Andrew G Wilson

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

Achievements

+12 more ↓

🐣 Hot Topic Early Bird 🌉 Interdisciplinary Bridge 🏃 Academic Marathon (13) 🧭 Keyword Pioneer 🐝 Cross-Pollinator (15)

🐣 Hot Topic Early Bird 🗺️ Taxonomy Completionist (65) 🏠 Conference Loyalist (36) 🔬 Deep Specialist (14) 🏆 Keyword Champion 🌱 Topic Pioneer 🗃️ Keyword Collector (165) ⚡ Prolific Year (6) 📈 Trend Setter 💎 Century Club (36) 🔥 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) scalable inference (2) bayesian neural network (2) image classification (2) acquisition function (2) markov chain monte carlo (2) sample efficiency (2) variational inference (2) domain generalization (2) reinforcement learning (2) image generation (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