← Authors

Andrew G Wilson

36 papers · 2010–2023 · 1 conference · across top CS/AI conferences

Achievements

🐝 Cross-Pollinator (15) 🌈 Renaissance Researcher (9) πŸŒ‰ Interdisciplinary Bridge πŸƒ Academic Marathon (13) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird πŸ—ΊοΈ Taxonomy Completionist (65) 🏠 Conference Loyalist (36) πŸ”¬ Deep Specialist (14) πŸ† Keyword Champion 🌱 Topic Pioneer πŸ—ƒοΈ Keyword Collector (165) ⚑ Prolific Year (6) πŸ“ˆ Trend Setter πŸ’Ž Century Club (36) πŸ”₯ Unstoppable (5) ❓ The Questioner (2)

Conferences

NIPS (36)

Top co-authors

Pavel Izmailov (8) Micah Goldblum (6) Marc Finzi (6) Polina Kirichenko (5) Sanyam Kapoor (4) Eric P. Xing (3) David Bindel (3) Tim G. J. Rudner (3) Samuel Stanton (3) Nate Gruver (3)

Keywords

gaussian process (13) bayesian optimization (6) neural network (5) bayesian inference (4) data augmentation (4) kernel learning (4) transfer learning (3) time series forecasting (3) generative adversarial network (2) multi-task learning (2) acquisition function (2) scalable inference (2) image classification (2) image generation (2) bayesian neural network (2) sample efficiency (2) markov chain monte carlo (2) variational inference (2) reinforcement learning (2) domain generalization (2)

Papers

Protein Design with Guided Discrete Diffusion NIPS 2023
CoLA: Exploiting Compositional Structure for Automatic and Efficient Numerical Linear Algebra NIPS 2023
A Performance-Driven Benchmark for Feature Selection in Tabular Deep Learning NIPS 2023
Should We Learn Most Likely Functions or Parameters? NIPS 2023
Simplifying Neural Network Training Under Class Imbalance NIPS 2023
Battle of the Backbones: A Large-Scale Comparison of Pretrained Models across Computer Vision Tasks NIPS 2023
Large Language Models Are Zero-Shot Time Series Forecasters NIPS 2023
Understanding the detrimental class-level effects of data augmentation NIPS 2023
Visual Explanations of Image-Text Representations via Multi-Modal Information Bottleneck Attribution NIPS 2023
Chroma-VAE: Mitigating Shortcut Learning with Generative Classifiers NIPS 2022
On Feature Learning in the Presence of Spurious Correlations NIPS 2022
PAC-Bayes Compression Bounds So Tight That They Can Explain Generalization NIPS 2022
Pre-Train Your Loss: Easy Bayesian Transfer Learning with Informative Priors NIPS 2022
On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification NIPS 2022
Conditioning Sparse Variational Gaussian Processes for Online Decision-making NIPS 2021
Dangers of Bayesian Model Averaging under Covariate Shift NIPS 2021
Does Knowledge Distillation Really Work? NIPS 2021
Bayesian Optimization with High-Dimensional Outputs NIPS 2021
Residual Pathway Priors for Soft Equivariance Constraints NIPS 2021
Why Normalizing Flows Fail to Detect Out-of-Distribution Data NIPS 2020
BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization NIPS 2020
Learning Invariances in Neural Networks from Training Data NIPS 2020
Simplifying Hamiltonian and Lagrangian Neural Networks via Explicit Constraints NIPS 2020
Improving GAN Training with Probability Ratio Clipping and Sample Reweighting NIPS 2020
Bayesian Deep Learning and a Probabilistic Perspective of Generalization NIPS 2020
Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs NIPS 2018
Scaling Gaussian Process Regression with Derivatives NIPS 2018
GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration NIPS 2018
Bayesian GAN NIPS 2017
Bayesian Optimization with Gradients NIPS 2017
Scalable Log Determinants for Gaussian Process Kernel Learning NIPS 2017
Scalable Levy Process Priors for Spectral Kernel Learning NIPS 2017
Stochastic Variational Deep Kernel Learning NIPS 2016
The Human Kernel NIPS 2015
Fast Kernel Learning for Multidimensional Pattern Extrapolation NIPS 2014
Copula Processes NIPS 2010