160 citations · 1.5k across the 122 of their papers we have counts for
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On Iterative Hard Thresholding Methods for High-dimensional M-Estimation
Prateek Jain, Ambuj Tewari, Purushottam Kar
The use of M-estimators in generalized linear regression models in high dimensional settings requires risk minimization with hard constraints. Of the known methods, the class…
Online Ranking with Top-1 Feedback
Sougata Chaudhuri, Ambuj Tewari
We consider a setting where a system learns to rank a fixed set of items. The goal is produce good item rankings for users with diverse interests who interact online with the s…
Online Linear Optimization via Smoothing
Jacob Abernethy, Chansoo Lee, Abhinav Sinha +1
We present a new optimization-theoretic approach to analyzing Follow-the-Leader style algorithms, particularly in the setting where perturbations are used as a tool for regularizat…
Perceptron-like Algorithms and Generalization Bounds for Learning to Rank
Sougata Chaudhuri, Ambuj Tewari
Learning to rank is a supervised learning problem where the output space is the space of rankings but the supervision space is the space of relevance scores. We make theoretical co…
On Lipschitz Continuity and Smoothness of Loss Functions in Learning to Rank
Ambuj Tewari, Sougata Chaudhuri
In binary classification and regression problems, it is well understood that Lipschitz continuity and smoothness of the loss function play key roles in governing generalization err…