Search Results for author: Mahnoosh Alizadeh

Found 18 papers, 2 papers with code

Optimistic Safety for Linearly-Constrained Online Convex Optimization

no code implementations9 Mar 2024 Spencer Hutchinson, Tianyi Chen, Mahnoosh Alizadeh

In this work, we consider a version of this problem with static linear constraints that the player receives noisy feedback of and must always satisfy.

Directional Optimism for Safe Linear Bandits

1 code implementation29 Aug 2023 Spencer Hutchinson, Berkay Turan, Mahnoosh Alizadeh

Lastly, we introduce a generalization of the safe linear bandit setting where the constraints are convex and adapt our algorithms and analyses to this setting by leveraging a novel convex-analysis based approach.

Safe Pricing Mechanisms for Distributed Resource Allocation with Bandit Feedback

no code implementations28 Jul 2023 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 costs and improve reliability.

The Impact of the Geometric Properties of the Constraint Set in Safe Optimization with Bandit Feedback

no code implementations1 May 2023 Spencer Hutchinson, Berkay Turan, Mahnoosh Alizadeh

Simulation results for this algorithm support the sublinear regret bound and provide empirical evidence that the sharpness of the constraint set impacts the performance of the algorithm.

A Deployable Online Optimization Framework for EV Smart Charging with Real-World Test Cases

no code implementations23 Jul 2022 Nathaniel Tucker, Mahnoosh Alizadeh

We present a customizable online optimization framework for real-time EV smart charging to be readily implemented at real large-scale charging facilities.

Collaborative Multi-agent Stochastic Linear Bandits

no code implementations12 May 2022 Ahmadreza Moradipari, Mohammad Ghavamzadeh, Mahnoosh Alizadeh

We propose a distributed upper confidence bound (UCB) algorithm and prove a high probability bound on its $T$-round regret in which we include a linear growth of regret associated with each communication round.

Multi-Environment Meta-Learning in Stochastic Linear Bandits

no code implementations12 May 2022 Ahmadreza Moradipari, Mohammad Ghavamzadeh, Taha Rajabzadeh, Christos Thrampoulidis, Mahnoosh Alizadeh

In this work we investigate meta-learning (or learning-to-learn) approaches in multi-task linear stochastic bandit problems that can originate from multiple environments.

Meta-Learning

Real-Time Electric Vehicle Smart Charging at Workplaces: A Real-World Case Study

no code implementations14 Mar 2022 Nathaniel Tucker, Gustavo Cezar, Mahnoosh Alizadeh

We study a real-time smart charging algorithm for electric vehicles (EVs) at a workplace parking lot in order to minimize electricity cost from time-of-use electricity rates and demand charges while ensuring that the owners of the EVs receive adequate levels of charge.

An Online Scheduling Algorithm for a Community Energy Storage System

no code implementations5 Oct 2021 Nathaniel Tucker, Mahnoosh Alizadeh

We study the problem facing the manager of such a CES who must schedule the charging, discharging, and capacity reservations for numerous users.

Scheduling

Robust Distributed Optimization With Randomly Corrupted Gradients

no code implementations28 Jun 2021 Berkay Turan, Cesar A. Uribe, Hoi-To Wai, Mahnoosh Alizadeh

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 the participating agents are prone to failure.

Distributed Optimization

Feature and Parameter Selection in Stochastic Linear Bandits

no code implementations9 Jun 2021 Ahmadreza Moradipari, Berkay Turan, Yasin Abbasi-Yadkori, Mahnoosh Alizadeh, Mohammad Ghavamzadeh

In the second setting, the reward parameter of the LB problem is arbitrarily selected from $M$ models represented as (possibly) overlapping balls in $\mathbb R^d$.

feature selection Model Selection

The Division of Assets in Multiagent Systems: A Case Study in Team Blotto Games

no code implementations30 Mar 2021 Keith Paarporn, Rahul Chandan, Mahnoosh Alizadeh, Jason R. Marden

The focus of this paper is on problem (i), where we seek to characterize the impact of the division of resources on the best-case efficiency of the resulting collective behavior.

Stage-wise Conservative Linear Bandits

no code implementations NeurIPS 2020 Ahmadreza Moradipari, Christos Thrampoulidis, Mahnoosh Alizadeh

For this problem, we present two novel algorithms, stage-wise conservative linear Thompson Sampling (SCLTS) and stage-wise conservative linear UCB (SCLUCB), that respect the baseline constraints and enjoy probabilistic regret bounds of order O(\sqrt{T} \log^{3/2}T) and O(\sqrt{T} \log T), respectively.

Thompson Sampling

Regret Bound for Safe Gaussian Process Bandit Optimization

no code implementations L4DC 2020 Sanae Amani, Mahnoosh Alizadeh, Christos Thrampoulidis

Many applications require a learner to make sequential decisions given uncertainty regarding both the system’s payoff function and safety constraints.

Gaussian Processes

Regret Bounds for Safe Gaussian Process Bandit Optimization

no code implementations5 May 2020 Sanae Amani, Mahnoosh Alizadeh, Christos Thrampoulidis

Many applications require a learner to make sequential decisions given uncertainty regarding both the system's payoff function and safety constraints.

Gaussian Processes

Safe Linear Thompson Sampling with Side Information

no code implementations6 Nov 2019 Ahmadreza Moradipari, Sanae Amani, Mahnoosh Alizadeh, Christos Thrampoulidis

We compare the performance of our algorithm with UCB-based safe algorithms and highlight how the inherently randomized nature of TS leads to a superior performance in expanding the set of safe actions the algorithm has access to at each round.

Thompson Sampling

Linear Stochastic Bandits Under Safety Constraints

no code implementations NeurIPS 2019 Sanae Amani, Mahnoosh Alizadeh, Christos Thrampoulidis

During the pure exploration phase the learner chooses her actions at random from a restricted set of safe actions with the goal of learning a good approximation of the entire unknown safe set.

Safe Exploration

On the interaction between Autonomous Mobility-on-Demand systems and the power network: models and coordination algorithms

1 code implementation14 Sep 2017 Federico Rossi, Ramon Iglesias, Mahnoosh Alizadeh, Marco Pavone

We assess the performance of the mode by studying a hypothetical AMoD system in Dallas-Fort Worth and its impact on the Texas power network.

Systems and Control Multiagent Systems Robotics

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