9 papers
On-line Learning in Tree MDPs by Treating Policies as Bandit Arms
Anvay Shah, Ramsundar Anandanarayanan, Sharayu Moharir +1
A Tree Markov Decision Problem (T-MDP) is a finite-horizon MDP with a starting state , in which every state is reachable from through exactly one state-action trajec…
Fixed-Budget Constrained Best Arm Identification in Grouped Bandits
Raunak Mukherjee, Sharayu Moharir
We study fixed budget constrained best-arm identification in grouped bandits, where each arm consists of multiple independent attributes with stochastic rewards. An arm is consider…
Cascading Bandits With Feedback
R Sri Prakash, Nikhil Karamchandani, Sharayu Moharir
Motivated by the challenges of edge inference, we study a variant of the cascade bandit model in which each arm corresponds to an inference model with an associated accuracy and er…
Observation-Free Attacks on Online Learning to Rank
Sameep Chattopadhyay, Nikhil Karamchandani, Sharayu Moharir
Online learning to rank (OLTR) plays a critical role in information retrieval and machine learning systems, with a wide range of applications in search engines and content recommen…
Inference Offloading for Cost-Sensitive Binary Classification at the Edge
Vishnu Narayanan Moothedath, Umang Agarwal, Umeshraja N +3
We focus on a binary classification problem in an edge intelligence system where false negatives are more costly than false positives. The system has a compact, locally deployed mo…
Low-Regret and Low-Complexity Learning for Hierarchical Inference
Sameep Chattopadhyay, Vinay Sutar, Jaya Prakash Champati +1
This work focuses on Hierarchical Inference (HI) in edge intelligence systems, where a compact Local-ML model on an end-device works in conjunction with a high-accuracy Remote-ML m…
Fixed-Confidence Best Arm Identification with Decreasing Variance
Tamojeet Roychowdhury, Kota Srinivas Reddy, Krishna P Jagannathan +1
We focus on the problem of best-arm identification in a stochastic multi-arm bandit with temporally decreasing variances for the arms' rewards. We model arm rewards as Gaussian ran…
Constrained Best Arm Identification in Grouped Bandits
Sahil Dharod, Malyala Preethi Sravani, Sakshi Heda +1
We study a grouped bandit setting where each arm comprises multiple independent sub-arms referred to as attributes. Each attribute of each arm has an independent stochastic reward.…
Influencing Bandits: Arm Selection for Preference Shaping
Viraj Nadkarni, D. Manjunath, Sharayu Moharir
We consider a non stationary multi-armed bandit in which the population preferences are positively and negatively reinforced by the observed rewards. The objective of the algorithm…