44 citations · 364 across the 85 of their papers we have counts for
12 papers · 1 filter
Echo Chambers and Segregation in Social Networks: Markov Bridge Models and Estimation
Rui Luo, Buddhika Nettasinghe, Vikram Krishnamurthy
This paper deals with the modeling and estimation of the sociological phenomena called echo chambers and segregation in social networks. Specifically, we present a novel community-…
Adaptive Non-reversible Stochastic Gradient Langevin Dynamics
Vikram Krishnamurthy, George Yin
It is well known that adding any skew symmetric matrix to the gradient of Langevin dynamics algorithm results in a non-reversible diffusion with improved convergence rate. This pap…
A Markov Decision Process Approach to Active Meta Learning
Bingjia Wang, Alec Koppel, Vikram Krishnamurthy
In supervised learning, we fit a single statistical model to a given data set, assuming that the data is associated with a singular task, which yields well-tuned models for specifi…
Multi-kernel Passive Stochastic Gradient Algorithms and Transfer Learning
Vikram Krishnamurthy, George Yin
This paper develops a novel passive stochastic gradient algorithm. In passive stochastic approximation, the stochastic gradient algorithm does not have control over the location wh…
Adversarial Radar Inference: Inverse Tracking, Identifying Cognition and Designing Smart Interference
Vikram Krishnamurthy, Kunal Pattanayak, Sandeep Gogineni +2
This paper considers three inter-related adversarial inference problems involving cognitive radars. We first discuss inverse tracking of the radar to estimate the adversary's estim…
Necessary and Sufficient Conditions for Inverse Reinforcement Learning of Bayesian Stopping Time Problems
Kunal Pattanayak, Vikram Krishnamurthy
This paper presents an inverse reinforcement learning~(IRL) framework for Bayesian stopping time problems. By observing the actions of a Bayesian decision maker, we provide a neces…
Langevin Dynamics for Adaptive Inverse Reinforcement Learning of Stochastic Gradient Algorithms
Vikram Krishnamurthy, George Yin
Inverse reinforcement learning (IRL) aims to estimate the reward function of optimizing agents by observing their response (estimates or actions). This paper considers IRL when noi…
Policy Gradient using Weak Derivatives for Reinforcement Learning
Sujay Bhatt, Alec Koppel, Vikram Krishnamurthy
This paper considers policy search in continuous state-action reinforcement learning problems. Typically, one computes search directions using a classic expression for the policy g…
Controllability of Network Opinion in Erdos-Renyi Graphs using Sparse Control Inputs
Geethu Joseph, Buddhika Nettasinghe, Vikram Krishnamurthy +1
This paper considers a social network modeled as an Erdos Renyi random graph. Each individual in the network updates her opinion using the weighted average of the opinions of her n…
Quickest Change Detection of Time Inconsistent Anticipatory Agents. Human-Sensor and Cyber-Physical Systems
Vikram Krishnamurthy
In behavioral economics, human decision makers are modeled as anticipatory agents that make decisions by taking into account the probability of future decisions (plans). We conside…
Adversarial Radar Inference. From Inverse Tracking to Inverse Reinforcement Learning of Cognitive Radar
Vikram Krishnamurthy
Cognitive sensing refers to a reconfigurable sensor that dynamically adapts its sensing mechanism by using stochastic control to optimize its sensing resources. For example, cognit…
Inverse Filtering for Hidden Markov Models with Applications to Counter-Adversarial Autonomous Systems
Robert Mattila, Cristian R. Rojas, Vikram Krishnamurthy +1
Bayesian filtering deals with computing the posterior distribution of the state of a stochastic dynamic system given noisy observations. In this paper, motivated by applications in…