44 citations · 364 across the 85 of their papers we have counts for
11 papers · 1 filter
Controlling Federated Learning for Covertness
Adit Jain, Vikram Krishnamurthy
A learner aims to minimize a function by repeatedly querying a distributed oracle that provides noisy gradient evaluations. At the same time, the learner seeks to hide $\arg\mi…
Inverse-Inverse Reinforcement Learning. How to Hide Strategy from an Adversarial Inverse Reinforcement Learner
Kunal Pattanayak, Vikram Krishnamurthy, Christopher Berry
Inverse reinforcement learning (IRL) deals with estimating an agent's utility function from its actions. In this paper, we consider how an agent can hide its strategy and mitigate…
Rationally Inattentive Utility Maximization for Interpretable Deep Image Classification
Kunal Pattanayak, Vikram Krishnamurthy
Are deep convolutional neural networks (CNNs) for image classification explainable by utility maximization with information acquisition costs? We demonstrate that deep CNNs behave…
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…
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…
Rationally Inattentive Inverse Reinforcement Learning Explains YouTube Commenting Behavior
William Hoiles, Vikram Krishnamurthy, Kunal Pattanayak
We consider a novel application of inverse reinforcement learning with behavioral economics constraints to model, learn and predict the commenting behavior of YouTube viewers. Each…
Estimating Rationally Inattentive Utility Functions with Deep Clustering for Framing - Applications in YouTube Engagement Dynamics
William Hoiles, Vikram Krishnamurthy
We consider a framework involving behavioral economics and machine learning. Rationally inattentive Bayesian agents make decisions based on their posterior distribution, utility fu…