no code implementations • 30 Aug 2024 • Haixiang Zhang, Baturalp Yalcin, Javad Lavaei, Eduardo D. Sontag

In this work, we study the system identification problem for parameterized non-linear systems using basis functions under adversarial attacks.

no code implementations • 26 May 2024 • Vanshaj Khattar, Yuhao Ding, Bilgehan Sel, Javad Lavaei, Ming Jin

Meta-reinforcement learning has widely been used as a learning-to-learn framework to solve unseen tasks with limited experience.

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, Haixiang Zhang, Javad Lavaei, Murat Arcak

We highlight that the attack vectors are allowed to be correlated with each other in this work, whereas we make some assumptions about the times at which the attacks happen.

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 • 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
**+2**

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.

1 code implementation • 22 May 2022 • Donghao Ying, Mengzi Amy Guo, Hyunin Lee, Yuhao Ding, Javad Lavaei, Zuo-Jun Max Shen

In the exact setting, we prove an $O(T^{-1/3})$ convergence rate for both the average optimality gap and constraint violation, which further improves to $O(T^{-1/2})$ under strong concavity of the objective in the 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 • 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, Hyunin Lee, Javad Lavaei

Our result is the first global convergence and sample complexity results for the stochastic entropy-regularized vanilla PG method.

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 • 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

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