Search Results for author:
Found 43 papers, 3 papers with code
Fast and Consistent Learning of Hidden Markov Models by Incorporating Non-Consecutive Correlations
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?
Fisher Information Approach for Masking the Sensing Plan: Applications in Multifunction Radars
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?
Controlling Federated Learning for Covertness
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.
Fréchet Statistics Based Change Point Detection in Multivariate Hawkes Process
This paper proposes a new approach for change point detection in causal networks of multivariate Hawkes processes using Frechet statistics.
Statistical Detection of Coordination in a Cognitive Radar Network through Inverse Multi-objective Optimization
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.
Finite-Sample Bounds for Adaptive Inverse Reinforcement Learning using Passive Langevin Dynamics
This paper provides a finite-sample analysis of a passive stochastic gradient Langevin dynamics algorithm (PSGLD) designed to achieve adaptive inverse reinforcement learning (IRL).
Who You Play Affects How You Play: Predicting Sports Performance Using Graph Attention Networks With Temporal Convolution
To construct a dynamic player interaction graph, we leverage player statistics and their interactions during gameplay.
Radar Clutter Covariance Estimation: A Nonlinear Spectral Shrinkage Approach
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.
Dynamics of Social Networks: Multi-agent Information Fusion, Anticipatory Decision Making and Polling
Part 2, namely, Multi-agent Information Fusion with Behavioral Economics Constraints generalizes Part 1.
Adaptive ECCM for Mitigating Smart Jammers
This paper considers adaptive radar electronic counter-counter measures (ECCM) to mitigate ECM by an adversarial jammer.
Identifying Coordination in a Cognitive Radar Network -- A Multi-Objective Inverse Reinforcement Learning Approach
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.
How can a Radar Mask its Cognition?
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.
Adaptive Filtering Algorithms for Set-Valued Observations -- Symmetric Measurement Approach to Unlabeled and Anonymized Data
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.
Quickest Detection for Human-Sensor Systems using Quantum Decision Theory
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).
Estimating Exposure to Information on Social Networks
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.
Lyapunov based Stochastic Stability of a Quantum Decision System for Human-Machine Interaction
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.
Inverse-Inverse Reinforcement Learning. How to Hide Strategy from an Adversarial Inverse Reinforcement Learner
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).
Meta-Cognition. An Inverse-Inverse Reinforcement Learning Approach for Cognitive Radars
A meta-cognitive radar is aware of the adversarial nature of the target and seeks to mitigate the adversarial target.
Lyapunov based Stochastic Stability of Human-Machine Interaction: A Quantum Decision System Approach
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.
Interval Dominance based Structural Results for Markov Decision Process
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).
How can a Cognitive Radar Mask its Cognition?
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.
Anomalous Edge Detection in Edge Exchangeable Social Network Models
This paper studies detecting anomalous edges in directed graphs that model social networks.
Principal Agent Problem as a Principled Approach to Electronic Counter-Countermeasures in Radar
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.
Unifying Revealed Preference and Revealed Rational Inattention
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.
Rationally Inattentive Utility Maximization for Interpretable Deep Image Classification
Are deep convolutional neural networks (CNNs) for image classification explainable by utility maximization with information acquisition costs?
Adaptive Non-reversible Stochastic Gradient Langevin Dynamics
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.
A Markov Decision Process Approach to Active Meta Learning
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.
Multi-kernel Passive Stochastic Gradient Algorithms and Transfer Learning
This paper develops a novel passive stochastic gradient algorithm.
Adversarial Radar Inference: Inverse Tracking, Identifying Cognition and Designing Smart Interference
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.
Necessary and Sufficient Conditions for Inverse Reinforcement Learning of Bayesian Stopping Time Problems
This paper presents an inverse reinforcement learning~(IRL) framework for Bayesian stopping time problems.
Langevin Dynamics for Adaptive Inverse Reinforcement Learning of Stochastic Gradient Algorithms
Inverse reinforcement learning (IRL) aims to estimate the reward function of optimizing agents by observing their response (estimates or actions).
Policy Gradient using Weak Derivatives for Reinforcement Learning
This paper considers policy search in continuous state-action reinforcement learning problems.
Quickest Change Detection of Time Inconsistent Anticipatory Agents. Human-Sensor and Cyber-Physical Systems
Given these decisions, how can the sensing device achieve quickest detection of a change in the anticipatory system?
Adversarial Radar Inference. From Inverse Tracking to Inverse Reinforcement Learning of Cognitive Radar
Cognitive sensing refers to a reconfigurable sensor that dynamically adapts its sensing mechanism by using stochastic control to optimize its sensing resources.
Identifying Cognitive Radars -- Inverse Reinforcement Learning using Revealed Preferences
(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?
Rationally Inattentive Inverse Reinforcement Learning Explains YouTube Commenting Behavior
We consider a novel application of inverse reinforcement learning with behavioral economics constraints to model, learn and predict the commenting behavior of YouTube viewers.
“What Do Your Friends Think?”: Efficient Polling Methods for Networks Using Friendship Paradoxhttps://ieeexplore.ieee.org/abstract/document/8832260
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?
Maximum Likelihood Estimation of Power-law Degree Distributions via Friendship Paradox based Sampling
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
Friendship Paradox Biases Perceptions in Directed Networks
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
Estimating Rationally Inattentive Utility Functions with Deep Clustering for Framing - Applications in YouTube Engagement Dynamics
By observing these decisions, how can an observer estimate the utility function and information acquisition cost?
Inverse Filtering for Hidden Markov Models
This paper considers a number of related inverse filtering problems for hidden Markov models (HMMs).
Syntactic Enhancement to VSIMM for Roadmap Based Anomalous Trajectory Detection: A Natural Language Processing Approach
In this paper, we generalize earlier work by considering a constrained stochastic context free grammar (CSCFG) for modeling patterns confined to a roadmap.
Reinforcement Learning and Nonparametric Detection of Game-Theoretic Equilibrium Play in Social Networks
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.
