← Authors

Andrew Gordon Wilson

61 papers · 2016–2025 · 7 conferences · across top CS/AI conferences

Achievements

🌈 Renaissance Researcher (8) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🐝 Cross-Pollinator (13) πŸƒ Academic Marathon (9) πŸŒ‰ Interdisciplinary Bridge 🌍 Conference Polyglot (7) πŸ—ΊοΈ Taxonomy Completionist (26) 🏠 Conference Loyalist (28) 🀝 Dynamic Duo (10) πŸ‘‘ Triple Crown πŸ† Keyword Champion (2) πŸ‘₯ Mega-Team (25) πŸ”¬ Deep Specialist (14) πŸ“ˆ Trend Setter πŸš€ Conference Pioneer πŸ”₯ Unstoppable (10) ❓ The Questioner ⚑ Prolific Year (9) πŸ’Ž Century Club (61) πŸ—ƒοΈ Keyword Collector (67)

Conferences

ICML (28) ICLR (14) AISTATS (6) NIPS (6) JMLR (3) UAI (3) L4DC (1)

Top co-authors

Micah Goldblum (10) Pavel Izmailov (8) Marc Anton Finzi (8) Shikai Qiu (8) Tim G. J. Rudner (7) Nate Gruver (6) Polina Kirichenko (6) Marc Finzi (6) Andres Potapczynski (6) Samuel Stanton (5)

Research topics

Methods (1)

Keywords

gaussian process (12) bayesian inference (8) uncertainty quantification (7) neural network (4) bayesian optimization (4) uncertainty estimation (3) conformal prediction (2) additive kernel (2) kernel interpolation (2) stochastic weight averaging (2) active learning (2) inducing point (2) bayesian deep learning (2) large language model (2) model calibration (2) domain generalization (2) posterior approximation (2) predictive distribution (2) scalable inference (2) approximate inference (1)

Papers

Compute-Optimal LLMs Provably Generalize Better with Scale ICLR 2025
Bayesian Optimization of Antibodies Informed by a Generative Model of Evolving Sequences ICLR 2025
Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization ICML 2025
Fine-Tuning with Uncertainty-Aware Priors Makes Vision and Language Foundation Models More Reliable AISTATS 2025
Customizing the Inductive Biases of Softmax Attention using Structured Matrices ICML 2025
Training Flexible Models of Genetic Variant Effects from Functional Annotations using Accelerated Linear Algebra ICML 2025
Position: Deep Learning is Not So Mysterious or Different ICML 2025
Position: Supervised Classifiers Answer the Wrong Questions for OOD Detection ICML 2025
Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks ICML 2025
Fortuna: A Library for Uncertainty Quantification in Deep Learning JMLR 2024
Mind the GAP: Improving Robustness to Subpopulation Shifts with Group-Aware Priors AISTATS 2024
Unlocking Tokens as Data Points for Generalization Bounds on Larger Language Models NIPS 2024
Large Language Models Must Be Taught to Know What They Don’t Know NIPS 2024
Fine-Tuned Language Models Generate Stable Inorganic Materials as Text ICLR 2024
Compute Better Spent: Replacing Dense Layers with Structured Matrices ICML 2024
Transferring Knowledge From Large Foundation Models to Small Downstream Models ICML 2024
A Study of Bayesian Neural Network Surrogates for Bayesian Optimization ICLR 2024
Controllable Prompt Tuning For Balancing Group Distributional Robustness ICML 2024
Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI ICML 2024
Non-Vacuous Generalization Bounds for Large Language Models ICML 2024
Modeling Caption Diversity in Contrastive Vision-Language Pretraining ICML 2024
Position: The No Free Lunch Theorem, Kolmogorov Complexity, and the Role of Inductive Biases in Machine Learning ICML 2024
Scalable and Flexible Causal Discovery with an Efficient Test for Adjacency ICML 2024
Searching for Efficient Linear Layers over a Continuous Space of Structured Matrices NIPS 2024
User-defined Event Sampling and Uncertainty Quantification in Diffusion Models for Physical Dynamical Systems ICML 2023
Bayesian Optimization with Conformal Prediction Sets AISTATS 2023
A Stable and Scalable Method for Solving Initial Value PDEs with Neural Networks ICLR 2023
How Much Data Are Augmentations Worth? An Investigation into Scaling Laws, Invariance, and Implicit Regularization ICLR 2023
Transfer Learning with Deep Tabular Models ICLR 2023
Learning Multimodal Data Augmentation in Feature Space ICLR 2023
The Lie Derivative for Measuring Learned Equivariance ICLR 2023
Last Layer Re-Training is Sufficient for Robustness to Spurious Correlations ICLR 2023
Simple and Fast Group Robustness by Automatic Feature Reweighting ICML 2023
Function-Space Regularization in Neural Networks: A Probabilistic Perspective ICML 2023
Bayesian Model Selection, the Marginal Likelihood, and Generalization ICML 2022
Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders ICML 2022
Low-Precision Stochastic Gradient Langevin Dynamics ICML 2022
Low-precision arithmetic for fast Gaussian processes UAI 2022
Deconstructing the Inductive Biases of Hamiltonian Neural Networks ICLR 2022
Volatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian Processes ICML 2022
Fast Adaptation with Linearized Neural Networks AISTATS 2021
Kernel Interpolation for Scalable Online Gaussian Processes AISTATS 2021
A Practical Method for Constructing Equivariant Multilayer Perceptrons for Arbitrary Matrix Groups ICML 2021
On the Model-Based Stochastic Value Gradient for Continuous Reinforcement Learning L4DC 2021
Semi-Supervised Learning with Normalizing Flows ICML 2020
Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data ICML 2020
Randomly Projected Additive Gaussian Processes for Regression ICML 2020
Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning ICLR 2020
There Are Many Consistent Explanations of Unlabeled Data: Why You Should Average ICLR 2019
SWALP : Stochastic Weight Averaging in Low Precision Training ICML 2019
Exact Gaussian Processes on a Million Data Points NIPS 2019
Subspace Inference for Bayesian Deep Learning UAI 2019
Practical Multi-fidelity Bayesian Optimization for Hyperparameter Tuning UAI 2019
Simple Black-box Adversarial Attacks ICML 2019
Function-Space Distributions over Kernels NIPS 2019
Change Surfaces for Expressive Multidimensional Changepoints and Counterfactual Prediction JMLR 2019
A Simple Baseline for Bayesian Uncertainty in Deep Learning NIPS 2019
Hierarchical Density Order Embeddings ICLR 2018
Constant-Time Predictive Distributions for Gaussian Processes ICML 2018
Learning Scalable Deep Kernels with Recurrent Structure JMLR 2017
Deep Kernel Learning AISTATS 2016