6 citations · 24 across the 9 of their papers we have counts for
9 papers
Online Learning to Rank with Top-k Feedback
Sougata Chaudhuri, Ambuj Tewari
We consider two settings of online learning to rank where feedback is restricted to top ranked items. The problem is cast as an online game between a learner and sequence of users,…
Phased Exploration with Greedy Exploitation in Stochastic Combinatorial Partial Monitoring Games
Sougata Chaudhuri, Ambuj Tewari
Partial monitoring games are repeated games where the learner receives feedback that might be different from adversary's move or even the reward gained by the learner. Recently, a…
Generalization error bounds for learning to rank: Does the length of document lists matter?
Ambuj Tewari, Sougata Chaudhuri
We consider the generalization ability of algorithms for learning to rank at a query level, a problem also called subset ranking. Existing generalization error bounds necessarily d…
Online Learning to Rank with Feedback at the Top
Sougata Chaudhuri, Ambuj Tewari
We consider an online learning to rank setting in which, at each round, an oblivious adversary generates a list of documents, pertaining to a query, and the learner produces sc…
Handling Class Imbalance in Link Prediction using Learning to Rank Techniques
Bopeng Li, Sougata Chaudhuri, Ambuj Tewari
We consider the link prediction problem in a partially observed network, where the objective is to make predictions in the unobserved portion of the network. Many existing methods…
Perceptron like Algorithms for Online Learning to Rank
Sougata Chaudhuri, Ambuj Tewari
Perceptron is a classic online algorithm for learning a classification function. In this paper, we provide a novel extension of the perceptron algorithm to the learning to rank pro…