40 citations · 90 across the 5 of their papers we have counts for
5 papers
Structured Linear Contextual Bandits: A Sharp and Geometric Smoothed Analysis
Vidyashankar Sivakumar, Zhiwei Steven Wu, Arindam Banerjee
Bandit learning algorithms typically involve the balance of exploration and exploitation. However, in many practical applications, worst-case scenarios needing systematic explorati…
Random Quadratic Forms with Dependence: Applications to Restricted Isometry and Beyond
Arindam Banerjee, Qilong Gu, Vidyashankar Sivakumar +1
Several important families of computational and statistical results in machine learning and randomized algorithms rely on uniform bounds on quadratic forms of random vectors or mat…
Structured Stochastic Linear Bandits
Nicholas Johnson, Vidyashankar Sivakumar, Arindam Banerjee
The stochastic linear bandit problem proceeds in rounds where at each round the algorithm selects a vector from a decision set after which it receives a noisy linear loss parameter…
Estimation with Norm Regularization
Arindam Banerjee, Sheng Chen, Farideh Fazayeli +1
Analysis of non-asymptotic estimation error and structured statistical recovery based on norm regularized regression, such as Lasso, needs to consider four aspects: the norm, the l…
Multi-task Sparse Structure Learning
Andre R. Goncalves, Puja Das, Soumyadeep Chatterjee +3
Multi-task learning (MTL) aims to improve generalization performance by learning multiple related tasks simultaneously. While sometimes the underlying task relationship structure i…