Statistical Learning
GloptiNets: Scalable Non-Convex Optimization with Certificates
Non-convex optimization is a difficult and crucial task.
Testing Semantic Importance via Betting
Providing guarantees on the decision-making processes of autonomous systems, often based on complex black-box machine learning models, is paramount for their safe deployment. This need motivates efforts towards responsible artificial intelligence, which broadly entails questions of reliability, robustness, fairness, and interpretability.
Tree-Based Diffusion Schrรถdinger Bridge with Applications to Wasserstein Barycenters
While OT commonly seeks at computing the transport plan that minimizes the cost of moving between two distributions, it can naturally be extended to the multi-marginal setting (mOT) when considering several distributions.
Fantastic Weights and How to Find Them: Where to Prune in Dynamic Sparse Training
Surprisingly, we find that most of the studied methods yield similar results.
Y ouTubePD: A Multimodal Benchmark for Parkinson's Disease Analysis Andy Zhou 1 Samuel Li
The healthcare and AI communities have witnessed a growing interest in the development of AI-assisted systems for automated diagnosis of Parkinson's Disease
Federated Learning with Manifold Regularization and Normalized Update Reaggregation
Federated Learning (FL) is an emerging collaborative machine learning framework where multiple clients train the global model without sharing their own datasets.
On the role of entanglement and statistics in learning
For learning Boolean concept classes, we show that the entangled and separable sample complexity are polynomially related .