no code implementations • ICML 2020 • Robert Mattila, Cristian Rojas, Eric Moulines, Vikram Krishnamurthy, Bo Wahlberg
Can the parameters of a hidden Markov model (HMM) be estimated from a single sweep through the observations -- and additionally, without being trapped at a local optimum in the likelihood surface?
no code implementations • 13 May 2024 • Adit Jain, Vikram Krishnamurthy
This paper studies how a stochastic gradient algorithm (SG) can be controlled to hide the estimate of the local stationary point from an eavesdropper.
no code implementations • 24 Mar 2024 • Shashwat Jain, Vikram Krishnamurthy, Muralidhar Rangaswamy, Bosung Kang, Sandeep Gogineni
How to design a Markov Decision Process (MDP) based radar controller that makes small sacrifices in performance to mask its sensing plan from an adversary?
no code implementations • 17 Aug 2023 • Adit Jain, Vikram Krishnamurthy
The problem of controlling the stochastic gradient algorithm for covert optimization is modeled as a Markov decision process, and we show that the dynamic programming operator has a supermodular structure implying that the optimal policy has a monotone threshold structure.
no code implementations • 13 Aug 2023 • Rui Luo, Vikram Krishnamurthy
This paper proposes a new approach for change point detection in causal networks of multivariate Hawkes processes using Frechet statistics.
no code implementations • 18 Apr 2023 • Luke Snow, Vikram Krishnamurthy
This paper provides a finite-sample analysis of a passive stochastic gradient Langevin dynamics algorithm (PSGLD) designed to achieve adaptive inverse reinforcement learning (IRL).
no code implementations • 18 Apr 2023 • Luke Snow, Vikram Krishnamurthy
By 'coordination' we mean that the radar emissions satisfy Pareto optimality with respect to multi-objective optimization over the objective functions of each radar and a constraint on total network power output.
no code implementations • 29 Mar 2023 • Rui Luo, Vikram Krishnamurthy
To construct a dynamic player interaction graph, we leverage player statistics and their interactions during gameplay.
no code implementations • 4 Feb 2023 • Shashwat Jain, Vikram Krishnamurthy, Muralidhar Rangaswamy, Bosung Kang, Sandeep Gogineni
We demonstrate that the computation time for the estimation by the proposed algorithm is less than the RCML-EL algorithm with identical Signal to Clutter plus Noise (SCNR) performance.
no code implementations • 26 Dec 2022 • Vikram Krishnamurthy
Part 2, namely, Multi-agent Information Fusion with Behavioral Economics Constraints generalizes Part 1.
no code implementations • 5 Dec 2022 • Kunal Pattanayak, Shashwat Jain, Vikram Krishnamurthy, Chris Berry
This paper considers adaptive radar electronic counter-counter measures (ECCM) to mitigate ECM by an adversarial jammer.
no code implementations • 13 Nov 2022 • Luke Snow, Vikram Krishnamurthy, Brian M. Sadler
This paper provides a novel multi-objective inverse reinforcement learning approach which allows for both detection of such Pareto optimal ('coordinating') behavior and subsequent reconstruction of each radar's utility function, given a finite dataset of radar network emissions.
no code implementations • 20 Oct 2022 • Kunal Pattanayak, Vikram Krishnamurthy, Christopher Berry
We provide theoretical guarantees by ensuring the Type-I error probability of the adversary's detector exceeds a pre-defined level for a specified tolerance on the radar's performance loss.
no code implementations • 31 Aug 2022 • Vikram Krishnamurthy
By exploiting that fact that the algebraic ring of multi-variable polynomials is a unique factorization domain over the ring of one-variable polynomials, we construct an adaptive filtering algorithm that yields a statistically consistent estimate of the underlying parameters.
no code implementations • 18 Aug 2022 • Luke Snow, Vikram Krishnamurthy, Brian M. Sadler
In mathematical psychology, recent models for human decision-making use Quantum Decision Theory to capture important human-centric features such as order effects and violation of the sure-thing principle (total probability law).
no code implementations • 13 Jul 2022 • Buddhika Nettasinghe, Kowe Kadoma, Mor Naaman, Vikram Krishnamurthy
The exact value of exposure to a piece of information is determined by two features: the structure of the underlying social network and the set of people who shared the piece of information.
no code implementations • 24 May 2022 • Luke Snow, Shashwat Jain, Vikram Krishnamurthy
We show via novel stochastic Lyapunov arguments how the Lindbladian dynamics of the quantum decision maker can be controlled to converge to a specific decision asymptotically.
no code implementations • 22 May 2022 • Kunal Pattanayak, Vikram Krishnamurthy, Christopher Berry
In this paper, we consider how an agent can hide its strategy and mitigate an adversarial IRL attack; we call this inverse IRL (I-IRL).
no code implementations • 3 May 2022 • Kunal Pattanayak, Vikram Krishnamurthy, Christopher Berry
A meta-cognitive radar is aware of the adversarial nature of the target and seeks to mitigate the adversarial target.
no code implementations • 31 Mar 2022 • Luke Snow, Shashwat Jain, Vikram Krishnamurthy
We show via novel stochastic Lyapunov arguments how the Lindbladian dynamics of the quantum decision maker can be controlled to converge to a specific decision asymptotically.
no code implementations • 20 Mar 2022 • Vikram Krishnamurthy
We present several MDP examples where supermodularity does not hold, yet I holds, and so the optimal policy is monotone; these include sigmoidal rewards (arising in prospect theory for human decision making), bi-diagonal and perturbed bi-diagonal transition matrices (in optimal allocation problems).
no code implementations • 16 Oct 2021 • Kunal Pattanayak, Vikram Krishnamurthy, Christopher Berry
In turn, the radar deliberately chooses sub-optimal responses so that its utility function almost fails the utility maximization test, and hence, its cognitive ability is masked from the adversary.
no code implementations • 27 Sep 2021 • Rui Luo, Buddhika Nettasinghe, Vikram Krishnamurthy
This paper studies detecting anomalous edges in directed graphs that model social networks.
no code implementations • 8 Sep 2021 • Anurag Gupta, Vikram Krishnamurthy
The main idea of this paper is to show that ECCM involving a radar and a jammer can be formulated as a principal-agent problem (PAP) - a problem widely studied in microeconomics.
no code implementations • 28 Jun 2021 • Kunal Pattanayak, Vikram Krishnamurthy
Second, we exploit the unification result computationally to extend robustness measures for goodness-of-fit of revealed preference tests in the literature to revealed rational inattention.
1 code implementation • 9 Feb 2021 • Kunal Pattanayak, Vikram Krishnamurthy
Are deep convolutional neural networks (CNNs) for image classification explainable by utility maximization with information acquisition costs?
no code implementations • 26 Sep 2020 • 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.
no code implementations • 10 Sep 2020 • 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 specific use, but does not adapt well to new contexts.
no code implementations • 23 Aug 2020 • Vikram Krishnamurthy, George Yin
This paper develops a novel passive stochastic gradient algorithm.
no code implementations • 1 Aug 2020 • Vikram Krishnamurthy, Kunal Pattanayak, Sandeep Gogineni, Bosung Kang, Muralidhar Rangaswamy
The levels of abstraction range from smart interference design based on Wiener filters (at the pulse/waveform level), inverse Kalman filters at the tracking level and revealed preferences for identifying utility maximization at the systems level.
no code implementations • 7 Jul 2020 • Kunal Pattanayak, Vikram Krishnamurthy
This paper presents an inverse reinforcement learning~(IRL) framework for Bayesian stopping time problems.
no code implementations • 20 Jun 2020 • Vikram Krishnamurthy, George Yin
Inverse reinforcement learning (IRL) aims to estimate the reward function of optimizing agents by observing their response (estimates or actions).
no code implementations • 9 Apr 2020 • Sujay Bhatt, Alec Koppel, Vikram Krishnamurthy
This paper considers policy search in continuous state-action reinforcement learning problems.
no code implementations • 23 Mar 2020 • Vikram Krishnamurthy
Given these decisions, how can the sensing device achieve quickest detection of a change in the anticipatory system?
no code implementations • 22 Feb 2020 • Vikram Krishnamurthy
Cognitive sensing refers to a reconfigurable sensor that dynamically adapts its sensing mechanism by using stochastic control to optimize its sensing resources.
no code implementations • 1 Dec 2019 • Vikram Krishnamurthy, Daniel Angley, Robin Evans, William Moran
(ii) How to construct a statistical test for detecting a cognitive radar (constrained utility maximization) when we observe the radar's actions in noise or the radar observes our probe signal in noise?
2 code implementations • 24 Oct 2019 • 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.
no code implementations • IEEE Transactions on Knowledge and Data Engineering 2019 • Buddhika Nettasinghe, Vikram Krishnamurthy
In this paper, we propose a novel neighborhood expectation polling (NEP) strategy that asks randomly sampled individuals: what is your estimate of the fraction of votes for A?
no code implementations • 1 Aug 2019 • Buddhika Nettasinghe, Vikram Krishnamurthy
Although power-law degree distributions are ubiquitous in nature, the widely used parametric methods for estimating them (e. g. linear regression on double-logarithmic axes, maximum likelihood estimation with uniformly sampled nodes) suffer from the large variance introduced by the lack of data-points from the tail portion of the power-law degree distribution.
Social and Information Networks Data Analysis, Statistics and Probability Physics and Society
1 code implementation • 13 May 2019 • Nazanin Alipourfard, Buddhika Nettasinghe, Andres Abeliuk, Vikram Krishnamurthy, Kristina Lerman
For example, in an online network of a social media platform, the number of people who mention a topic in their posts---i. e., its global popularity---can be dramatically different from how people see it in their social feeds---i. e., its perceived popularity---where the feeds aggregate their friends' posts.
Social and Information Networks Physics and Society
no code implementations • 23 Dec 2018 • William Hoiles, Vikram Krishnamurthy
By observing these decisions, how can an observer estimate the utility function and information acquisition cost?
no code implementations • NeurIPS 2017 • Robert Mattila, Cristian Rojas, Vikram Krishnamurthy, Bo Wahlberg
This paper considers a number of related inverse filtering problems for hidden Markov models (HMMs).
no code implementations • 10 Nov 2016 • Vikram Krishnamurthy, Sijia Gao
In this paper, we generalize earlier work by considering a constrained stochastic context free grammar (CSCFG) for modeling patterns confined to a roadmap.
no code implementations • 11 Dec 2014 • Omid Namvar Gharehshiran, William Hoiles, Vikram Krishnamurthy
This paper studies two important signal processing aspects of equilibrium behavior in non-cooperative games arising in social networks, namely, reinforcement learning and detection of equilibrium play.