160 citations · 1.5k across the 122 of their papers we have counts for
7 papers · 1 filter
Fighting Bandits with a New Kind of Smoothness
Jacob Abernethy, Chansoo Lee, Ambuj Tewari
We define a novel family of algorithms for the adversarial multi-armed bandit problem, and provide a simple analysis technique based on convex smoothing. We prove two main results.…
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…
Spectral Smoothing via Random Matrix Perturbations
Jacob Abernethy, Chansoo Lee, Ambuj Tewari
We consider stochastic smoothing of spectral functions of matrices using perturbations commonly studied in random matrix theory. We show that a spectral function remains spectral w…
Consistent Algorithms for Multiclass Classification with a Reject Option
Harish G. Ramaswamy, Ambuj Tewari, Shivani Agarwal
We consider the problem of -class classification (), where the classifier can choose to abstain from making predictions at a given cost, say, a factor of the cost o…
Sample Size Calculations for Micro-randomized Trials in mHealth
Peng Liao, Predrag Klasnja, Ambuj Tewari +1
The use and development of mobile interventions are experiencing rapid growth. In "just-in-time" mobile interventions, treatments are provided via a mobile device and they are inte…