38 citations · 90 across the 10 of their papers we have counts for
6 papers · 1 filter
The Interplay Between Stability and Regret in Online Learning
Ankan Saha, Prateek Jain, Ambuj Tewari
This paper considers the stability of online learning algorithms and its implications for learnability (bounded regret). We introduce a novel quantity called {\em forward regret} t…
Smoothing Multivariate Performance Measures
Xinhua Zhang, Ankan Saha, S. V. N. Vishwanatan
A Support Vector Method for multivariate performance measures was recently introduced by Joachims (2005). The underlying optimization problem is currently solved using cutting plan…
Regularized Risk Minimization by Nesterov's Accelerated Gradient Methods: Algorithmic Extensions and Empirical Studies
Xinhua Zhang, Ankan Saha, S. V. N. Vishwanathan
Nesterov's accelerated gradient methods (AGM) have been successfully applied in many machine learning areas. However, their empirical performance on training max-margin models has…
On the Finite Time Convergence of Cyclic Coordinate Descent Methods
Ankan Saha, Ambuj Tewari
Cyclic coordinate descent is a classic optimization method that has witnessed a resurgence of interest in machine learning. Reasons for this include its simplicity, speed and stabi…
Faster Rates for training Max-Margin Markov Networks
Xinhua Zhang, Ankan Saha, S. V. N. Vishwanathan
Structured output prediction is an important machine learning problem both in theory and practice, and the max-margin Markov network (\mcn) is an effective approach. All state-of-t…
Lower Bounds for BMRM and Faster Rates for Training SVMs
Ankan Saha, Xinhua Zhang, S. V. N. Vishwanathan
Regularized risk minimization with the binary hinge loss and its variants lies at the heart of many machine learning problems. Bundle methods for regularized risk minimization (BMR…