; Andres Potapczynski; Marc Anton Finzi; Micah Goldblum; Andrew Gordon Wilson (ICML, 2024)

Large Language Models Must Be Taught to Know What They Don't Know. - Sanyam Kapoor; Nate Gruver; Manley Roberts; Katie Collins; Arka Pal; Umang Bhatt; Adrian Weller; Samuel Dooley; Micah Goldblum; Andrew Gordon Wilson (NeurIPS, 2024)

Unlocking Tokens as Data Points for Generalization Bounds on Larger Language Models. - Sanae Lotfi; Yilun Kuang; Marc Finzi; Brandon Amos; Micah Goldblum; Andrew Gordon Wilson (NeurIPS, 2024)

Searching for Efficient Linear Layers over a Continuous Space of Structured Matrices. - Andres Potapczynski; Shikai Qiu; Marc Finzi; Christopher Ferri; Charlie Chen; Micah Goldblum; C. Bayan Bruss; Christopher De Sa; Andrew Gordon Wilson (NeurIPS, 2024)

Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI. - 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; et al. (CoRR, 2024)

Chronos: Learning the Language of Time Series. - 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; et al. (CoRR, 2024)

Generating Potent Poisons and Backdoors from Scratch with Guided Diffusion. - Hossein Souri; Arpit Bansal; Hamid Kazemi; Liam Fowl; Aniruddha Saha; Jonas Geiping; Andrew Gordon Wilson; Rama Chellappa; Tom Goldstein; Micah Goldblum (CoRR, 2024)

Just How Flexible are Neural Networks in Practice? - Ravid Shwartz-Ziv; Micah Goldblum; Arpit Bansal; C. Bayan Bruss; Yann LeCun; Andrew Gordon Wilson (CoRR, 2024)

Searching for Efficient Linear Layers over a Continuous Space of Structured Matrices. - Andres Potapczynski; Shikai Qiu; Marc Finzi; Christopher Ferri; Zixi Chen; Micah Goldblum; C. Bayan Bruss; Christopher De Sa; Andrew Gordon Wilson (CoRR, 2024)

LLMForecaster: Improving Seasonal Event Forecasts with Unstructured Textual Data. - Hanyu Zhang; Chuck Arvin; Dmitry Efimov; Michael W. Mahoney; Dominique Perrault-Joncas; Shankar Ramasubramanian; Andrew Gordon Wilson; Malcolm Wolff (CoRR, 2024)

Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization. - Luca Masserano; Abdul Fatir Ansari; Boran Han; Xiyuan Zhang; Christos Faloutsos; Michael W. Mahoney; Andrew Gordon Wilson; Youngsuk Park; Syama Sundar Rangapuram; Danielle C. Maddix; Yuyang Wang 001 (CoRR, 2024)

Fortuna: A Library for Uncertainty Quantification in Deep Learning. - Gianluca Detommaso; Alberto Gasparin; Michele Donini; Matthias W. Seeger; Andrew Gordon Wilson; Cédric Archambeau (J. Mach. Learn. Res., 2024)

Chronos: Learning the Language of Time Series. - 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; et al. (Trans. Mach. Learn. Res., 2024)

Fine-Tuning with Uncertainty-Aware Priors Makes Vision and Language Foundation Models More Reliable. - Tim G. J. Rudner; Xiang Pan; Yucen Lily Li; Ravid Shwartz-Ziv; Andrew Gordon Wilson (AISTATS, 2025)

Bayesian Optimization of Antibodies Informed by a Generative Model of Evolving Sequences. - Alan Nawzad Amin; Nate Gruver; Yilun Kuang; Yucen Lily Li; Hunter Elliott; Calvin McCarter; Aniruddh Raghu; Peyton Greenside; Andrew Gordon Wilson (ICLR, 2025)

Compute-Optimal LLMs Provably Generalize Better with Scale. - Marc Anton Finzi; Sanyam Kapoor; Diego Granziol; Anming Gu; Christopher De Sa; J. Zico Kolter; Andrew Gordon Wilson (ICLR, 2025)

Transformers Boost the Performance of Decision Trees on Tabular Data across Sample Sizes. - Mayuka Jayawardhana; Renbo Tu; Samuel Dooley; Valeriia Cherepanova; Andrew Gordon Wilson; Frank Hutter; Colin White; Tom Goldstein; Micah Goldblum (CoRR, 2025)

Deep Learning is Not So Mysterious or Different. - Andrew Gordon Wilson (CoRR, 2025)

When Should We Orchestrate Multiple Agents? - Umang Bhatt; Sanyam Kapoor; Mihir Upadhyay; Ilia Sucholutsky; Francesco Quinzan; Katherine M. Collins; Adrian Weller; Andrew Gordon Wilson; Muhammad Bilal Zafar (CoRR, 2025)

Why Masking Diffusion Works: Condition on the Jump Schedule for Improved Discrete Diffusion. - Alan Nawzad Amin; Nate Gruver; Andrew Gordon Wilson (CoRR, 2025)

Training Flexible Models of Genetic Variant Effects from Functional Annotations using Accelerated Linear Algebra. - Alan Nawzad Amin; Andres Potapczynski; Andrew Gordon Wilson (CoRR, 2025)

Does Multimodality Lead to Better Time Series Forecasting? - Xiyuan Zhang; Boran Han; Haoyang Fang; Abdul Fatir Ansari; Shuai Zhang 007; Danielle C. Maddix; Cuixiong Hu; Andrew Gordon Wilson; Michael W. Mahoney; Hao Wang 014; Yan Liu; Huzefa Rangwala; et al. (CoRR, 2025)

Out-of-Distribution Detection Methods Answer the Wrong Questions. - Yucen Lily Li; Daohan Lu; Polina Kirichenko; Shikai Qiu; Tim G. J. Rudner; C. Bayan Bruss; Andrew Gordon Wilson (CoRR, 2025)

Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks. - Shikai Qiu; Lechao Xiao; Andrew Gordon Wilson; Jeffrey Pennington; Atish Agarwala (CoRR, 2025)

Small Batch Size Training for Language Models: When Vanilla SGD Works, and Why Gradient Accumulation Is Wasteful. - Martin Marek; Sanae Lotfi; Aditya Somasundaram; Andrew Gordon Wilson; Micah Goldblum (CoRR, 2025)