Andrew G Wilson

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