no code implementations • 10 Mar 2024 • Ziye Ma, Ying Chen, Javad Lavaei, Somayeh Sojoudi
Matrix sensing problems exhibit pervasive non-convexity, plaguing optimization with a proliferation of suboptimal spurious solutions.
no code implementations • 17 May 2023 • Baturalp Yalcin, Javad Lavaei, Murat Arcak
In this paper, we study the system identification problem for linear discrete-time systems under adversaries and analyze two lasso-type estimators.
no code implementations • 15 Feb 2023 • Donghao Ying, Yuhao Ding, Alec Koppel, Javad Lavaei
The objective is to find a localized policy that maximizes the average of the team's local utility functions without the full observability of each agent in the team.
Multi-agent Reinforcement Learning reinforcement-learning +1
no code implementations • 15 Feb 2023 • Ziye Ma, Igor Molybog, Javad Lavaei, Somayeh Sojoudi
This paper studies the role of over-parametrization in solving non-convex optimization problems.
no code implementations • 19 Nov 2022 • Yuhao Ding, Ming Jin, Javad Lavaei
We study risk-sensitive reinforcement learning (RL) based on an entropic risk measure in episodic non-stationary Markov decision processes (MDPs).
no code implementations • 4 Oct 2022 • Han Feng, Baturalp Yalcin, Javad Lavaei
We study the identification of a linear time-invariant dynamical system affected by large-and-sparse disturbances modeling adversarial attacks or faults.
no code implementations • 15 Aug 2022 • Baturalp Yalcin, Ziye Ma, Javad Lavaei, Somayeh Sojoudi
In this paper, we shed light on some major differences between these two methods.
no code implementations • 22 May 2022 • Donghao Ying, Mengzi Amy Guo, Yuhao Ding, Javad Lavaei, Zuo-Jun Max Shen
We study convex Constrained Markov Decision Processes (CMDPs) in which the objective is concave and the constraints are convex in the state-action occupancy measure.
no code implementations • 28 Jan 2022 • Yuhao Ding, Javad Lavaei
We consider primal-dual-based reinforcement learning (RL) in episodic constrained Markov decision processes (CMDPs) with non-stationary objectives and constraints, which plays a central role in ensuring the safety of RL in time-varying environments.
no code implementations • NeurIPS 2021 • Haixiang Zhang, Zeyu Zheng, Javad Lavaei
When applied to a stochastic submodular function, the computational complexity of the proposed algorithms is lower than that of the existing stochastic submodular minimization algorithms.
no code implementations • 19 Oct 2021 • Baturalp Yalcin, Haixiang Zhang, Javad Lavaei, Somayeh Sojoudi
It is well-known that the Burer-Monteiro (B-M) factorization approach can efficiently solve low-rank matrix optimization problems under the RIP condition.
no code implementations • 19 Oct 2021 • Yuhao Ding, Junzi Zhang, Javad Lavaei
For the generic Fisher-non-degenerate policy parametrizations, our result is the first single-loop and finite-batch PG algorithm achieving $\tilde{O}(\epsilon^{-3})$ global optimality sample complexity.
no code implementations • 19 Oct 2021 • Yuhao Ding, Junzi Zhang, Javad Lavaei
Our result is the first global convergence and sample complexity results for the stochastic entropy-regularized vanilla PG method.
no code implementations • 17 Oct 2021 • Donghao Ying, Yuhao Ding, Javad Lavaei
We study entropy-regularized constrained Markov decision processes (CMDPs) under the soft-max parameterization, in which an agent aims to maximize the entropy-regularized value function while satisfying constraints on the expected total utility.
no code implementations • 18 May 2021 • Ziye Ma, Yingjie Bi, Javad Lavaei, Somayeh Sojoudi
By analyzing the landscape of the non-convex problem, we first propose a global guarantee on the maximum distance between an arbitrary local minimizer and the ground truth under the assumption that the RIP constant is smaller than $1/2$.
no code implementations • 31 May 2020 • Igor Molybog, Javad Lavaei
The paper studies the complexity of the optimization problem behind the Model-Agnostic Meta-Learning (MAML) algorithm.
1 code implementation • 19 Nov 2019 • Yi Ouyang, Richard Y. Zhang, Javad Lavaei, Pravin Varaiya
The offset optimization problem seeks to coordinate and synchronize the timing of traffic signals throughout a network in order to enhance traffic flow and reduce stops and delays.
Optimization and Control Systems and Control Systems and Control
no code implementations • 7 Jan 2019 • Richard Y. Zhang, Somayeh Sojoudi, Javad Lavaei
Using the technique, we prove that in the case of a rank-1 ground truth, an RIP constant of $\delta<1/2$ is both necessary and sufficient for exact recovery from any arbitrary initial point (such as a random point).
no code implementations • 26 Oct 2018 • Ming Jin, Javad Lavaei
We investigate the important problem of certifying stability of reinforcement learning policies when interconnected with nonlinear dynamical systems.
no code implementations • NeurIPS 2018 • Richard Y. Zhang, Cédric Josz, Somayeh Sojoudi, Javad Lavaei
When the linear measurements of an instance of low-rank matrix recovery satisfy a restricted isometry property (RIP)---i. e. they are approximately norm-preserving---the problem is known to contain no spurious local minima, so exact recovery is guaranteed.
1 code implementation • 10 Oct 2017 • Richard Y. Zhang, Javad Lavaei
Clique tree conversion solves large-scale semidefinite programs by splitting an $n\times n$ matrix variable into up to $n$ smaller matrix variables, each representing a principal submatrix of up to $\omega\times\omega$.
Optimization and Control