35 citations · 91 across the 22 of their papers we have counts for
23 papers
Neural Parameter Calibration for Finite-State Mean Field Games
Anna C. M. Thöni, Grégoire Lambrecht, Gökçe Dayanıklı +3
Mean field games efficiently approximate a very large population of strategic agents. While these games can aid the understanding of complex systems, their deployment in real-world…
MINDGAMES: A Live Arena for Evaluating Social and Strategic Reasoning in Multi-Agent LLMs
Kevin Wang, Anna Thöni, Benjamin Kempinski +50
Large language models (LLMs) are increasingly deployed as interactive agents, yet their capacity for social and strategic reasoning over extended interaction remains poorly underst…
David vs. Goliath: Verifiable Agent-to-Agent Jailbreaking via Reinforcement Learning
Samuel Nellessen, Tal Kachman
The evolution of large language models into autonomous agents introduces adversarial failures that exploit legitimate tool privileges, transforming safety evaluation in tool-augmen…
Evaluating Artificial Intelligence Algorithms for the Standardization of Transtibial Prosthetic Socket Shape Design
C. H. E. Jordaan, M. van der Stelt, T. J. J. Maal +4
The quality of a transtibial prosthetic socket depends on the prosthetist's skills and expertise, as the fitting is performed manually. This study investigates multiple artificial…
Neural Mean-Field Games: Extending Mean-Field Game Theory with Neural Stochastic Differential Equations
Anna C. M. Thöni, Yoram Bachrach, Tal Kachman
Mean-field game theory relies on approximating games that are intractable to model due to a very large to infinite population of players. While these kinds of games can be solved a…
InfluenceNet: AI Models for Banzhaf and Shapley Value Prediction
Benjamin Kempinski, Tal Kachman
Power indices are essential in assessing the contribution and influence of individual agents in multi-agent systems, providing crucial insights into collaborative dynamics and deci…
Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations
Anna C. M. Thöni, William E. Robinson, Yoram Bachrach +2
In chemical reaction network theory, ordinary differential equations are used to model the temporal change of chemical species concentration. As the functional form of these ordina…
Using Cooperative Game Theory to Prune Neural Networks
Mauricio Diaz-Ortiz, Benjamin Kempinski, Daphne Cornelisse +2
We show how solution concepts from cooperative game theory can be used to tackle the problem of pruning neural networks. The ever-growing size of deep neural networks (DNNs) increa…
Generating and Imputing Tabular Data via Diffusion and Flow-based Gradient-Boosted Trees
Alexia Jolicoeur-Martineau, Kilian Fatras, Tal Kachman
Tabular data is hard to acquire and is subject to missing values. This paper introduces a novel approach for generating and imputing mixed-type (continuous and categorical) tabular…
Explainability Techniques for Chemical Language Models
Stefan Hödl, William Robinson, Yoram Bachrach +2
Explainability techniques are crucial in gaining insights into the reasons behind the predictions of deep learning models, which have not yet been applied to chemical language mode…
Charting the Topography of the Neural Network Landscape with Thermal-Like Noise
Theo Jules, Gal Brener, Tal Kachman +2
The training of neural networks is a complex, high-dimensional, non-convex and noisy optimization problem whose theoretical understanding is interesting both from an applicative pe…
Diffusion models with location-scale noise
Alexia Jolicoeur-Martineau, Kilian Fatras, Ke Li +1
Diffusion Models (DMs) are powerful generative models that add Gaussian noise to the data and learn to remove it. We wanted to determine which noise distribution (Gaussian or non-G…
Safe Reinforcement Learning From Pixels Using a Stochastic Latent Representation
Yannick Hogewind, Thiago D. Simao, Tal Kachman +1
We address the problem of safe reinforcement learning from pixel observations. Inherent challenges in such settings are (1) a trade-off between reward optimization and adhering to…
Neural Payoff Machines: Predicting Fair and Stable Payoff Allocations Among Team Members
Daphne Cornelisse, Thomas Rood, Mateusz Malinowski +2
In many multi-agent settings, participants can form teams to achieve collective outcomes that may far surpass their individual capabilities. Measuring the relative contributions of…
Lyapunov Exponents for Diversity in Differentiable Games
Jonathan Lorraine, Paul Vicol, Jack Parker-Holder +3
Ridge Rider (RR) is an algorithm for finding diverse solutions to optimization problems by following eigenvectors of the Hessian ("ridges"). RR is designed for conservative gradien…
Anomalous Diffusion: Fractional Brownian Motion vs. Fractional Ito Motion
Iddo Eliazar, Tal Kachman
Generalizing Brownian motion (BM), fractional Brownian motion (FBM) is a paradigmatic selfsimilar model for anomalous diffusion. Specifically, varying its Hurst exponent, FBM spans…
Gradients are Not All You Need
Luke Metz, C. Daniel Freeman, Samuel S. Schoenholz +1
Differentiable programming techniques are widely used in the community and are responsible for the machine learning renaissance of the past several decades. While these methods are…
Gotta Go Fast When Generating Data with Score-Based Models
Alexia Jolicoeur-Martineau, Ke Li, Rémi Piché-Taillefer +2
Score-based (denoising diffusion) generative models have recently gained a lot of success in generating realistic and diverse data. These approaches define a forward diffusion proc…
Novel Uncertainty Framework for Deep Learning Ensembles
Tal Kachman, Michal Moshkovitz, Michal Rosen-Zvi
Deep neural networks have become the default choice for many of the machine learning tasks such as classification and regression. Dropout, a method commonly used to improve the con…
Learning multiple non-mutually-exclusive tasks for improved classification of inherently ordered labels
Vadim Ratner, Yoel Shoshan, Tal Kachman
Medical image classification involves thresholding of labels that represent malignancy risk levels. Usually, a task defines a single threshold, and when developing computer-aided d…
Numerical implementation of the multiscale and averaging methods for quasi periodic systems
Tal Kachman, Shmuel Fishman, Avy Soffer
We consider the problem of numerically solving the Schrödinger equation with a potential that is quasi periodic in space and time. We introduce a numerical scheme based on a newly…
Dynamics of a Classical Particle in a Quasi Periodic Potential
Yaniv Tenenbaum Katan, Tal Kachman, Shmuel Fishman +1
We study the dynamics of a one-dimensional classical particle in a space and time dependent potential with randomly chosen parameters. The focus of this work is a quasi-periodic po…
Computer vision-based recognition of liquid surfaces and phase boundaries in transparent vessels, with emphasis on chemistry applications
Sagi Eppel, Tal Kachman
The ability to recognize the liquid surface and the liquid level in transparent containers is perhaps the most commonly used evaluation method when dealing with fluids. Such recogn…