Statistical Learning
Human Expertise in Algorithmic Prediction
Algorithmic indistinguishability yields a natural test for assessing whether experts incorporate this kind of "side
Learning the Infinitesimal Generator of Stochastic Diffusion Processes Vladimir R. Kostic CSML, Istituto Italiano di Tecnologia University of Novi Sad
We address data-driven learning of the infinitesimal generator of stochastic diffusion processes, essential for understanding numerical simulations of natural and physical systems.
Graph Classification via Reference Distribution Learning: Theory and Practice
This work introduces Graph Reference Distribution Learning (GRDL), an efficient and accurate graph classification method. GRDL treats each graph's latent node embeddings given by GNN layers as a
Confusion-Resistant Federated Learning via Diffusion-Based Data Harmonization on Non-IID Data Xiaohong Chen
Federated learning has become a pivotal distributed learning paradigm, involving collaborative model updates across multiple nodes with private data.
Task-oriented Time Series Imputation Evaluation via Generalized Representers Zhixian Wang 1,2, Linxiao Y ang
Specifically, we summarize our contributions into the following points.
dattri: A Library for Efficient Data Attribution Junwei Deng 1* Ting-Wei Li
Data attribution methods aim to quantify the influence of individual training samples on the prediction of artificial intelligence (AI) models.
Sharpness-diversity tradeoff: improving flat ensembles with SharpBalance
Building on this, our study investigates the interplay between sharpness and diversity within deep ensembles, illustrating their crucial role in robust generalization to both in-distribution (ID) and out-of-distribution (OOD) data.