arXiv subjects

Jayanth Bhargav

Publications and source records attributed to Jayanth Bhargav.

8 recordsLinked to original sources

arXiv2026

A Unified Framework for Joint Sensor Placement and Scheduling for Intrusion Detection

We consider an intrusion detection task in which a defender must jointly optimize sensor placement locations and orientations to minimize the probability of missed detection of an intruder traversing a protected environment. We decompose this problem into a meta problem, termed SensorPlacement, and an embedded subproblem, termed OrientationScheduling. The OrientationScheduling subproblem, for a fixed sensor placement, is modeled as a 2-player zero-sum game between the defender and the intruder, where the defender seeks an orientation strategy for the deployed sensors to minimize the probability of missed detection, while the intruder seeks a path selection strategy to maximize it. Since the defender's strategy space grows combinatorially with the number of sensors and orientations, solving the game via standard linear programming becomes prohibitive. To this end, we develop an iterative and efficient equilibrium-seeking algorithm that exploits the structure of the game's payoff function and establishes theoretical guarantees for convergence to the Nash equilibrium (NE) of the game. This NE value is then used as a utility measure in the SensorPlacement meta problem. We show that this game-value-based utility function is weakly submodular over the set of sensor placements and propose a greedy placement algorithm with near-optimality guarantees. To our knowledge, this is the first unified framework to integrate game-theoretic utility design with (weak) submodular optimization, enabling principled joint optimization of sensor placement and orientation scheduling. Through extensive simulations, we demonstrate that the proposed approach achieves near-optimal detection performance while significantly reducing computation time compared to baselines.

eess.SY↗

arXiv2026

Distributed Equilibrium-Seeking in Target Coverage Games via Self-Configurable Networks under Limited Communication

We study a target coverage problem in which a team of sensing agents, operating under limited communication, must collaboratively monitor targets that may be adaptively repositioned by an attacker. We model this interaction as a zero-sum game between the sensing team (known as the defender) and the attacker. However, computing an exact Nash equilibrium (NE) for this game is computationally prohibitive as the action space of the defender grows exponentially with the number of sensors and their possible orientations. Exploiting the submodularity property of the game's utility function, we propose a distributed framework that enables agents to self-configure their communication neighborhoods under bandwidth constraints and collaboratively maximize the target coverage. We establish theoretical guarantees showing that the resulting sensing strategies converge to an approximate NE of the game. To our knowledge, this is the first distributed, communication-aware approach that scales effectively for games with combinatorial action spaces while explicitly incorporating communication constraints. To this end, we leverage the distributed bandit-submodular optimization framework and the notion of Value of Coordination that were introduced in [1]. Through simulations, we show that our approach attains near-optimal game value and higher target coverage compared to baselines.

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arXiv2026

Microservice Architecture Patterns for Scalable Machine Learning Systems

Machine learning is now a central part of how modern systems are built and used, powering everything from personalized recommendations to large-scale business analytics. As its role grows, organizations are facing new challenges in managing, deploying, and scaling these models efficiently. One approach that has gained wide adoption is the use of microservice architectures, which break complex machine learning systems into smaller, independent parts that can be built, updated, and scaled on their own. In this paper, we review how major companies such as Netflix, Uber, and Google use microservices to handle key machine learning tasks like training, deployment, and monitoring. We discuss the main challenges involved in designing such systems and explore how microservices fit into large-scale applications, particularly in recommendation systems. We also present some simulation studies showing that microservice-based designs can reduce latency and improve scalability, leading to faster, more efficient, and more responsive machine learning applications in real-world and large-scale systems.

cs.SE↗

arXiv2025

Sensor Scheduling in Intrusion Detection Games with Uncertain Payoffs

We study the problem of sensor scheduling for an intrusion detection task. We model this as a two-player zero-sum game over a graph, where the defender (Player 1) seeks to identify the optimal strategy for scheduling sensor orientations to minimize the probability of missed detection at minimal cost, while the intruder (Player 2) aims to identify the optimal path selection strategy to maximize missed detection probability at minimal cost. The defender's strategy space grows exponentially with the number of sensors, making direct computation of the Nash Equilibrium (NE) strategies computationally expensive. To tackle this, we propose a distributed variant of the Weighted Majority algorithm that exploits the structure of the game's payoff matrix, enabling efficient computation of the NE strategies with provable convergence guarantees. Next, we consider a more challenging scenario where the defender lacks knowledge of the true sensor models and, consequently, the game's payoff matrix. For this setting, we develop online learning algorithms that leverage bandit feedback from sensors to estimate the NE strategies. By building on existing results from perturbation theory and online learning in matrix games, we derive high-probability order-optimal regret bounds for our algorithms. Finally, through simulations, we demonstrate the empirical performance of our proposed algorithms in both known and unknown payoff scenarios.

eess.SY↗

arXiv2025

Robust Information Selection for Hypothesis Testing with Misclassification Penalties

We study the problem of robust information selection for a Bayesian hypothesis testing / classification task, where the goal is to identify the true state of the world from a finite set of hypotheses based on observations from the selected information sources. We introduce a novel misclassification penalty framework, which enables non-uniform treatment of different misclassification events. Extending the classical subset selection framework, we study the problem of selecting a subset of sources that minimize the maximum penalty of misclassification under a limited budget, despite deletions or failures of a subset of the selected sources. We characterize the curvature properties of the objective function and propose an efficient greedy algorithm with performance guarantees. Next, we highlight certain limitations of optimizing for the maximum penalty metric and propose a submodular surrogate metric to guide the selection of the information set. We propose a greedy algorithm with near-optimality guarantees for optimizing the surrogate metric. Finally, we empirically demonstrate the performance of our proposed algorithms in several instances of the information set selection problem.

stat.ML↗

arXiv2024

Optimal Sensor and Actuator Selection for Factored Markov Decision Processes: Complexity, Approximability and Algorithms

Factored Markov Decision Processes (fMDPs) are a class of Markov Decision Processes (MDPs) in which the states (and actions) can be factored into a set of state (and action) variables and can be encoded compactly using a factored representation. In this paper, we consider a setting where the state of the fMDP is not directly observable, and the agent relies on a set of potential sensors to gather information. We formulate the problem of selecting a set of sensors for fMDPs (under a limited budget) to maximize the infinite-horizon discounted return provided by the optimal policy. We show the fundamental result that it is NP-hard to approximate this problem to within a factor of $n^{1-c}$ for any $c > 1$, where $n$ is the number of state variables. Our inapproximability results for sensor selection also extend to a general class of Partially Observable MDPs (POMDPs). We also consider the dual problem of budgeted actuator selection (at design-time) to maximize the expected return under the optimal policy, for which we establish similar inapproximability results. Finally, we consider a simple greedy algorithm and empirically show that, despite the lack of formal theoretical guarantees, it performs effectively in practice, achieving on average over $70\%$ of the optimal solution value across a variety of real-world and randomly generated problem instances.

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arXiv2024

Submodular Information Selection for Hypothesis Testing with Misclassification Penalties

We consider the problem of selecting an optimal subset of information sources for a hypothesis testing/classification task where the goal is to identify the true state of the world from a finite set of hypotheses, based on finite observation samples from the sources. In order to characterize the learning performance, we propose a misclassification penalty framework, which enables nonuniform treatment of different misclassification errors. In a centralized Bayesian learning setting, we study two variants of the subset selection problem: (i) selecting a minimum cost information set to ensure that the maximum penalty of misclassifying the true hypothesis is below a desired bound and (ii) selecting an optimal information set under a limited budget to minimize the maximum penalty of misclassifying the true hypothesis. Under certain assumptions, we prove that the objective (or constraints) of these combinatorial optimization problems are weak (or approximate) submodular, and establish high-probability performance guarantees for greedy algorithms. Further, we propose an alternate metric for information set selection which is based on the total penalty of misclassification. We prove that this metric is submodular and establish near-optimal guarantees for the greedy algorithms for both the information set selection problems. Finally, we present numerical simulations to validate our theoretical results over several randomly generated instances.

stat.ML↗

arXiv2021

Track based Offline Policy Learning for Overtaking Maneuvers with Autonomous Racecars

The rising popularity of driver-less cars has led to the research and development in the field of autonomous racing, and overtaking in autonomous racing is a challenging task. Vehicles have to detect and operate at the limits of dynamic handling and decisions in the car have to be made at high speeds and high acceleration. One of the most crucial parts in autonomous racing is path planning and decision making for an overtaking maneuver with a dynamic opponent vehicle. In this paper we present the evaluation of a track based offline policy learning approach for autonomous racing. We define specific track portions and conduct offline experiments to evaluate the probability of an overtaking maneuver based on speed and position of the ego vehicle. Based on these experiments we can define overtaking probability distributions for each of the track portions. Further, we propose a switching MPCC controller setup for incorporating the learnt policies to achieve a higher rate of overtaking maneuvers. By exhaustive simulations, we show that our proposed algorithm is able to increase the number of overtakes at different track portions.

cs.RO↗