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07/02/2023
Morse Neural Networks for Uncertainty Quantification
We introduce a new deep generative model useful for uncertainty quantifi...
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05/26/2023
Building One-class Detector for Anything: Open-vocabulary Zero-shot OOD Detection Using Text-image Models
We focus on the challenge of out-of-distribution (OOD) detection in deep...
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02/13/2023
A Simple Zero-shot Prompt Weighting Technique to Improve Prompt Ensembling in Text-Image Models
Contrastively trained text-image models have the remarkable ability to p...
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02/11/2023
Pushing the Accuracy-Group Robustness Frontier with Introspective Self-play
Standard empirical risk minimization (ERM) training can produce deep neu...
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12/20/2022
Improving the Robustness of Summarization Models by Detecting and Removing Input Noise
The evaluation of abstractive summarization models typically uses test d...
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09/30/2022
Out-of-Distribution Detection and Selective Generation for Conditional Language Models
Machine learning algorithms typically assume independent and identically...
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05/01/2022
A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness
Accurate uncertainty quantification is a major challenge in deep learnin...
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11/25/2021
Reliable Graph Neural Networks for Drug Discovery Under Distributional Shift
The concern of overconfident mis-predictions under distributional shift ...
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10/15/2021
Understanding and Improving Robustness of Vision Transformers through Patch-based Negative Augmentation
We investigate the robustness of vision transformers (ViTs) through the ...
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10/07/2021
Sparse MoEs meet Efficient Ensembles
Machine learning models based on the aggregated outputs of submodels, ei...
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10/06/2021
Deep Classifiers with Label Noise Modeling and Distance Awareness
Uncertainty estimation in deep learning has recently emerged as a crucia...
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07/30/2021
Soft Calibration Objectives for Neural Networks
Optimal decision making requires that classifiers produce uncertainty es...
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07/23/2021
A Realistic Simulation Framework for Learning with Label Noise
We propose a simulation framework for generating realistic instance-depe...
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07/17/2021
BEDS-Bench: Behavior of EHR-models under Distributional Shift–A Benchmark
Machine learning has recently demonstrated impressive progress in predic...
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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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06/16/2021
A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detection
Mahalanobis distance (MD) is a simple and popular post-processing method...
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06/15/2021
Predicting Unreliable Predictions by Shattering a Neural Network
Piecewise linear neural networks can be split into subfunctions, each wi...
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06/07/2021
Uncertainty Baselines: Benchmarks for Uncertainty Robustness in Deep Learning
High-quality estimates of uncertainty and robustness are crucial for num...
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06/06/2021
Exploring the Limits of Out-of-Distribution Detection
Near out-of-distribution detection (OOD) is a major challenge for deep n...
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04/08/2021
Does Your Dermatology Classifier Know What It Doesn't Know? Detecting the Long-Tail of Unseen Conditions
We develop and rigorously evaluate a deep learning based system that can...
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10/19/2020
Combining Ensembles and Data Augmentation can Harm your Calibration
Ensemble methods which average over multiple neural network predictions ...
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10/13/2020
Training independent subnetworks for robust prediction
Recent approaches to efficiently ensemble neural networks have shown tha...
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07/11/2020
Bayesian Deep Ensembles via the Neural Tangent Kernel
We explore the link between deep ensembles and Gaussian processes (GPs) ...
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07/10/2020
Revisiting One-vs-All Classifiers for Predictive Uncertainty and Out-of-Distribution Detection in Neural Networks
Accurate estimation of predictive uncertainty in modern neural networks ...
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06/19/2020
Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift
Covariate shift has been shown to sharply degrade both predictive accura...
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06/17/2020
Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness
Bayesian neural networks (BNN) and deep ensembles are principled approac...
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06/16/2020
Density of States Estimation for Out-of-Distribution Detection
Perhaps surprisingly, recent studies have shown probabilistic model like...
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05/14/2020
Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors
Bayesian neural networks (BNNs) demonstrate promising success in improvi...
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12/05/2019
AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty
Modern deep neural networks can achieve high accuracy when the training ...
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12/05/2019
Normalizing Flows for Probabilistic Modeling and Inference
Normalizing flows provide a general mechanism for defining expressive pr...
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12/05/2019
Deep Ensembles: A Loss Landscape Perspective
Deep ensembles have been empirically shown to be a promising approach fo...
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06/07/2019
Detecting Out-of-Distribution Inputs to Deep Generative Models Using a Test for Typicality
Recent work has shown that deep generative models can assign higher like...
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06/07/2019
Likelihood Ratios for Out-of-Distribution Detection
Discriminative neural networks offer little or no performance guarantees...
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06/06/2019
Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift
Modern machine learning methods including deep learning have achieved gr...
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02/07/2019
Hybrid Models with Deep and Invertible Features
We propose a neural hybrid model consisting of a linear model defined on...
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12/05/2018
Adapting Auxiliary Losses Using Gradient Similarity
One approach to deal with the statistical inefficiency of neural network...
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10/22/2018
Do Deep Generative Models Know What They Don't Know?
A neural network deployed in the wild may be asked to make predictions f...
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07/24/2018
Learning from Delayed Outcomes with Intermediate Observations
Optimizing for long term value is desirable in many practical applicatio...
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02/19/2018
Distribution Matching in Variational Inference
The difficulties in matching the latent posterior to the prior, balancin...
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10/23/2017
Many Paths to Equilibrium: GANs Do Not Need to Decrease a Divergence At Every Step
Generative adversarial networks (GANs) are a family of generative models...
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06/15/2017
Variational Approaches for Auto-Encoding Generative Adversarial Networks
Auto-encoding generative adversarial networks (GANs) combine the standar...
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05/30/2017
The Cramer Distance as a Solution to Biased Wasserstein Gradients
The Wasserstein probability metric has received much attention from the ...
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10/11/2016
Learning in Implicit Generative Models
Generative adversarial networks (GANs) provide an algorithmic framework ...
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06/16/2016
The Mondrian Kernel
We introduce the Mondrian kernel, a fast random feature approximation to...
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12/31/2015
Distributed Bayesian Learning with Stochastic Natural-gradient Expectation Propagation and the Posterior Server
This paper makes two contributions to Bayesian machine learning algorith...
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06/19/2015
Approximate Inference with the Variational Holder Bound
We introduce the Variational Holder (VH) bound as an alternative to Vari...
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06/11/2015
Mondrian Forests for Large-Scale Regression when Uncertainty Matters
Many real-world regression problems demand a measure of the uncertainty ...
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03/09/2015
Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages
We propose an efficient nonparametric strategy for learning a message op...
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02/16/2015
Particle Gibbs for Bayesian Additive Regression Trees
Additive regression trees are flexible non-parametric models and popular...
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04/30/2013