no code implementations • 4 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.
no code implementations • 27 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.
no code implementations • 31 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.
no code implementations • 30 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.
no code implementations • 21 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.
no code implementations • 21 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.
no code implementations • 18 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.
no code implementations • 30 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.
2 code implementations • 11 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.
no code implementations • 17 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.
no code implementations • 8 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 $\gamma_c = \mathcal{O}(\overline{\gamma})$.
no code implementations • 27 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.
no code implementations • 12 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.
no code implementations • 23 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.
no code implementations • 21 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.
no code implementations • 14 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
3 code implementations • 31 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.
no code implementations • 30 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.