157 citations · 529 across the 78 of their papers we have counts for
15 papers · 1 filter
Self-Play Q-learners Can Provably Collude in the Iterated Prisoner's Dilemma
Quentin Bertrand, Juan Duque, Emilio Calvano +1
A growing body of computational studies shows that simple machine learning agents converge to cooperative behaviors in social dilemmas, such as collusive price-setting in oligopoly…
Adversarial Attacks and Defenses in Large Language Models: Old and New Threats
Leo Schwinn, David Dobre, Stephan Günnemann +1
Over the past decade, there has been extensive research aimed at enhancing the robustness of neural networks, yet this problem remains vastly unsolved. Here, one major impediment h…
Proving Linear Mode Connectivity of Neural Networks via Optimal Transport
Damien Ferbach, Baptiste Goujaud, Gauthier Gidel +1
The energy landscape of high-dimensional non-convex optimization problems is crucial to understanding the effectiveness of modern deep neural network architectures. Recent works ha…
A Persuasive Approach to Combating Misinformation
Safwan Hossain, Andjela Mladenovic, Yiling Chen +1
Bayesian Persuasion is proposed as a tool for social media platforms to combat the spread of misinformation. Since platforms can use machine learning to predict the popularity and…
Expected flow networks in stochastic environments and two-player zero-sum games
Marco Jiralerspong, Bilun Sun, Danilo Vucetic +4
Generative flow networks (GFlowNets) are sequential sampling models trained to match a given distribution. GFlowNets have been successfully applied to various structured object gen…
High-Probability Convergence for Composite and Distributed Stochastic Minimization and Variational Inequalities with Heavy-Tailed Noise
Eduard Gorbunov, Abdurakhmon Sadiev, Marina Danilova +5
High-probability analysis of stochastic first-order optimization methods under mild assumptions on the noise has been gaining a lot of attention in recent years. Typically, gradien…
On the Stability of Iterative Retraining of Generative Models on their own Data
Quentin Bertrand, Avishek Joey Bose, Alexandre Duplessis +2
Deep generative models have made tremendous progress in modeling complex data, often exhibiting generation quality that surpasses a typical human's ability to discern the authentic…
AI4GCC -- Track 3: Consumption and the Challenges of Multi-Agent RL
Marco Jiralerspong, Gauthier Gidel
The AI4GCC competition presents a bold step forward in the direction of integrating machine learning with traditional economic policy analysis. Below, we highlight two potential ar…
Omega: Optimistic EMA Gradients
Juan Ramirez, Rohan Sukumaran, Quentin Bertrand +1
Stochastic min-max optimization has gained interest in the machine learning community with the advancements in GANs and adversarial training. Although game optimization is fairly w…
Raising the Bar for Certified Adversarial Robustness with Diffusion Models
Thomas Altstidl, David Dobre, Björn Eskofier +2
Certified defenses against adversarial attacks offer formal guarantees on the robustness of a model, making them more reliable than empirical methods such as adversarial training,…
Synaptic Weight Distributions Depend on the Geometry of Plasticity
Roman Pogodin, Jonathan Cornford, Arna Ghosh +3
A growing literature in computational neuroscience leverages gradient descent and learning algorithms that approximate it to study synaptic plasticity in the brain. However, the va…
Sarah Frank-Wolfe: Methods for Constrained Optimization with Best Rates and Practical Features
Aleksandr Beznosikov, David Dobre, Gauthier Gidel
The Frank-Wolfe (FW) method is a popular approach for solving optimization problems with structured constraints that arise in machine learning applications. In recent years, stocha…
Performative Prediction with Neural Networks
Mehrnaz Mofakhami, Ioannis Mitliagkas, Gauthier Gidel
Performative prediction is a framework for learning models that influence the data they intend to predict. We focus on finding classifiers that are performatively stable, i.e. opti…
Feature Likelihood Divergence: Evaluating the Generalization of Generative Models Using Samples
Marco Jiralerspong, Avishek Joey Bose, Ian Gemp +3
The past few years have seen impressive progress in the development of deep generative models capable of producing high-dimensional, complex, and photo-realistic data. However, cur…
High-Probability Bounds for Stochastic Optimization and Variational Inequalities: the Case of Unbounded Variance
Abdurakhmon Sadiev, Marina Danilova, Eduard Gorbunov +5
During recent years the interest of optimization and machine learning communities in high-probability convergence of stochastic optimization methods has been growing. One of the ma…