Beyond Time-Average Convergence: Near-Optimal Uncoupled Online Learning via Clairvoyant Multiplicative Weights Update

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We study an energy market composed of producers who compete to supply energy to different markets and want to maximize their profits. The energy market is modeled by a graph representing a constrained power network where nodes represent the markets and lin ...

The goal of this thesis is to study continuous-domain inverse problems for the reconstruction of sparse signals and to develop efficient algorithms to solve such problems computationally. The task is to recover a signal of interest as a continuous function ...

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 ...

Oligopolistic competition occurs in various transportation markets. In this paper, we introduce a framework to find approximate equilibrium solutions of oligopolistic markets in which demand is modeled at the disaggregate level using discrete choice models ...

Visual Focus of Attention (VFOA) estimation in conversation is challenging as it relies on difficult to estimate information (gaze) combined with scene features like target positions and other contextual information (speaking status) allowing to disambigua ...

We study a stochastic program where the probability distribution of the uncertain problem parameters is unknown and only indirectly observed via finitely many correlated samples generated by an unknown Markov chain with d states. We propose a data-driven d ...

The celebrated Kelly betting strategy guarantees, with probability one, higher long-run wealth than any other causal investment strategy. However, on the way to its long-term supremacy, this strategy has a notable downfall: it typically displays high varia ...

We study a robust auction design problem with a minimax regret objective, where a seller seeks a mechanism for selling multiple items to multiple anonymous bidders with additive values. The seller knows that the bidders' values range over a box uncertainty ...

We study a robust monopoly pricing problem with a minimax regret objective, where a seller endeavors to sell multiple goods to a single buyer, only knowing that the buyer's values for the goods range over a rectangular uncertainty set. We interpret this pr ...

The objective of this thesis is to provide a mathematical and computational framework for the proactive maintenance of complex systems with a particular application to structural health monitoring (SHM). SHM techniques rely primarily on sensor responses to ...

Many modern services need to routinely perform tasks on a large scale. This prompts us to consider the following question: How can we design efficient algorithms for large-scale computation? In this thesis, we focus on devising a general strategy to addr ...

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. ...

Humans are comparison machines: comparing and choosing an item among a set of alternatives (such as objects or concepts) is arguably one of the most natural ways for us to express our preferences and opinions. In many applications, the analysis of data con ...

The wide adoption of DNNs has given birth to unrelenting computing requirements, forcing datacenter operators to adopt domain-specific accelerators to train them. These accelerators typically employ densely packed full-precision floating-point arithmetic t ...

We reconsider the training objective of Generative Adversarial Networks (GANs) from the mixed Nash Equilibria (NE) perspective. Inspired by the classical prox methods, we develop a novel algorithmic framework for GANs via an infinite-dimensional two-player ...

To quantify a player's commitment in a given Nash equilibrium of a finite dynamic game, we map the corresponding normal-form game to a "canonical extension," which allows each player to adjust his or her move with a certain probability. The commitment meas ...

Oligopolistic competition occurs often in transportation as well as in other markets due to reasons such as barriers to entry, limited capacity of the infrastructure and external regulations. In transport oligopolies, suppliers are profit maximizers and ta ...

We study minimal single-task peer prediction mechanisms that have limited knowledge about agents' beliefs. Without knowing what agents' beliefs are or eliciting additional information, it is not possible to design a truthful mechanism in a Bayesian-Nash se ...

This paper proposes a set of distributed real-time optimisation schemes for the steady-state optimisation of parametrically and structurally uncertain systems that are composed of multiple interconnected subsystems. These schemes are derived in the framewo ...

Optimization is a fundamental tool in modern science. Numerous important tasks in biology, economy, physics and computer science can be cast as optimization problems. Consider the example of machine learning: recent advances have shown that even the most s ...

Beyond Time-Average Convergence: Near-Optimal Uncoupled Online Learning via Clairvoyant Multiplicative Weights Update

Chattez avec Graph Search

Equilibria in Network Constrained Energy Markets

Variational Methods For Continuous-Domain Inverse Problems: the Quest for the Sparsest Solution

Mixed Nash Equilibria in the Adversarial Examples Game

A Simulation-Based Heuristic to Find Approximate Equilibria with Disaggregate Demand Models

Visual Focus of Attention Estimation in 3D Scene with an Arbitrary Number of Targets

Distributionally Robust Optimization with Markovian Data

On Risk Reduction in Kelly Betting Using the Conservative Expected Value

Regret Minimization and Separation in Multi-Bidder Multi-Item Auctions

Robust Multidimensional Pricing: Separation without Regret

Numerical methods for structural anomaly detection using model order reduction and data-driven techniques

When Stuck, Flip a Coin : New Algorithms for Large-Scale Tasks

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

Efficient Learning from Comparisons

Training DNNs with Hybrid Block Floating Point

Finding Mixed Nash Equilibria of Generative Adversarial Networks

Quantifying Commitment in Nash Equilibria

A demand-based optimization approach to find market equilibria in oligopolies

Partial Truthfulness in Minimal Peer Prediction Mechanisms with Limited Knowledge

Distributed modifier-adaptation schemes for the real-time optimisation of uncertain interconnected systems

New Algorithmic Paradigms for Discrete Problems using Dynamical Systems and Polynomials