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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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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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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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12/31/2021
When are Iterative Gaussian Processes Reliably Accurate?
While recent work on conjugate gradient methods and Lanczos decompositio...
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11/12/2021
A Simple and Fast Baseline for Tuning Large XGBoost Models
XGBoost, a scalable tree boosting algorithm, has proven effective for ma...
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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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06/09/2020
Variational Auto-Regressive Gaussian Processes for Continual Learning
This paper proposes Variational Auto-Regressive Gaussian Process (VAR-GP...
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10/18/2019
First-Order Preconditioning via Hypergradient Descent
Standard gradient descent methods are susceptible to a range of issues t...
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07/25/2018
Multi-Agent Reinforcement Learning: A Report on Challenges and Approaches
Reinforcement Learning (RL) is a learning paradigm concerned with learni...
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07/18/2018
