4 papers
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…