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stat.ML2025
A Generalization Theory for Zero-Shot Prediction
Ronak Mehta, Zaid Harchaoui
A modern paradigm for generalization in machine learning and AI consists of pre-training a task-agnostic foundation model, generally obtained using self-supervised and multimodal c…
stat.ML2025
Stochastic Gradients under Nuisances
Facheng Yu, Ronak Mehta, Alex Luedtke +1
Stochastic gradient optimization is the dominant learning paradigm for a variety of scenarios, from classical supervised learning to modern self-supervised learning. We consider st…
stat.ML2024
The Benefits of Balance: From Information Projections to Variance Reduction
Lang Liu, Ronak Mehta, Soumik Pal +1
Data balancing across multiple modalities and sources appears in various forms in foundation models in machine learning and AI, e.g. in CLIP and DINO. We show that data balancing a…