Publications associées à Convergence without Convexity: Sampling, Optimization, and Games

A first-order primal-dual method with adaptivity to local smoothness

We consider the problem of finding a saddle point for the convex-concave objective

\min_x \max_y f(x) + \langle Ax, y\rangle - g^*(y)

, where

f

is a convex function with locally Lipschitz gradient and

g

is convex and possibly non-smooth. We propose an ...

2021

Pareto-like sequential sampling heuristic for global optimisation

Katrin Beyer, Mahmoud S. M. Shaqfa

In this paper, we propose a simple global optimisation algorithm inspired by Pareto’s principle. This algorithm samples most of its solutions within prominent search domains and is equipped with a self-adaptive mechanism to control the dynamic tightening o ...

2021

Learning in Volatile Environments With the Bayes Factor Surprise

Wulfram Gerstner, Johanni Michael Brea, Alireza Modirshanechi, Vasiliki Liakoni

Surprise-based learning allows agents to rapidly adapt to nonstationary stochastic environments characterized by sudden changes. We show that exact Bayesian inference in a hierarchical model gives rise to a surprise-modulated trade-off between forgetting o ...

MIT PRESS2021

Mixed Nash Equilibria in the Adversarial Examples Game

Rafaël Benjamin Pinot

This paper tackles the problem of adversarial examples from a game theoretic point of view. We study the open question of the existence of mixed Nash equilibria in the zero-sum game formed by the attacker and the classifier. While previous works usually al ...

2021

Fast Multi-Step Critiquing for VAE-based Recommender Systems

Boi Faltings, Diego Matteo Antognini

Recent studies have shown that providing personalized explanations alongside recommendations increases trust and perceived quality. Furthermore, it gives users an opportunity to refine the recommendations by critiquing parts of the explanations. On one han ...

ASSOC COMPUTING MACHINERY2021

Convergence of adaptive algorithms for constrained weakly convex optimization

Volkan Cevher, Ahmet Alacaoglu, Yurii Malitskyi

We analyze the adaptive first order algorithm AMSGrad, for solving a constrained stochastic optimization problem with a weakly convex objective. We prove the

\tilde O(t^{-1/2})

rate of convergence for the squared norm of the gradient of Moreau envelope, ...

2021

Evading Voltage-Based Intrusion Detection on Automotive CAN

Mathias Josef Payer, Duo Xu, Rohit Bhatia

The controller area network (CAN) is widely adopted in modern automobiles to enable communications among in-vehicle electronic control units (ECUs). Lacking mainstream network security capabilities due to resource constraints, the CAN is susceptible to the ...

INTERNET SOC2021

A Reinforcement Learning Approach to Train Routing and Scheduling

Zora Oswald

Good train scheduling for a big network with many trains is very hard to achieve. As the trains are competing for the tracks with one another, the number of constraints grows rapidly. Trying to take advantage of emerging technologies in the areas of optimi ...

2020

Estimation of discrete choice models with hybrid stochastic adaptive batch size algorithms

Michel Bierlaire, Timothy Michael Hillel, Virginie Janine Camille Lurkin, Gael Lederrey

The emergence of Big Data has enabled new research perspectives in the discrete choice community. While the techniques to estimate Machine Learning models on a massive amount of data are well established, these have not yet been fully explored for the esti ...

2020

Walkman: A Communication-Efficient Random-Walk Algorithm for Decentralized Optimization

Ali H. Sayed, Kun Yuan

This paper introduces a new algorithm for consensus optimization in a multi-agent network, where all agents collaboratively find a minimizer for the sum of their private functions. All decentralized algorithms rely on communications between adjacent nodes. ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Convergence without Convexity: Sampling, Optimization, and Games

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A first-order primal-dual method with adaptivity to local smoothness

Pareto-like sequential sampling heuristic for global optimisation

Learning in Volatile Environments With the Bayes Factor Surprise

Mixed Nash Equilibria in the Adversarial Examples Game

Fast Multi-Step Critiquing for VAE-based Recommender Systems

Convergence of adaptive algorithms for constrained weakly convex optimization

Evading Voltage-Based Intrusion Detection on Automotive CAN

A Reinforcement Learning Approach to Train Routing and Scheduling

Estimation of discrete choice models with hybrid stochastic adaptive batch size algorithms

Walkman: A Communication-Efficient Random-Walk Algorithm for Decentralized Optimization

Mixed Nash Equilibria in the Adversarial Examples Game

Walkman: A Communication-Efficient Random-Walk Algorithm for Decentralized Optimization

A first-order primal-dual method with adaptivity to local smoothness

A Reinforcement Learning Approach to Train Routing and Scheduling

Fast Multi-Step Critiquing for VAE-based Recommender Systems

Learning in Volatile Environments With the Bayes Factor Surprise

Convergence of adaptive algorithms for constrained weakly convex optimization

Pareto-like sequential sampling heuristic for global optimisation

Evading Voltage-Based Intrusion Detection on Automotive CAN

Estimation of discrete choice models with hybrid stochastic adaptive batch size algorithms