Publications related to Stability (learning theory)

Subquadratic Overparameterization for Shallow Neural Networks

Volkan Cevher, Thomas Michaelsen Pethick, Armin Eftekhari, Chaehwan Song

Overparameterization refers to the important phenomenon where the width of a neural network is chosen such that learning algorithms can provably attain zero loss in nonconvex training. The existing theory establishes such global convergence using various i ...

2021

Adaptation and Learning Over Networks Under Subspace Constraints & x2014;Part II: Performance Analysis

Ali H. Sayed, Stefan Vlaski, Roula Nassif

Part & x00A0;I of this paper considered optimization problems over networks where agents have individual objectives to meet, or individual parameter vectors to estimate, subject to subspace constraints that require the objectives across the network to lie ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

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

Idiap and UAM Participation at MEX-A3T Evaluation Campaign

Petr Motlicek

This paper describes our participation in the shared evaluation campaign of MexA3T 2020. Our main goal wasto evaluate a Supervised Autoencoder (SAE) learning algorithm in text classification tasks. For our experiments,we used three different sets of featur ...

CEUR Workshop Proceedings2020

Systematic sensor placement for structural anomaly detection in the absence of damaged states

Jan Sickmann Hesthaven, Caterina Bigoni, Zhenying Zhang

In structural health monitoring (SHM), risk assessment and decision strategies rely primarily on sensor responses. Simulated data can be generated to emulate the monitoring phenomena under different natural operational and environmental conditions in order ...

2020

Supplementary Material - AL2: Progressive Activation Loss for Learning General Representations in Classification Neural Networks

Sabine Süsstrunk, Majed El Helou, Frederike Dümbgen

In this supplementary material, we present the details of the neural network architecture and training settings used in all our experiments. This holds for all experiments presented in the main paper as well as in this supplementary material. We also show ...

2020

Strengthened Information-theoretic Bounds on the Generalization Error

Michael Christoph Gastpar, Amedeo Roberto Esposito, Ibrahim Issa

The following problem is considered: given a joint distribution P XY and an event E, bound P XY (E) in terms of P X P Y (E) (where P X P Y is the product of the marginals of P XY ) and a measure of dependence of X and Y. Such bounds have direct application ...

IEEE2019

A Generalized Representer Theorem for Hilbert Space - Valued Functions

Colin Neil Jones, Sanket Sanjay Diwale

The necessary and sufficient conditions for existence of a generalized representer theorem are presented for learning Hilbert space - valued functions. Representer theorems involving explicit basis functions and Reproducing Kernels are a common occurrence ...

2019

Crowdsourcing with Fairness, Diversity and Budget Constraints

Boi Faltings, Naman Goel

Recent studies have shown that the labels collected from crowdworkers can be discriminatory with respect to sensitive attributes such as gender and race. This raises questions about the suitability of using crowdsourced data for further use, such as for tr ...

ASSOC COMPUTING MACHINERY2019

Learning and Adaptive Data Analysis via Maximal Leakage

Michael Christoph Gastpar, Amedeo Roberto Esposito, Ibrahim Issa

There has been growing interest in studying connections between generalization error of learning algorithms and information measures. In this work, we generalize a result that employs the maximal leakage, a measure of leakage of information, and explore ho ...

IEEE2019

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Subquadratic Overparameterization for Shallow Neural Networks

Adaptation and Learning Over Networks Under Subspace Constraints & x2014;Part II: Performance Analysis

Robust Generalization via $\alpha$-Mutual Information

Idiap and UAM Participation at MEX-A3T Evaluation Campaign

Systematic sensor placement for structural anomaly detection in the absence of damaged states

Supplementary Material - AL2: Progressive Activation Loss for Learning General Representations in Classification Neural Networks

Strengthened Information-theoretic Bounds on the Generalization Error

A Generalized Representer Theorem for Hilbert Space - Valued Functions

Crowdsourcing with Fairness, Diversity and Budget Constraints

Learning and Adaptive Data Analysis via Maximal Leakage

Robust Generalization via $\alpha$-Mutual Information

Supplementary Material - AL2: Progressive Activation Loss for Learning General Representations in Classification Neural Networks

Strengthened Information-theoretic Bounds on the Generalization Error

Learning and Adaptive Data Analysis via Maximal Leakage

Systematic sensor placement for structural anomaly detection in the absence of damaged states

Idiap and UAM Participation at MEX-A3T Evaluation Campaign

A Generalized Representer Theorem for Hilbert Space - Valued Functions

Adaptation and Learning Over Networks Under Subspace Constraints & x2014;Part II: Performance Analysis

Crowdsourcing with Fairness, Diversity and Budget Constraints

Subquadratic Overparameterization for Shallow Neural Networks