270 citations · 1.4k across the 97 of their papers we have counts for
13 papers · 1 filter
Hamiltonian Dynamics with Non-Newtonian Momentum for Rapid Sampling
Greg Ver Steeg, Aram Galstyan
Sampling from an unnormalized probability distribution is a fundamental problem in machine learning with applications including Bayesian modeling, latent factor inference, and ener…
Failure Modes of Domain Generalization Algorithms
Tigran Galstyan, Hrayr Harutyunyan, Hrant Khachatrian +2
Domain generalization algorithms use training data from multiple domains to learn models that generalize well to unseen domains. While recently proposed benchmarks demonstrate that…
Implicit SVD for Graph Representation Learning
Sami Abu-El-Haija, Hesham Mostafa, Marcel Nassar +3
Recent improvements in the performance of state-of-the-art (SOTA) methods for Graph Representational Learning (GRL) have come at the cost of significant computational resource requ…
Information-theoretic generalization bounds for black-box learning algorithms
Hrayr Harutyunyan, Maxim Raginsky, Greg Ver Steeg +1
We derive information-theoretic generalization bounds for supervised learning algorithms based on the information contained in predictions rather than in the output of the training…
Attributing Fair Decisions with Attention Interventions
Ninareh Mehrabi, Umang Gupta, Fred Morstatter +2
The widespread use of Artificial Intelligence (AI) in consequential domains, such as healthcare and parole decision-making systems, has drawn intense scrutiny on the fairness of th…
Secure Neuroimaging Analysis using Federated Learning with Homomorphic Encryption
Dimitris Stripelis, Hamza Saleem, Tanmay Ghai +8
Federated learning (FL) enables distributed computation of machine learning models over various disparate, remote data sources, without requiring to transfer any individual data to…