no code implementations • 10 Mar 2024 • Quanyan Zhu, Tamer Basar
The article concludes by discussing the interplay between robustness and resilience, suggesting that a comprehensive theory of resilience and quantification metrics, and formalization through game-theoretic frameworks are necessary.
no code implementations • 20 Oct 2023 • Sebin Gracy, Ji Liu, Tamer Basar, Cesar A. Uribe
We identify a sufficient condition for exponential convergence to the disease-free equilibrium (DFE).
no code implementations • 15 May 2023 • Ciyuan Zhang, Sebin Gracy, Tamer Basar, Philip E. Pare
Second, we propose an observation model which captures the summation of all the viruses' infection levels in each node, which represents the individuals who are infected by different viruses but share similar symptoms.
no code implementations • 14 Dec 2022 • Yongqiang Wang, Tamer Basar
The new algorithm allows the incorporation of persistent additive noise to enable rigorous differential privacy for data samples, gradients, and intermediate optimization variables without losing provable convergence, and thus circumventing the dilemma of trading accuracy for privacy in differential privacy design.
no code implementations • 7 Aug 2022 • Yongqiang Wang, Tamer Basar
In combination with the presented quantization scheme, the proposed algorithm ensures, for the first time, rigorous differential privacy in decentralized stochastic optimization without losing provable convergence accuracy.
no code implementations • 1 Apr 2022 • Ciyuan Zhang, Sebin Gracy, Tamer Basar, Philip E. Pare
This paper proposes a novel discrete-time multi-virus SIR (susceptible-infected-recovered) model that captures the spread of competing SIR epidemics over a population network.
no code implementations • 23 Dec 2021 • Navjot Singh, Xuanyu Cao, Suhas Diggavi, Tamer Basar
The paper develops algorithms and obtains performance bounds for two different models of local information availability at the nodes: (i) sample feedback, where each node has direct access to samples of the local random variable to evaluate its local cost, and (ii) bandit feedback, where samples of the random variables are not available, but only the values of the local cost functions at two random points close to the decision are available to each node.
no code implementations • 29 Sep 2021 • Weichao Mao, Tamer Basar, Lin Yang, Kaiqing Zhang
Many real-world applications of multi-agent reinforcement learning (RL), such as multi-robot navigation and decentralized control of cyber-physical systems, involve the cooperation of agents as a team with aligned objectives.
no code implementations • NeurIPS 2021 • Muhammed O. Sayin, Kaiqing Zhang, David S. Leslie, Tamer Basar, Asuman Ozdaglar
The key challenge in this decentralized setting is the non-stationarity of the environment from an agent's perspective, since both her own payoffs and the system evolution depend on the actions of other agents, and each agent adapts her policies simultaneously and independently.
no code implementations • 17 May 2021 • Tao Li, Guanze Peng, Quanyan Zhu, Tamer Basar
In addition to existing research works on game-theoretic learning over networks, we highlight several new angles and research endeavors on learning in games that are related to recent developments in artificial intelligence.
no code implementations • 23 Dec 2020 • Jorge I. Poveda, Miroslav Krstic, Tamer Basar
We introduce a novel class of Nash equilibrium seeking dynamics for non-cooperative games with a finite number of players, where the convergence to the Nash equilibrium is bounded by a KL function with a settling time that can be upper bounded by a positive constant that is independent of the initial conditions of the players, and which can be prescribed a priori by the system designer.
Optimization and Control
no code implementations • NeurIPS 2020 • Dongsheng Ding, Kaiqing Zhang, Tamer Basar, Mihailo Jovanovic
To the best of our knowledge, our work is the first to establish non-asymptotic convergence guarantees of policy-based primal-dual methods for solving infinite-horizon discounted CMDPs.
no code implementations • NeurIPS 2020 • Kaiqing Zhang, Tao Sun, Yunzhe Tao, Sahika Genc, Sunil Mallya, Tamer Basar
In contrast, we model the problem as a robust Markov game, where the goal of all agents is to find policies such that no agent has the incentive to deviate, i. e., reach some equilibrium point, which is also robust to the possible uncertainty of the MARL model.
no code implementations • NeurIPS 2020 • Kaiqing Zhang, Bin Hu, Tamer Basar
We find: i) the conventional RARL framework (Pinto et al., 2017) can learn a destabilizing policy if the initial policy does not enjoy the robust stability property against the adversary; and ii) with robustly stabilizing initializations, our proposed double-loop RARL algorithm provably converges to the global optimal cost while maintaining robust stability on-the-fly.
no code implementations • 28 Sep 2020 • Weichao Mao, Kaiqing Zhang, Ruihao Zhu, David Simchi-Levi, Tamer Basar
We consider model-free reinforcement learning (RL) in non-stationary Markov decision processes (MDPs).
no code implementations • L4DC 2020 • Kaiqing Zhang, Bin Hu, Tamer Basar
In this paper, we study the convergence theory of PO for $\mathcal{H}_{2}$ linear control with $\mathcal{H}_{\infty}$ robustness guarantee.
no code implementations • 24 Mar 2020 • Naci Saldi, Tamer Basar, Maxim Raginsky
In this paper, we consider discrete-time partially observed mean-field games with the risk-sensitive optimality criterion.
no code implementations • 22 Jul 2019 • Muhammed O. Sayin, Tamer Basar
We also quantify the approximation error for a quantized version of a continuous distribution and show that a semi-definite program relaxation of the equivalent problem could be a benchmark lower bound for the sender's cost for large state spaces.
Computer Science and Game Theory Optimization and Control
1 code implementation • 15 Mar 2019 • Wesley Suttle, Zhuoran Yang, Kaiqing Zhang, Zhaoran Wang, Tamer Basar, Ji Liu
This paper extends off-policy reinforcement learning to the multi-agent case in which a set of networked agents communicating with their neighbors according to a time-varying graph collaboratively evaluates and improves a target policy while following a distinct behavior policy.
no code implementations • 30 Jan 2019 • Muhammed O. Sayin, Chung-Wei Lin, Eunsuk Kang, Shinichi Shiraishi, Tamer Basar
Recently, vision-based road sign classification algorithms have been shown to be vulnerable against (even) small scale adversarial interventions that are imperceptible for humans.
no code implementations • 3 Jan 2014 • Garrett Warnell, Sourabh Bhattacharya, Rama Chellappa, Tamer Basar
We provide two novel adaptive-rate compressive sensing (CS) strategies for sparse, time-varying signals using side information.