101 citations · 125 across the 21 of their papers we have counts for
29 papers
Distributionally Robust Federated Learning with Multi-Source Data
Yingzhu Liu, Zhongkui Li, Pengcheng You +1
Federated learning trains a shared model from private client data. In practice, data-generating distributions may differ, and the true mixture across clients is often unknown, maki…
Stability of Information-Based Routing in Dynamic Transportation Networks
Shaya Garjani, Ashish Cherukuri, Bayu Jayawardhana +1
Recent studies on transportation networks have shown that real-time route guidance can inadvertently induce congestion or oscillatory traffic patterns. Nevertheless, such technolog…
Distributed alternating gradient descent for convex semi-infinite programs over a network
Ashwin Aravind, Debasish Chatterjee, Ashish Cherukuri
This paper presents a first-order distributed algorithm for solving a convex semi-infinite program (SIP) over a time-varying network. In this setting, the objective function associ…
Wardrop equilibrium and Braess's paradox for varying demand
Jasper Verbree, Ashish Cherukuri
This work explores the relationship between the set of Wardrop equilibria~(WE) of a routing game, the total demand of that game, and the occurrence of Braess's paradox~(BP). The BP…
Wasserstein distributionally robust risk-constrained iterative MPC for motion planning: computationally efficient approximations
Alireza Zolanvari, Ashish Cherukuri
This paper considers a risk-constrained motion planning problem and aims to find the solution combining the concepts of iterative model predictive control (MPC) and data-driven dis…
Iterative risk-constrained model predictive control: A data-driven distributionally robust approach
Alireza Zolanvari, Ashish Cherukuri
This paper proposes an iterative distributionally robust model predictive control (MPC) scheme to solve a risk-constrained infinite-horizon optimal control problem. In each iterati…
Stochastic approximation approaches for CVaR-based variational inequalities
Jasper Verbree, Ashish Cherukuri
This paper considers variational inequalities (VI) defined by the conditional value-at-risk (CVaR) of uncertain functions and provides three stochastic approximation schemes to sol…
Cross apprenticeship learning framework: Properties and solution approaches
Ashwin Aravind, Debasish Chatterjee, Ashish Cherukuri
Apprenticeship learning is a framework in which an agent learns a policy to perform a given task in an environment using example trajectories provided by an expert. In the real wor…
Sample Average Approximation of Conditional Value-at-risk based Variational Inequalities
Ashish Cherukuri
This paper focuses on a class of variational inequalities (VIs), where the map defining the VI is given by the component-wise conditional value-at-risk (CVaR) of a random function.…
Data-driven distributionally robust optimization over a network via distributed semi-infinite programming
Ashish Cherukuri, Alireza Zolanvari, Goran Banjac +1
This paper focuses on solving a data-driven distributionally robust optimization problem over a network of agents. The agents aim to minimize the worst-case expected cost computed…
A two-step approach to Wasserstein distributionally robust chance- and security-constrained dispatch
Amin Maghami, Evrim Ursavas, Ashish Cherukuri
This paper considers a security constrained dispatch problem involving generation and line contingencies in the presence of the renewable generation. The uncertainty due to renewab…
Dynamic interventions with limited knowledge in network games
Mehran Shakarami, Ashish Cherukuri, Nima Monshizadeh
This paper studies the problem of intervention design for steering the actions of noncooperative players in quadratic network games to the social optimum. The players choose their…
Data-driven distributionally robust iterative risk-constrained model predictive control
Alireza Zolanvari, Ashish Cherukuri
This paper considers a risk-constrained infinite-horizon optimal control problem and proposes to solve it in an iterative manner. Each iteration of the algorithm generates a trajec…
Inferring the prior in routing games using public signalling
Jasper Verbree, Ashish Cherukuri
This paper considers Bayesian persuasion for routing games where information about the uncertain state of the network is provided by a traffic information system (TIS) using public…
Consistency of Distributionally Robust Risk- and Chance-Constrained Optimization under Wasserstein Ambiguity Sets
Ashish Cherukuri, Ashish R. Hota
We study stochastic optimization problems with chance and risk constraints, where in the latter, risk is quantified in terms of the conditional value-at-risk (CVaR). We consider th…
Steering the aggregative behavior of noncooperative agents: a nudge framework
Mehran Shakarami, Ashish Cherukuri, Nima Monshizadeh
This paper considers the problem of steering the aggregative behavior of a population of noncooperative price-taking agents towards a desired behavior. Different from conventional…
Stochastic approximation of CVaR-based variational inequalities
Jasper Verbree, Ashish Cherukuri
In this paper we study variational inequalities (VI) defined by the conditional value-at-risk (CVaR) of uncertain functions. We introduce stochastic approximation schemes that empl…
Wasserstein Distributionally Robust Look-Ahead Economic Dispatch
Bala Kameshwar Poolla, Ashish R. Hota, Saverio Bolognani +2
We consider the problem of look-ahead economic dispatch (LAED) with uncertain renewable energy generation. The goal of this problem is to minimize the cost of conventional energy g…
Sample average approximation of CVaR-based Wardrop equilibrium in routing under uncertain costs
Ashish Cherukuri
This paper focuses on the class of routing games that have uncertain costs. Assuming that agents are risk-averse and select paths with minimum conditional value-at-risk (CVaR) asso…
Data-Driven Chance Constrained Optimization under Wasserstein Ambiguity Sets
Ashish R. Hota, Ashish Cherukuri, John Lygeros
We present a data-driven approach for distributionally robust chance constrained optimization problems (DRCCPs). We consider the case where the decision maker has access to a finit…
Hybrid interconnection of iterative bidding and power network dynamics for frequency regulation and optimal dispatch
Tjerk Stegink, Ashish Cherukuri, Claudio De Persis +2
This paper considers a real-time electricity market involving an independent system operator (ISO) and a group of strategic generators. The ISO operates a market where generators b…
Frequency-driven market mechanisms for optimal dispatch in power networks
Tjerk Stegink, Ashish Cherukuri, Claudio De Persis +2
This paper studies real-time bidding mechanisms for economic dispatch and frequency regulation in electrical power networks. We consider a market administered by an independent sys…
Cooperative data-driven distributionally robust optimization
Ashish Cherukuri, Jorge Cortes
This paper studies a class of multiagent stochastic optimization problems where the objective is to minimize the expected value of a function which depends on a random variable. Th…
Iterative bidding in electricity markets: rationality and robustness
Ashish Cherukuri, Jorge Cortes
This paper studies an electricity market consisting of an independent system operator (ISO) and a group of generators. The goal is to solve the DC optimal power flow (DC-OPF) probl…