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09/06/2023
CoLA: Exploiting Compositional Structure for Automatic and Efficient Numerical Linear Algebra
Many areas of machine learning and science involve large linear algebra ...
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06/19/2023
Simple and Fast Group Robustness by Automatic Feature Reweighting
A major challenge to out-of-distribution generalization is reliance on s...
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06/13/2023
User-defined Event Sampling and Uncertainty Quantification in Diffusion Models for Physical Dynamical Systems
Diffusion models are a class of probabilistic generative models that hav...
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05/31/2023
A Study of Bayesian Neural Network Surrogates for Bayesian Optimization
Bayesian optimization is a highly efficient approach to optimizing objec...
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05/31/2023
Protein Design with Guided Discrete Diffusion
A popular approach to protein design is to combine a generative model wi...
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05/21/2023
Automated Few-shot Classification with Instruction-Finetuned Language Models
A particularly successful class of approaches for few-shot learning comb...
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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...
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04/24/2023
A Cookbook of Self-Supervised Learning
Self-supervised learning, dubbed the dark matter of intelligence, is a p...
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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...
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02/08/2023
Fortuna: A Library for Uncertainty Quantification in Deep Learning
We present Fortuna, an open-source library for uncertainty quantificatio...
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12/29/2022
Learning Multimodal Data Augmentation in Feature Space
The ability to jointly learn from multiple modalities, such as text, aud...
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12/13/2022
What do Vision Transformers Learn? A Visual Exploration
Vision transformers (ViTs) are quickly becoming the de-facto architectur...
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11/28/2022
Chroma-VAE: Mitigating Shortcut Learning with Generative Classifiers
Deep neural networks are susceptible to shortcut learning, using simple ...
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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...
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10/23/2022
K-SAM: Sharpness-Aware Minimization at the Speed of SGD
Sharpness-Aware Minimization (SAM) has recently emerged as a robust tech...
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10/22/2022
Bayesian Optimization with Conformal Coverage Guarantees
Bayesian optimization is a coherent, ubiquitous approach to decision-mak...
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10/20/2022
On Feature Learning in the Presence of Spurious Correlations
Deep classifiers are known to rely on spurious features x2013 patterns w...
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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 ...
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10/06/2022
The Lie Derivative for Measuring Learned Equivariance
Equivariance guarantees that a model's predictions capture key symmetrie...
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07/14/2022
Low-Precision Arithmetic for Fast Gaussian Processes
Low-precision arithmetic has had a transformative effect on the training...
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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 ...
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06/20/2022
Low-Precision Stochastic Gradient Langevin Dynamics
While low-precision optimization has been widely used to accelerate deep...
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05/20/2022
Pre-Train Your Loss: Easy Bayesian Transfer Learning with Informative Priors
Deep learning is increasingly moving towards a transfer learning paradig...
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04/06/2022
Last Layer Re-Training is Sufficient for Robustness to Spurious Correlations
Neural network classifiers can largely rely on simple spurious features,...
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03/30/2022
On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification
Aleatoric uncertainty captures the inherent randomness of the data, such...
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03/23/2022
Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders
Bayesian optimization is a gold standard for query-efficient continuous ...
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02/23/2022
Bayesian Model Selection, the Marginal Likelihood, and Generalization
How do we compare between hypotheses that are entirely consistent with o...
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02/10/2022
Deconstructing the Inductive Biases of Hamiltonian Neural Networks
Physics-inspired neural networks (NNs), such as Hamiltonian or Lagrangia...
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12/31/2021
When are Iterative Gaussian Processes Reliably Accurate?
While recent work on conjugate gradient methods and Lanczos decompositio...
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12/02/2021
Residual Pathway Priors for Soft Equivariance Constraints
There is often a trade-off between building deep learning systems that a...
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10/28/2021
Conditioning Sparse Variational Gaussian Processes for Online Decision-making
With a principled representation of uncertainty and closed form posterio...
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06/24/2021
Bayesian Optimization with High-Dimensional Outputs
Bayesian Optimization is a sample-efficient black-box optimization proce...
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06/24/2021
Task-agnostic Continual Learning with Hybrid Probabilistic Models
Learning new tasks continuously without forgetting on a constantly chang...
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06/22/2021
Dangers of Bayesian Model Averaging under Covariate Shift
Approximate Bayesian inference for neural networks is considered a robus...
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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...
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06/10/2021
Scalable Variational Gaussian Processes via Harmonic Kernel Decomposition
We introduce a new scalable variational Gaussian process approximation w...
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06/10/2021
Does Knowledge Distillation Really Work?
Knowledge distillation is a popular technique for training a small stude...
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04/29/2021
What Are Bayesian Neural Network Posteriors Really Like?
The posterior over Bayesian neural network (BNN) parameters is extremely...
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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...
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03/02/2021
Kernel Interpolation for Scalable Online Gaussian Processes
Gaussian processes (GPs) provide a gold standard for performance in onli...
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03/02/2021
Fast Adaptation with Linearized Neural Networks
The inductive biases of trained neural networks are difficult to underst...
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02/25/2021
Loss Surface Simplexes for Mode Connecting Volumes and Fast Ensembling
With a better understanding of the loss surfaces for multilayer networks...
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10/26/2020
Simplifying Hamiltonian and Lagrangian Neural Networks via Explicit Constraints
Reasoning about the physical world requires models that are endowed with...
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10/22/2020
Learning Invariances in Neural Networks
Invariances to translations have imbued convolutional neural networks wi...
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08/28/2020
On the model-based stochastic value gradient for continuous reinforcement learning
Model-based reinforcement learning approaches add explicit domain knowle...
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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...
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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...
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03/04/2020
Rethinking Parameter Counting in Deep Models: Effective Dimensionality Revisited
Neural networks appear to have mysterious generalization properties when...
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02/25/2020
Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data
The translation equivariance of convolutional layers enables convolution...
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02/20/2020
