46 citations · 165 across the 45 of their papers we have counts for
9 papers · 1 filter
Maximizing Use-Case Specificity through Precision Model Tuning
Pranjali Awasthi, David Recio-Mitter, Yosuke Kyle Sugi
Language models have become increasingly popular in recent years for tasks like information retrieval. As use-cases become oriented toward specific domains, fine-tuning becomes def…
On the Adversarial Robustness of Mixture of Experts
Joan Puigcerver, Rodolphe Jenatton, Carlos Riquelme +2
Adversarial robustness is a key desirable property of neural networks. It has been empirically shown to be affected by their sizes, with larger networks being typically more robust…
Agnostic Learning of General ReLU Activation Using Gradient Descent
Pranjal Awasthi, Alex Tang, Aravindan Vijayaraghavan
We provide a convergence analysis of gradient descent for the problem of agnostically learning a single ReLU function with moderate bias under Gaussian distributions. Unlike prior…
Individual Preference Stability for Clustering
Saba Ahmadi, Pranjal Awasthi, Samir Khuller +4
In this paper, we propose a natural notion of individual preference (IP) stability for clustering, which asks that every data point, on average, is closer to the points in its own…
Do More Negative Samples Necessarily Hurt in Contrastive Learning?
Pranjal Awasthi, Nishanth Dikkala, Pritish Kamath
Recent investigations in noise contrastive estimation suggest, both empirically as well as theoretically, that while having more "negative samples" in the contrastive loss improves…
Trimmed Maximum Likelihood Estimation for Robust Learning in Generalized Linear Models
Pranjal Awasthi, Abhimanyu Das, Weihao Kong +1
We study the problem of learning generalized linear models under adversarial corruptions. We analyze a classical heuristic called the iterative trimmed maximum likelihood estimator…