16 papers
Environment Parameter Gradient Theorem for Co-Design in Reinforcement Learning
Amber Srivastava
Reinforcement learning (RL) is traditionally concerned with learning a control policy for a fixed environment. In many engineering systems, however, the environment itself is alter…
Autonomy-Aware Clustering: When Local Decisions Supersede Global Prescriptions
Amber Srivastava, Salar Basiri, Srinivasa Salapaka
Clustering arises in a wide range of problem formulations, yet most existing approaches assume that the entities under clustering are passive and strictly conform to their assigned…
Sparse Linear Regression with Constraints: A Flexible Entropy-based Framework
Amber Srivastava, Alisina Bayati, Srinivasa Salapaka
This work presents a new approach to solve the sparse linear regression problem, i.e., to determine a k-sparse vector w in R^d that minimizes the cost ||y - Aw||^2_2. In contrast t…
Closed-Loop Identification of Stabilized Models Using Dual Input-Output Parameterization
Ran Chen, Amber Srivastava, Mingzhou Yin +1
This paper introduces a dual input-output parameterization (dual IOP) for the identification of linear time-invariant systems from closed-loop data. It draws inspiration from the r…
On Fulfilling the Exigent Need for Automating and Modernizing Logistics Infrastructure in India: Enabling AI-based Integration, Digitalization, and Smart Automation of Industrial Parks and Robotic Warehouses
Shaurya Shriyam, Prashant Palkar, Amber Srivastava
To stay competitive, the Low- or Middle-Income Countries (LMICs) need to embrace Industry 4.0 and Logistics 4.0. This requires government-level interventions and policy-making to i…
A Dual System-Level Parameterization for Identification from Closed-Loop Data
Amber Srivastava, Mingzhou Yin, Andrea Iannelli +1
This work presents a dual system-level parameterization (D-SLP) method for closed-loop system identification. The recent system-level synthesis framework parameterizes all stabiliz…
Once upon a time step: A closed-loop approach to robust MPC design
Anilkumar Parsi, Marcell Bartos, Amber Srivastava +2
A novel perspective on the design of robust model predictive control (MPC) methods is presented, whereby closed-loop constraint satisfaction is ensured using recursive feasibility…
Towards Efficient Modularity in Industrial Drying: A Combinatorial Optimization Viewpoint
Alisina Bayati, Amber Srivastava, Amir Malvandi +2
The industrial drying process consumes approximately 12% of the total energy used in manufacturing, with the potential for a 40% reduction in energy usage through improved process…
Time-Varying Parameters in Sequential Decision Making Problems
Amber Srivastava, S. M. Salapaka
In this paper we address the class of Sequential Decision Making (SDM) problems that are characterized by time-varying parameters. These parameter dynamics are either pre-specified…
Design of Experiments with Imputable Feature Data: An Entropy-Based Approach
Raj K. Velicheti, Amber Srivastava, Srinivasa M. Salapaka
Tactical selection of experiments to estimate an underlying model is an innate task across various fields. Since each experiment has costs associated with it, selecting statistical…
On Determining and Qualifying the Number of Superstates in Aggregation of Markov Chains
Amber Srivastava, Raj K. Velicheti, Srinivasa M. Salapaka
Many studies involving large Markov chains require determining a smaller representative (aggregated) chains. Each {\em superstate} in the representative chain represents a {\em gro…
Parameterized MDPs and Reinforcement Learning Problems -- A Maximum Entropy Principle Based Framework
Amber Srivastava, Srinivasa M Salapaka
We present a framework to address a class of sequential decision making problems. Our framework features learning the optimal control policy with robustness to noisy data, determin…
Inequality Constraints in Facility Location and Other Similar Optimization Problems: An Entropy Based Approach
Amber Srivastava, Gabriel Barsi Haberfeld, Naira Hovakimyan +1
In this paper we propose an annealing based framework to incorporate inequality constraints in optimization problems such as facility location, simultaneous facility location with…
A Clustering Approach to Edge Controller Placement in Software Defined Networks with Cost Balancing
Reza Soleymanifar, Amber Srivastava, Carolyn Beck +1
In this work we introduce two novel deterministic annealing based clustering algorithms to address the problem of Edge Controller Placement (ECP) in wireless edge networks. These n…
Multiway k-Cut in Static and Dynamic Graphs: A Maximum Entropy Principle Approach
Mayank Baranwal, Amber Srivastava, Srinivasa Salapaka
This work presents a maximum entropy principle based algorithm for solving minimum multiway -cut problem defined over static and dynamic {\em digraphs}. A multiway -cut probl…
On the Persistence of Clustering Solutions and True Number of Clusters in a Dataset
Amber Srivastava, Mayank Baranwal, Srinivasa Salapaka
Typically clustering algorithms provide clustering solutions with prespecified number of clusters. The lack of a priori knowledge on the true number of underlying clusters in the d…