5 papers
Optimal Multiclass Online Regret with Arbitrary Label Spaces
Sajad Ashkezari, Shai Ben-David
We determine the optimal regret in agnostic multiclass online learning with arbitrary, possibly infinite, label spaces. For every hypothesis class of multiclass Littlestone dimensi…
Robust Online Learning
Sajad Ashkezari
We study the problem of learning robust classifiers where the classifier will receive a perturbed input. Unlike robust PAC learning studied in prior work, here the clean data and i…
Online Learning with Improving Agents: Multiclass, Budgeted Agents and Bandit Learners
Sajad Ashkezari, Shai Ben-David
We investigate the recently introduced model of learning with improvements, where agents are allowed to make small changes to their feature values to be warranted a more desirable…
Open-PMC-18M: A High-Fidelity Large Scale Medical Dataset for Multimodal Representation Learning
Negin Baghbanzadeh, Mohammed Saidul Islam, Sajad Ashkezari +2
In biomedical vision-language modeling, datasets are typically mined from scientific literature, pairing compound figures with captions that are short, context-dependent, and ofter…
Advancing Medical Representation Learning Through High-Quality Data
Negin Baghbanzadeh, Adibvafa Fallahpour, Yasaman Parhizkar +8
Despite the growing scale of medical Vision-Language datasets, the impact of dataset quality on model performance remains under-explored. We introduce Open-PMC, a high-quality medi…