no code implementations • 9 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.
1 code implementation • 29 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.
no code implementations • 28 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.
no code implementations • 1 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.
no code implementations • 23 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.
no code implementations • 12 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.
no code implementations • 12 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.
no code implementations • 14 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.
no code implementations • 5 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.
no code implementations • 28 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.
no code implementations • 9 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 $\mathbb R^d$.
no code implementations • 30 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.
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.
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.
no code implementations • 5 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.
no code implementations • 6 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.
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.
1 code implementation • 14 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