Andrew Gordon Wilson

research

∙ 05/31/2023

A Study of Bayesian Neural Network Surrogates for Bayesian Optimization

Bayesian optimization is a highly efficient approach to optimizing objec...

0 Yucen Lily Li, et al. ∙

research

∙ 05/31/2023

Protein Design with Guided Discrete Diffusion

A popular approach to protein design is to combine a generative model wi...

3 Nate Gruver, et al. ∙

research

∙ 05/21/2023

Automated Few-shot Classification with Instruction-Finetuned Language Models

A particularly successful class of approaches for few-shot learning comb...

0 Rami Aly, et al. ∙

research

∙ 04/28/2023

A Stable and Scalable Method for Solving Initial Value PDEs with Neural Networks

Unlike conventional grid and mesh based methods for solving partial diff...

0 Marc Finzi, et al. ∙

research

∙ 04/24/2023

A Cookbook of Self-Supervised Learning

Self-supervised learning, dubbed the dark matter of intelligence, is a p...

0 Randall Balestriero, et al. ∙

research

∙ 04/11/2023

The No Free Lunch Theorem, Kolmogorov Complexity, and the Role of Inductive Biases in Machine Learning

No free lunch theorems for supervised learning state that no learner can...

0 Micah Goldblum, et al. ∙

research

∙ 02/08/2023

Fortuna: A Library for Uncertainty Quantification in Deep Learning

We present Fortuna, an open-source library for uncertainty quantificatio...

0 Gianluca Detommaso, et al. ∙

research

∙ 12/29/2022

Learning Multimodal Data Augmentation in Feature Space

The ability to jointly learn from multiple modalities, such as text, aud...

14 Zichang Liu, et al. ∙

research

∙ 12/13/2022

What do Vision Transformers Learn? A Visual Exploration

Vision transformers (ViTs) are quickly becoming the de-facto architectur...

0 Amin Ghiasi, et al. ∙

research

∙ 11/28/2022

Chroma-VAE: Mitigating Shortcut Learning with Generative Classifiers

Deep neural networks are susceptible to shortcut learning, using simple ...

0 Wanqian Yang, et al. ∙

research

∙ 11/24/2022

PAC-Bayes Compression Bounds So Tight That They Can Explain Generalization

While there has been progress in developing non-vacuous generalization b...

0 Sanae Lotfi, et al. ∙

research

∙ 10/23/2022

K-SAM: Sharpness-Aware Minimization at the Speed of SGD

Sharpness-Aware Minimization (SAM) has recently emerged as a robust tech...

0 Renkun Ni, et al. ∙

research

∙ 10/22/2022

Bayesian Optimization with Conformal Coverage Guarantees

Bayesian optimization is a coherent, ubiquitous approach to decision-mak...

0 Samuel Stanton, et al. ∙

research

∙ 10/20/2022

On Feature Learning in the Presence of Spurious Correlations

Deep classifiers are known to rely on spurious features x2013 patterns w...

0 Pavel Izmailov, et al. ∙

research

∙ 10/12/2022

How Much Data Are Augmentations Worth? An Investigation into Scaling Laws, Invariance, and Implicit Regularization

Despite the clear performance benefits of data augmentations, little is ...

0 Jonas Geiping, et al. ∙

research

∙ 10/06/2022

The Lie Derivative for Measuring Learned Equivariance

Equivariance guarantees that a model's predictions capture key symmetrie...

0 Nate Gruver, et al. ∙

research

∙ 07/14/2022

Low-Precision Arithmetic for Fast Gaussian Processes

Low-precision arithmetic has had a transformative effect on the training...

0 Wesley J. Maddox, et al. ∙

research

∙ 07/13/2022

Volatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian Processes

A broad class of stochastic volatility models are defined by systems of ...

0 Gregory Benton, et al. ∙

research

∙ 06/20/2022

Low-Precision Stochastic Gradient Langevin Dynamics

While low-precision optimization has been widely used to accelerate deep...

6 Ruqi Zhang, et al. ∙

research

∙ 05/20/2022

Pre-Train Your Loss: Easy Bayesian Transfer Learning with Informative Priors

Deep learning is increasingly moving towards a transfer learning paradig...

17 Ravid Shwartz-Ziv, et al. ∙

research

∙ 04/06/2022

Last Layer Re-Training is Sufficient for Robustness to Spurious Correlations

Neural network classifiers can largely rely on simple spurious features,...

0 Polina Kirichenko, et al. ∙

research

∙ 03/30/2022

On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification

Aleatoric uncertainty captures the inherent randomness of the data, such...

0 Sanyam Kapoor, et al. ∙

research

∙ 03/23/2022

Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders

Bayesian optimization is a gold standard for query-efficient continuous ...

0 Samuel Stanton, et al. ∙

research

∙ 02/23/2022

Bayesian Model Selection, the Marginal Likelihood, and Generalization

How do we compare between hypotheses that are entirely consistent with o...

0 Sanae Lotfi, et al. ∙

research

∙ 02/10/2022

Deconstructing the Inductive Biases of Hamiltonian Neural Networks

Physics-inspired neural networks (NNs), such as Hamiltonian or Lagrangia...

0 Nate Gruver, et al. ∙

research

∙ 12/31/2021

When are Iterative Gaussian Processes Reliably Accurate?

While recent work on conjugate gradient methods and Lanczos decompositio...

0 Wesley J. Maddox, et al. ∙

research

∙ 12/02/2021

Residual Pathway Priors for Soft Equivariance Constraints

There is often a trade-off between building deep learning systems that a...

0 Marc Finzi, et al. ∙

research

∙ 10/28/2021

Conditioning Sparse Variational Gaussian Processes for Online Decision-making

With a principled representation of uncertainty and closed form posterio...

0 Wesley J. Maddox, et al. ∙

research

∙ 06/24/2021

Bayesian Optimization with High-Dimensional Outputs

Bayesian Optimization is a sample-efficient black-box optimization proce...

0 Wesley J. Maddox, et al. ∙

research

∙ 06/24/2021

Task-agnostic Continual Learning with Hybrid Probabilistic Models

Learning new tasks continuously without forgetting on a constantly chang...

0 Polina Kirichenko, et al. ∙

research

∙ 06/22/2021

Dangers of Bayesian Model Averaging under Covariate Shift

Approximate Bayesian inference for neural networks is considered a robus...

0 Pavel Izmailov, et al. ∙

research

∙ 06/12/2021

SKIing on Simplices: Kernel Interpolation on the Permutohedral Lattice for Scalable Gaussian Processes

State-of-the-art methods for scalable Gaussian processes use iterative a...

0 Sanyam Kapoor, et al. ∙

research

∙ 06/10/2021

Scalable Variational Gaussian Processes via Harmonic Kernel Decomposition

We introduce a new scalable variational Gaussian process approximation w...

0 Shengyang Sun, et al. ∙

research

∙ 06/10/2021

Does Knowledge Distillation Really Work?

Knowledge distillation is a popular technique for training a small stude...

0 Samuel Stanton, et al. ∙

research

∙ 04/29/2021

What Are Bayesian Neural Network Posteriors Really Like?

The posterior over Bayesian neural network (BNN) parameters is extremely...

0 Pavel Izmailov, et al. ∙

research

∙ 04/19/2021

A Practical Method for Constructing Equivariant Multilayer Perceptrons for Arbitrary Matrix Groups

Symmetries and equivariance are fundamental to the generalization of neu...

0 Marc Finzi, et al. ∙

research

∙ 03/02/2021

Kernel Interpolation for Scalable Online Gaussian Processes

Gaussian processes (GPs) provide a gold standard for performance in onli...

0 Samuel Stanton, et al. ∙

research

∙ 03/02/2021

Fast Adaptation with Linearized Neural Networks

The inductive biases of trained neural networks are difficult to underst...

0 Wesley J. Maddox, et al. ∙

research

∙ 02/25/2021

Loss Surface Simplexes for Mode Connecting Volumes and Fast Ensembling

With a better understanding of the loss surfaces for multilayer networks...

1 Gregory W. Benton, et al. ∙

research

∙ 10/26/2020

Simplifying Hamiltonian and Lagrangian Neural Networks via Explicit Constraints

Reasoning about the physical world requires models that are endowed with...

0 Marc Finzi, et al. ∙

research

∙ 10/22/2020

Learning Invariances in Neural Networks

Invariances to translations have imbued convolutional neural networks wi...

0 Gregory Benton, et al. ∙

research

∙ 08/28/2020

On the model-based stochastic value gradient for continuous reinforcement learning

Model-based reinforcement learning approaches add explicit domain knowle...

6 Brandon Amos, et al. ∙

research

∙ 06/15/2020

Why Normalizing Flows Fail to Detect Out-of-Distribution Data

Detecting out-of-distribution (OOD) data is crucial for robust machine l...

13 Polina Kirichenko, et al. ∙

research

∙ 06/12/2020

Improving GAN Training with Probability Ratio Clipping and Sample Reweighting

Despite success on a wide range of problems related to vision, generativ...

15 Yue Wu, et al. ∙

research

∙ 03/04/2020

Rethinking Parameter Counting in Deep Models: Effective Dimensionality Revisited

Neural networks appear to have mysterious generalization properties when...

14 Wesley J. Maddox, et al. ∙

research

∙ 02/25/2020

Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data

The translation equivariance of convolutional layers enables convolution...

11 Marc Finzi, et al. ∙

research

∙ 02/20/2020

Bayesian Deep Learning and a Probabilistic Perspective of Generalization

The key distinguishing property of a Bayesian approach is marginalizatio...

41 Andrew Gordon Wilson, et al. ∙

research

∙ 01/29/2020

The Case for Bayesian Deep Learning

The key distinguishing property of a Bayesian approach is marginalizatio...

112 Andrew Gordon Wilson, et al. ∙

research

∙ 12/30/2019

Semi-Supervised Learning with Normalizing Flows

Normalizing flows transform a latent distribution through an invertible ...

13 Pavel Izmailov, et al. ∙

research

∙ 12/30/2019

Randomly Projected Additive Gaussian Processes for Regression

Gaussian processes (GPs) provide flexible distributions over functions, ...

30 Ian A. Delbridge, et al. ∙

Andrew Gordon Wilson

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