Publications related to Probably approximately correct learning

Landscape and training regimes in deep learning

Matthieu Wyart, Mario Geiger, Leonardo Petrini

Deep learning algorithms are responsible for a technological revolution in a variety oftasks including image recognition or Go playing. Yet, why they work is not understood.Ultimately, they manage to classify data lying in high dimension – a feat generical ...

2021

Generalization Error Bounds Via Rényi-, f-Divergences and Maximal Leakage

Michael Christoph Gastpar, Amedeo Roberto Esposito, Ibrahim Issa

In this work, the probability of an event under some joint distribution is bounded by measuring it with the product of the marginals instead (which is typically easier to analyze) together with a measure of the dependence between the two random variables. ...

2021

Robust Generalization via $\alpha$-Mutual Information

Michael Christoph Gastpar, Amedeo Roberto Esposito, Ibrahim Issa

The aim of this work is to provide bounds connecting two probability measures of the same event using Rényi

\alpha

-Divergences and Sibson’s

\alpha

-Mutual Information, a generalization of respectively the Kullback-Leibler Divergence and Shannon’s Mutual ...

ETHZ2020

Efficiently Learning Fourier Sparse Set Functions

Andreas Krause, Mikhail Kapralov, Amir Zandieh

Learning set functions is a key challenge arising in many domains, ranging from sketching graphs to black-box optimization with discrete parameters. In this paper we consider the problem of efficiently learning set functions that are defined over a ground ...

NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)2019

Solvent fluctuations and nuclear quantum effects modulate the molecular hyperpolarizability of water

Michele Ceriotti, Sylvie Roke, David Mark Wilkins, Andrea Grisafi, Gabriele Tocci, Chung-Wen Liang

Second-harmonic scattering (SHS) experiments provide a unique approach to probe noncentrosymmetric environments in aqueous media, from bulk solutions to interfaces, living cells, and tissue. A central assumption made in analyzing SHS experiments is that ea ...

Amer Physical Soc2017

ABC Reinforcement Learning

Christos Dimitrakakis, Nikolaos Tziortziotis

This paper introduces a simple, general framework for likelihood-free Bayesian reinforcement learning, through Approximate Bayesian Computation (ABC). The main advantage is that we only require a prior distribution on a class of simulators (generative mode ...

2013

PAC-Bayesian Generalization Error Bounds for Gaussian Process Classification

Matthias Seeger

Approximate Bayesian Gaussian process (GP) classification techniques are powerful non-parametric learning methods, similar in appearance and performance to support vector machines. Based on simple probabilistic models, they render interpretable results and ...

Massachusetts Institute of Technology Press2003

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Robust Generalization via $\alpha$-Mutual Information

Michael Christoph Gastpar, Amedeo Roberto Esposito, Ibrahim Issa

The aim of this work is to provide bounds connecting two probability measures of the same event using Rényi

\alpha

-Divergences and Sibson’s

\alpha

-Mutual Information, a generalization of respectively the Kullback-Leibler Divergence and Shannon’s Mutual ...

ETHZ2020