13 citations · 18 across the 6 of their papers we have counts for
5 papers · 1 filter
On Learning Mixture of Linear Regressions in the Non-Realizable Setting
Avishek Ghosh, Arya Mazumdar, Soumyabrata Pal +1
While mixture of linear regressions (MLR) is a well-studied topic, prior works usually do not analyze such models for prediction error. In fact, {\em prediction} and {\em loss} are…
Breaking the Barrier: Instance-Independent Logarithmic Regret in Stochastic Contextual Linear Bandits
Avishek Ghosh, Abishek Sankararaman
We prove an instance independent (poly) logarithmic regret for stochastic contextual bandits with linear payoff. Previously, in \cite{chu2011contextual}, a lower bound of $\mathcal…
Problem-Complexity Adaptive Model Selection for Stochastic Linear Bandits
Avishek Ghosh, Abishek Sankararaman, Kannan Ramchandran
We consider the problem of model selection for two popular stochastic linear bandit settings, and propose algorithms that adapts to the unknown problem complexity. In the first set…
Alternating Minimization Converges Super-Linearly for Mixed Linear Regression
Avishek Ghosh, Kannan Ramchandran
We address the problem of solving mixed random linear equations. We have unlabeled observations coming from multiple linear regressions, and each observation corresponds to exactly…
Max-Affine Regression: Provable, Tractable, and Near-Optimal Statistical Estimation
Avishek Ghosh, Ashwin Pananjady, Adityanand Guntuboyina +1
Max-affine regression refers to a model where the unknown regression function is modeled as a maximum of unknown affine functions for a fixed . This generalizes linea…