Sanyam Kapoor | DeepAI

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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...

0 Sanae Lotfi, et al. ∙

∙ 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. ∙

∙ 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. ∙

∙ 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. ∙

∙ 11/12/2021

A Simple and Fast Baseline for Tuning Large XGBoost Models

XGBoost, a scalable tree boosting algorithm, has proven effective for ma...

0 Sanyam Kapoor, et al. ∙

∙ 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. ∙

∙ 06/09/2020

Variational Auto-Regressive Gaussian Processes for Continual Learning

This paper proposes Variational Auto-Regressive Gaussian Process (VAR-GP...

0 Sanyam Kapoor, et al. ∙

∙ 10/18/2019

First-Order Preconditioning via Hypergradient Descent

Standard gradient descent methods are susceptible to a range of issues t...

22 Ted Moskovitz, et al. ∙

∙ 07/25/2018

Multi-Agent Reinforcement Learning: A Report on Challenges and Approaches

Reinforcement Learning (RL) is a learning paradigm concerned with learni...

0 Sanyam Kapoor, et al. ∙

∙ 07/18/2018

Backplay: "Man muss immer umkehren"

A long-standing problem in model free reinforcement learning (RL) is tha...

2 Cinjon Resnick, et al. ∙

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