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08/01/2023
Sharp Taylor Polynomial Enclosures in One Dimension
It is often useful to have polynomial upper or lower bounds on a one-dim...
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05/23/2023
Federated Variational Inference: Towards Improved Personalization and Generalization
Conventional federated learning algorithms train a single global model b...
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12/22/2022
Automatically Bounding the Taylor Remainder Series: Tighter Bounds and New Applications
We present a new algorithm for automatically bounding the Taylor remaind...
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11/18/2022
Weighted Ensemble Self-Supervised Learning
Ensembling has proven to be a powerful technique for boosting model perf...
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11/17/2020
VIB is Half Bayes
In discriminative settings such as regression and classification there a...
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10/19/2020
PAC^m-Bayes: Narrowing the Empirical Risk Gap in the Misspecified Bayesian Regime
While the decision-theoretic optimality of the Bayesian formalism under ...
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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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03/03/2020
Automatic Differentiation Variational Inference with Mixtures
Automatic Differentiation Variational Inference (ADVI) is a useful tool ...
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02/07/2020
The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks
Variational Bayesian Inference is a popular methodology for approximatin...
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02/04/2020
tfp.mcmc: Modern Markov Chain Monte Carlo Tools Built for Modern Hardware
Markov chain Monte Carlo (MCMC) is widely regarded as one of the most im...
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01/22/2020
Joint Distributions for TensorFlow Probability
A central tenet of probabilistic programming is that a model is specifie...
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01/14/2020
Hydra: Preserving Ensemble Diversity for Model Distillation
Ensembles of models have been empirically shown to improve predictive pe...
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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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03/09/2019
NeuTra-lizing Bad Geometry in Hamiltonian Monte Carlo Using Neural Transport
Hamiltonian Monte Carlo is a powerful algorithm for sampling from diffic...
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07/02/2018
Uncertainty in the Variational Information Bottleneck
We present a simple case study, demonstrating that Variational Informati...
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11/28/2017
TensorFlow Distributions
The TensorFlow Distributions library implements a vision of probability ...
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11/01/2017