Search Results for author: Deepan Muthirayan

Found 18 papers, 2 papers with code

Long-term Fairness For Real-time Decision Making: A Constrained Online Optimization Approach

no code implementations4 Jan 2024 Ruijie Du, Deepan Muthirayan, Pramod P. Khargonekar, Yanning Shen

However, the widespread integration of machine learning also makes it necessary to ensure machine learning-driven decision-making systems do not violate ethical principles and values of society in which they operate.

Decision Making Fairness

Online Learning for Incentive-Based Demand Response

no code implementations27 Mar 2023 Deepan Muthirayan, Pramod P. Khargonekar

We consider the problem of learning online to estimate the baseline and to optimize the operating costs over a period of time under such incentives.

counterfactual

Scalable Grid-Aware Dynamic Matching using Deep Reinforcement Learning

no code implementations31 Jan 2023 Majid Majidi, Deepan Muthirayan, Masood Parvania, Pramod P. Khargonekar

The central agent then solves an optimal power flow problem with the IHRs as the nodes, with their active power flow and reactive power {capacities}, and grid constraints to scalably determine the final flows such that matched power can be delivered to the extent the grid constraints are satisfied.

reinforcement-learning Reinforcement Learning (RL)

Online Convex Optimization with Long Term Constraints for Predictable Sequences

no code implementations30 Oct 2022 Deepan Muthirayan, Jianjun Yuan, Pramod P. Khargonekar

While many algorithmic advances have been made towards online optimization with long term constraints, these algorithms typically assume that the sequence of cost functions over a certain $T$ finite steps that determine the cost to the online learner are adversarially generated.

Change Point Detection Approach for Online Control of Unknown Time Varying Dynamical Systems

no code implementations21 Oct 2022 Deepan Muthirayan, Ruijie Du, Yanning Shen, Pramod P. Khargonekar

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 dynamical systems.

Change Point Detection

Competing Bandits in Time Varying Matching Markets

no code implementations21 Oct 2022 Deepan Muthirayan, Chinmay Maheshwari, Pramod P. Khargonekar, Shankar Sastry

We study the problem of online learning in two-sided non-stationary matching markets, where the objective is to converge to a stable match.

Meta-Learning Online Control for Linear Dynamical Systems

no code implementations18 Aug 2022 Deepan Muthirayan, Dileep Kalathil, Pramod P. Khargonekar

We show that when the number of tasks are sufficiently large, our proposed approach achieves a meta-regret that is smaller by a factor $D/D^{*}$ compared to an independent-learning online control algorithm which does not perform learning across the tasks, where $D$ is a problem constant and $D^{*}$ is a scalar that decreases with increase in the similarity between tasks.

Meta-Learning

Online Learning for Predictive Control with Provable Regret Guarantees

no code implementations30 Nov 2021 Deepan Muthirayan, Jianjun Yuan, Dileep Kalathil, Pramod P. Khargonekar

Specifically, we study the online learning problem where the control algorithm does not know the true system model and has only access to a fixed-length (that does not grow with the control horizon) preview of the future cost functions.

Model Predictive Control

Spatio-Temporal Scene-Graph Embedding for Autonomous Vehicle Collision Prediction

2 code implementations11 Nov 2021 Arnav V. Malawade, Shih-Yuan Yu, Brandon Hsu, Deepan Muthirayan, Pramod P. Khargonekar, Mohammad A. Al Faruque

Finally, we demonstrate that sg2vec performs inference 9. 3x faster with an 88. 0% smaller model, 32. 4% less power, and 92. 8% less energy than the state-of-the-art method on the industry-standard Nvidia DRIVE PX 2 platform, making it more suitable for implementation on the edge.

Autonomous Vehicles Graph Embedding

Graph Learning for Cognitive Digital Twins in Manufacturing Systems

no code implementations17 Sep 2021 Trier Mortlock, Deepan Muthirayan, Shih-Yuan Yu, Pramod P. Khargonekar, Mohammad A. Al Faruque

In this paper, we detail the cognitive digital twin as the next stage of advancement of a digital twin that will help realize the vision of Industry 4. 0.

Graph Learning

Online Learning Robust Control of Nonlinear Dynamical Systems

no code implementations8 Jun 2021 Deepan Muthirayan, Pramod P. Khargonekar

We show that when the controller has preview of the cost functions and the disturbances for a short duration of time and the system is known $R^p_T(\gamma) = O(1)$ when $\gamma \geq \gamma_c$, where $\gamma_c = \mathcal{O}(\overline{\gamma})$.

Generative Adversarial Imitation Learning for Empathy-based AI

no code implementations27 May 2021 Pratyush Muthukumar, Karishma Muthukumar, Deepan Muthirayan, Pramod Khargonekar

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 environments.

Imitation Learning Language Modelling +3

Dynamic Matching Markets in Power Grid: Concepts and Solution using Deep Reinforcement Learning

no code implementations12 Apr 2021 Majid Majidi, Deepan Muthirayan, Masood Parvania, Pramod P. Khargonekar

This paper proposes an alternative to bulk load flexibility options for managing uncertainty in power markets: a reinforcement learning based dynamic matching market.

reinforcement-learning Reinforcement Learning (RL)

Neuroscience-Inspired Algorithms for the Predictive Maintenance of Manufacturing Systems

no code implementations23 Feb 2021 Arnav V. Malawade, Nathan D. Costa, Deepan Muthirayan, Pramod P. Khargonekar, Mohammad A. Al Faruque

If machine failures can be detected preemptively, then maintenance and repairs can be performed more efficiently, reducing production costs.

Anomaly Detection

Meta-Learning Guarantees for Online Receding Horizon Learning Control

no code implementations21 Oct 2020 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, where in each iteration the system to be controlled is a linear deterministic system that is different and unknown, the cost for the controller in an iteration is a general additive cost function and the control input is required to be constrained, which if violated incurs an additional cost.

Meta-Learning

Regret Guarantees for Online Receding Horizon Learning Control

no code implementations14 Oct 2020 Deepan Muthirayan, Jianjun Yuan, Pramod P. Khargonekar

In this paper we provide provable regret guarantees for an online learning receding horizon type control policy in a setting where the system to be controlled is an unknown linear dynamical system, the cost for the controller is a general additive function over a finite period $T$, and there exist control input constraints that when violated incur an additional cost.

Optimization and Control Systems and Control Systems and Control

Scene-Graph Augmented Data-Driven Risk Assessment of Autonomous Vehicle Decisions

3 code implementations31 Aug 2020 Shih-Yuan Yu, Arnav V. Malawade, Deepan Muthirayan, Pramod P. Khargonekar, Mohammad A. Al Faruque

Finally, we demonstrate that the use of spatial and temporal attention layers improves our model's performance by 2. 7% and 0. 7% respectively, and increases its explainability.

Autonomous Driving Scene Classification

A Meta-Learning Control Algorithm with Provable Finite-Time Guarantees

no code implementations30 Aug 2020 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 controlled is a linear deterministic system that is different and unknown, the cost for the controller in an iteration is a general additive cost function and the control input is required to be constrained, which if violated incurs an additional cost.

Meta-Learning

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