7 citations · 18 across the 29 of their papers we have counts for
32 papers
Multi-Task Representation Learning for Conservative Linear Bandits
Jiabin Lin, Shana Moothedath
This paper presents the Constrained Multi-Task Representation Learning (CMTRL) framework for linear bandits. We consider T linear bandit tasks in a d dimensional space, which share…
Provable Multi-Task Reinforcement Learning: A Representation Learning Framework with Low Rank Rewards
Yaoze Guo, Shana Moothedath
Multi-task representation learning (MTRL) is an approach that learns shared latent representations across related tasks, facilitating collaborative learning that improves the overa…
RepUCB: Representation Learning-Based UCB for Heterogeneous Multi-Task Linear Bandits
Jiabin Lin, Shana Moothedath
Multi-task representation learning exploits the shared structure among related tasks by learning a common latent representation, thereby improving sample efficiency. This paper int…
Post-Decision State-Based Online Learning for Delay-Energy-Aware Flow Allocation in Wireless Systems
Mahesh Ganesh Bhat, Shana Moothedath, Prasanna Chaporkar
We develop a structure-aware reinforcement learning (RL) approach for delay- and energy-aware flow allocation in 5G User Plane Functions (UPFs). We consider a dynamic system with $…
Diffusion-based Decentralized Federated Multi-Task Representation Learning
Donghwa Kang, Shana Moothedath
Representation learning is a widely adopted framework for learning in data-scarce environments to obtain a feature extractor or representation from various different yet related ta…
Beyond Centralization: Provable Communication Efficient Decentralized Multi-Task Learning
Donghwa Kang, Shana Moothedath
Representation learning is a widely adopted framework for learning in data-scarce environments, aiming to extract common features from related tasks. While centralized approaches h…
Energy-Aware 6G Network Design: A Survey
Rashmi Kamran, Mahesh Ganesh Bhat, Pranav Jha +3
6th Generation (6G) mobile networks are envisioned to support several new capabilities and data-centric applications for unprecedented number of users, potentially raising signific…
Neural Contextual Bandits Under Delayed Feedback Constraints
Mohammadali Moghimi, Sharu Theresa Jose, Shana Moothedath
This paper presents a new algorithm for neural contextual bandits (CBs) that addresses the challenge of delayed reward feedback, where the reward for a chosen action is revealed af…
Byzantine Resilient Federated Multi-Task Representation Learning
Tuan Le, Shana Moothedath
In this paper, we propose BR-MTRL, a Byzantine-resilient multi-task representation learning framework that handles faulty or malicious agents. Our approach leverages representation…
Fast and Sample Efficient Multi-Task Representation Learning in Stochastic Contextual Bandits
Jiabin Lin, Shana Moothedath, Namrata Vaswani
We study how representation learning can improve the learning efficiency of contextual bandit problems. We study the setting where we play T contextual linear bandits with dimensio…
Thompson Sampling for Stochastic Bandits with Noisy Contexts: An Information-Theoretic Regret Analysis
Sharu Theresa Jose, Shana Moothedath
We explore a stochastic contextual linear bandit problem where the agent observes a noisy, corrupted version of the true context through a noise channel with an unknown noise param…
Distributed Multi-Task Learning for Stochastic Bandits with Context Distribution and Stage-wise Constraints
Jiabin Lin, Shana Moothedath
We present conservative distributed multi-task learning in stochastic linear contextual bandits with heterogeneous agents. This extends conservative linear bandits to a distributed…
Minimal Input Structural Modifications for Strongly Structural Controllability
Geethu Joseph, Shana Moothedath, Jiabin Lin
This paper studies the problem of modifying the input matrix of a structured system to make the system strongly structurally controllable. We focus on the generalized structured sy…
Federated Learning for Heterogeneous Bandits with Unobserved Contexts
Jiabin Lin, Shana Moothedath
We study the problem of federated stochastic multi-arm contextual bandits with unknown contexts, in which M agents are faced with different bandits and collaborate to learn. The co…
Distributed Stochastic Bandit Learning with Delayed Context Observation
Jiabin Lin, Shana Moothedath
We consider the problem where M agents collaboratively interact with an instance of a stochastic K-armed contextual bandit, where K>>M. The goal of the agents is to simultaneously…
Decentralized Communication-Efficient Multi-Task Representation Learning
Shana Moothedath, Namrata Vaswani
This work develops a provably accurate fully-decentralized alternating projected gradient descent (GD) algorithm for recovering a low rank (LR) matrix from mutually independent pro…
Stochastic Conservative Contextual Linear Bandits
Jiabin Lin, Xian Yeow Lee, Talukder Jubery +3
Many physical systems have underlying safety considerations that require that the strategy deployed ensures the satisfaction of a set of constraints. Further, often we have only pa…
Fully Decentralized and Federated Low Rank Compressive Sensing
Shana Moothedath, Namrata Vaswani
In this work we develop a fully decentralized, federated, and fast solution to the recently studied Low Rank Compressive Sensing (LRCS) problem: recover an nxq low-rank matrix from…
Stochastic Dynamic Information Flow Tracking Game using Supervised Learning for Detecting Advanced Persistent Threats
Shana Moothedath, Dinuka Sahabandu, Joey Allen +3
Advanced persistent threats (APTs) are organized prolonged cyberattacks by sophisticated attackers. Although APT activities are stealthy, they interact with the system components a…
A Reinforcement Learning Approach for Dynamic Information Flow Tracking Games for Detecting Advanced Persistent Threats
Dinuka Sahabandu, Shana Moothedath, Joey Allen +3
Advanced Persistent Threats (APTs) are stealthy attacks that threaten the security and privacy of sensitive information. Interactions of APTs with victim system introduce informati…
Dynamic Information Flow Tracking for Detection of Advanced Persistent Threats: A Stochastic Game Approach
Shana Moothedath, Dinuka Sahabandu, Joey Allen +4
Advanced Persistent Threats (APTs) are stealthy customized attacks by intelligent adversaries. This paper deals with the detection of APTs that infiltrate cyber systems and comprom…
A Game Theoretic Approach for Dynamic Information Flow Tracking to Detect Multi-Stage Advanced Persistent Threats
Shana Moothedath, Dinuka Sahabandu, Joey Allen +4
Advanced Persistent Threats (APTs) infiltrate cyber systems and compromise specifically targeted data and/or resources through a sequence of stealthy attacks consisting of multiple…
Optimal Network Topology Design in Composite Systems with Constrained Neighbors for Structural Controllability
Shana Moothedath, Prasanna Chaporkar, Aishwary Joshi
Composite systems are large complex systems con- sisting of interconnected agents (subsystems). Agents in a com- posite system interact with each other towards performing an in- te…
Minimizing Inputs for Strong Structural Controllability
Kumar Yashashwi, Shana Moothedath, Prasanna Chaporkar
The notion of strong structural controllability (s-controllability) allows for determining controllability properties of large linear time-invariant systems even when numerical val…