Vikram Krishnamurthy

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The University of British Columbia ∙ cornell university

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Distributed learning in congested environments with partial information

How can non-communicating agents learn to share congested resources effi...
03/29/2021 ∙ by Tomer Boyarski, et al. ∙ 0 ∙ share

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A Directed, Bi-Populated Preferential Attachment Model with Applications to Analyzing the Glass Ceiling Effect

Preferential attachment, homophily and, their consequences such as the g...
03/22/2021 ∙ by Buddhika Nettasinghe, et al. ∙ 0 ∙ share

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Emergence of Structural Inequalities in Scientific Citation Networks

Structural inequalities persist in society, conferring systematic advant...
03/19/2021 ∙ by Buddhika Nettasinghe, et al. ∙ 0 ∙ share

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Behavioral Economics Approach to Interpretable Deep Image Classification. Rationally Inattentive Utility Maximization Explains Deep Image Classification

Are deep convolutional neural networks (CNNs) for image classification c...
02/09/2021 ∙ by Kunal Pattanayak, et al. ∙ 0 ∙ share

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Adaptive Non-reversible Stochastic Gradient Langevin Dynamics

It is well known that adding any skew symmetric matrix to the gradient o...
09/26/2020 ∙ by Vikram Krishnamurthy, et al. ∙ 0 ∙ share

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A Markov Decision Process Approach to Active Meta Learning

In supervised learning, we fit a single statistical model to a given dat...
09/10/2020 ∙ by Bingjia Wang, et al. ∙ 6 ∙ share

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Multi-kernel Passive Stochastic Gradient Algorithms

This paper develops a novel passive stochastic gradient algorithm. In pa...
08/23/2020 ∙ by Vikram Krishnamurthy, et al. ∙ 0 ∙ share

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Adversarial Radar Inference: Inverse Tracking, Identifying Cognition and Designing Smart Interference

This paper considers three inter-related adversarial inference problems ...
08/01/2020 ∙ by Vikram Krishnamurthy, et al. ∙ 0 ∙ share

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Inverse Reinforcement Learning for Sequential Hypothesis Testing and Search

This paper considers a novel formulation of inverse reinforcement learni...
07/07/2020 ∙ by Kunal Pattanayak, et al. ∙ 0 ∙ share

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Langevin Dynamics for Inverse Reinforcement Learning of Stochastic Gradient Algorithms

Inverse reinforcement learning (IRL) aims to estimate the reward functio...
06/20/2020 ∙ by Vikram Krishnamurthy, et al. ∙ 0 ∙ share

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Policy Gradient using Weak Derivatives for Reinforcement Learning

This paper considers policy search in continuous state-action reinforcem...
04/09/2020 ∙ by Sujay Bhatt, et al. ∙ 0 ∙ share

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Anticipatory Psychological Models for Quickest Change Detection: Human Sensor Interaction

We consider anticipatory psychological models for human decision makers ...
03/23/2020 ∙ by Vikram Krishnamurthy, et al. ∙ 0 ∙ share

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Inverse Cognitive Radar – A Revealed Preferences Approach

We consider an adversarial signal processing problem involving "us" vers...
12/01/2019 ∙ by Vikram Krishnamurthy, et al. ∙ 0 ∙ share

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Rationally Inattentive Inverse Reinforcement Learning Explains YouTube Commenting Behavior

We consider a novel application of inverse reinforcement learning which ...
10/24/2019 ∙ by William Hoiles, et al. ∙ 0 ∙ share

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Estimating Rationally Inattentive Utility Functions with Deep Clustering for Framing - Applications in YouTube Engagement Dynamics

We consider a framework involving behavioral economics and machine learn...
12/23/2018 ∙ by William Hoiles, et al. ∙ 0 ∙ share

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Roadmap Enhanced Improvement to the VSIMM Tracker via a Constrained Stochastic Context Free Grammar

The aim of syntactic tracking is to classify spatio-temporal patterns of...
11/10/2016 ∙ by Sijia Gao, et al. ∙ 0 ∙ share

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Reinforcement Learning and Nonparametric Detection of Game-Theoretic Equilibrium Play in Social Networks

This paper studies two important signal processing aspects of equilibriu...
12/11/2014 ∙ by Omid Namvar Gharehshiran, et al. ∙ 0 ∙ share

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Intent Inference and Syntactic Tracking with GMTI Measurements

In conventional target tracking systems, human operators use the estimat...
04/22/2011 ∙ by Alex Wang, et al. ∙ 0 ∙ share

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