40 citations · 82 across the 13 of their papers we have counts for
13 papers
A Safe First-Order Method for Pricing-Based Resource Allocation in Safety-Critical Networks
Berkay Turan, Spencer Hutchinson, Mahnoosh Alizadeh
We introduce a novel algorithm for solving network utility maximization (NUM) problems that arise in resource allocation schemes over networks with known safety-critical constraint…
Directional Optimism for Safe Linear Bandits
Spencer Hutchinson, Berkay Turan, Mahnoosh Alizadeh
The safe linear bandit problem is a version of the classical stochastic linear bandit problem where the learner's actions must satisfy an uncertain constraint at all rounds. Due it…
Safe Pricing Mechanisms for Distributed Resource Allocation with Bandit Feedback
Spencer Hutchinson, Berkay Turan, Mahnoosh Alizadeh
In societal-scale infrastructures, such as electric grids or transportation networks, pricing mechanisms are often used as a way to shape users' demand in order to lower operating…
The Impact of the Geometric Properties of the Constraint Set in Safe Optimization with Bandit Feedback
Spencer Hutchinson, Berkay Turan, Mahnoosh Alizadeh
We consider a safe optimization problem with bandit feedback in which an agent sequentially chooses actions and observes responses from the environment, with the goal of maximizing…
Safe Dual Gradient Method for Network Utility Maximization Problems
Berkay Turan, Mahnoosh Alizadeh
In this paper, we introduce a novel first-order dual gradient algorithm for solving network utility maximization problems that arise in resource allocation schemes over networks wi…
Robust Distributed Optimization With Randomly Corrupted Gradients
Berkay Turan, Cesar A. Uribe, Hoi-To Wai +1
In this paper, we propose a first-order distributed optimization algorithm that is provably robust to Byzantine failures-arbitrary and potentially adversarial behavior, where all t…
Feature and Parameter Selection in Stochastic Linear Bandits
Ahmadreza Moradipari, Berkay Turan, Yasin Abbasi-Yadkori +2
We study two model selection settings in stochastic linear bandits (LB). In the first setting, which we refer to as feature selection, the expected reward of the LB problem is in t…
On Robustness of the Normalized Subgradient Method with Randomly Corrupted Subgradients
Berkay Turan, Cesar A. Uribe, Hoi-To Wai +1
Numerous modern optimization and machine learning algorithms rely on subgradient information being trustworthy and hence, they may fail to converge when such information is corrupt…
Competition in Electric Autonomous Mobility on Demand Systems
Berkay Turan, Mahnoosh Alizadeh
This paper investigates the impacts of competition in autonomous mobility-on-demand systems. By adopting a network-flow based formulation, we first determine the optimal strategies…
Resilient Primal-Dual Optimization Algorithms for Distributed Resource Allocation
Berkay Turan, Cesar A. Uribe, Hoi-To Wai +1
Distributed algorithms for multi-agent resource allocation can provide privacy and scalability over centralized algorithms in many cyber-physical systems. However, the distributed…
Dynamic Pricing and Fleet Management for Electric Autonomous Mobility on Demand Systems
Berkay Turan, Ramtin Pedarsani, Mahnoosh Alizadeh
The proliferation of ride sharing systems is a major drive in the advancement of autonomous and electric vehicle technologies. This paper considers the joint routing, battery charg…
Online Charge Scheduling for Electric Vehicles in Autonomous Mobility on Demand Fleets
Nathaniel Tucker, Berkay Turan, Mahnoosh Alizadeh
In this paper, we study an online charge scheduling strategy for fleets of autonomous-mobility-on-demand electric vechicles (AMoD EVs). We consider the case where vehicles complete…
Smart Charging Benefits in Autonomous Mobility on Demand Systems
Berkay Turan, Nathaniel Tucker, Mahnoosh Alizadeh
In this paper, we study the potential benefits from smart charging for a fleet of electric vehicles (EVs) providing autonomous mobility-on-demand (AMoD) services. We first consider…