204 citations · 414 across the 37 of their papers we have counts for
40 papers
First-Order Optimization as Minimum-Time Control
Liraz Mudrik, Isaac Kaminer, Pramod P. Khargonekar
We formulate first-order optimization as a minimum-time control problem. The iterate is the state, the update, a combination of the gradients observed so far, is the control, and t…
Long-term Fairness For Real-time Decision Making: A Constrained Online Optimization Approach
Ruijie Du, Deepan Muthirayan, Pramod P. Khargonekar +1
Machine learning (ML) has demonstrated remarkable capabilities across many real-world systems, from predictive modeling to intelligent automation. However, the widespread integrati…
Online Learning for Incentive-Based Demand Response
Deepan Muthirayan, Pramod P. Khargonekar
In this paper, we consider the problem of learning online to manage Demand Response (DR) resources. A typical DR mechanism requires the DR manager to assign a baseline to the parti…
Scalable Grid-Aware Dynamic Matching using Deep Reinforcement Learning
Majid Majidi, Deepan Muthirayan, Masood Parvania +1
This paper proposes a two-level hierarchical matching framework for Integrated Hybrid Resources (IHRs) with grid constraints. An IHR is a collection of Renewable Energy Sources (RE…
Online Convex Optimization with Long Term Constraints for Predictable Sequences
Deepan Muthirayan, Jianjun Yuan, Pramod P. Khargonekar
In this paper, we investigate the framework of Online Convex Optimization (OCO) for online learning. OCO offers a very powerful online learning framework for many applications. In…
Competing Bandits in Time Varying Matching Markets
Deepan Muthirayan, Chinmay Maheshwari, Pramod P. Khargonekar +1
We study the problem of online learning in two-sided non-stationary matching markets, where the objective is to converge to a stable match. In particular, we consider the setting w…
Change Point Detection Approach for Online Control of Unknown Time Varying Dynamical Systems
Deepan Muthirayan, Ruijie Du, Yanning Shen +1
We propose a novel change point detection approach for online learning control with full information feedback (state, disturbance, and cost feedback) for unknown time-varying dynam…
Meta-Learning Online Control for Linear Dynamical Systems
Deepan Muthirayan, Dileep Kalathil, Pramod P. Khargonekar
In this paper, we consider the problem of finding a meta-learning online control algorithm that can learn across the tasks when faced with a sequence of (similar) control tasks…
Mixed-Precision Neural Networks: A Survey
Mariam Rakka, Mohammed E. Fouda, Pramod Khargonekar +1
Mixed-precision Deep Neural Networks achieve the energy efficiency and throughput needed for hardware deployment, particularly when the resources are limited, without sacrificing a…
Optimal Storage and Solar Capacity of a Residential Household under Net Metering and Time-of-Use Pricing
Victor Sam Moses Babu K., Pratyush Chakraborty, Enrique Baeyens +1
Incentive programs and ongoing reduction in costs are driving joint installation of solar PV panels and storage systems in residential households. There is a need for optimal inves…
Spatio-Temporal Scene-Graph Embedding for Autonomous Vehicle Collision Prediction
Arnav V. Malawade, Shih-Yuan Yu, Brandon Hsu +3
In autonomous vehicles (AVs), early warning systems rely on collision prediction to ensure occupant safety. However, state-of-the-art methods using deep convolutional networks eith…
Online Robust Control of Linear Dynamical Systems with Limited Prediction
Deepan Muthirayan, Dileep Kalathil, Pramod P. Khargonekar
We study the online robust control problem for linear dynamical systems with disturbances and uncertainties in the cost functions, with limited preview of the future disturbances a…
Online Learning for Predictive Control with Provable Regret Guarantees
Deepan Muthirayan, Jianjun Yuan, Dileep Kalathil +1
We study the problem of online learning in predictive control of an unknown linear dynamical system with time varying cost functions which are unknown apriori. Specifically, we stu…
Graph Learning for Cognitive Digital Twins in Manufacturing Systems
Trier Mortlock, Deepan Muthirayan, Shih-Yuan Yu +2
Future manufacturing requires complex systems that connect simulation platforms and virtualization with physical data from industrial processes. Digital twins incorporate a physica…
Online Learning Robust Control of Nonlinear Dynamical Systems
Deepan Muthirayan, Pramod P. Khargonekar
In this work we address the problem of the online robust control of nonlinear dynamical systems perturbed by disturbance. We study the problem of attenuation of the total cost over…
Online Algorithms for Network Robustness under Connectivity Constraints
Deepan Muthirayan, Pramod P. Khargonekar
In this paper, we present algorithms for designing networks that are robust to node failures with minimal or limited number of links. We present algorithms for both the static netw…
Generative Adversarial Imitation Learning for Empathy-based AI
Pratyush Muthukumar, Karishma Muthukumar, Deepan Muthirayan +1
Generative adversarial imitation learning (GAIL) is a model-free algorithm that has been shown to provide strong results in imitating complex behaviors in high-dimensional environm…
Dynamic Matching Markets in Power Grid: Concepts and Solution using Deep Reinforcement Learning
Majid Majidi, Deepan Muthirayan, Masood Parvania +1
Traditional bulk load flexibility options, such as load shifting and load curtailment, for managing uncertainty in power markets limit the diversity of options and ignore the prefe…
Adaptive Gradient Online Control
Deepan Muthirayan, Jianjun Yuan, Pramod P. Khargonekar
In this work we consider the online control of a known linear dynamic system with adversarial disturbance and adversarial controller cost. The goal in online control is to minimize…
Cognitive Preadaptation for Resilient Adaptive Control
Deepan Muthirayan, Pramod P. Khargonekar
In this paper, we investigate a novel control architecture and algorithm for incorporating preadaption functions. We propose a preadaptation mechanism that can augment any adaptive…
Meta-Learning Guarantees for Online Receding Horizon Learning Control
Deepan Muthirayan, Pramod P. Khargonekar
In this paper we provide provable regret guarantees for an online meta-learning receding horizon control algorithm in an iterative control setting. We consider the setting where, i…
Balancing Exploration for Online Receding Horizon Learning Control with Provable Regret Guarantees
Deepan Muthirayan, Jianjun Yuan, Pramod P. Khargonekar
We address the problem of simultaneously learning and control in an online receding horizon control setting. We consider the control of an unknown linear dynamical system with gene…
Scene-Graph Augmented Data-Driven Risk Assessment of Autonomous Vehicle Decisions
Shih-Yuan Yu, Arnav V. Malawade, Deepan Muthirayan +2
Despite impressive advancements in Autonomous Driving Systems (ADS), navigation in complex road conditions remains a challenging problem. There is considerable evidence that evalua…
A Meta-Learning Control Algorithm with Provable Finite-Time Guarantees
Deepan Muthirayan, Pramod Khargonekar
In this work we provide provable regret guarantees for an online meta-learning control algorithm in an iterative control setting, where in each iteration the system to be controlle…