5 citations · 8 across the 7 of their papers we have counts for
16 papers
Non-stationary Risk-sensitive Reinforcement Learning: Near-optimal Dynamic Regret, Adaptive Detection, and Separation Design
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). Both the reward functions and the…
Learning of Dynamical Systems under Adversarial Attacks -- Null Space Property Perspective
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. Under the assumption that…
Factorization Approach for Low-complexity Matrix Completion Problems: Exponential Number of Spurious Solutions and Failure of Gradient Methods
Baturalp Yalcin, Haixiang Zhang, Javad Lavaei +1
It is well-known that the Burer-Monteiro (B-M) factorization approach can efficiently solve low-rank matrix optimization problems under the RIP condition. It is natural to ask whet…
General Low-rank Matrix Optimization: Geometric Analysis and Sharper Bounds
Haixiang Zhang, Yingjie Bi, Javad Lavaei
This paper considers the global geometry of general low-rank minimization problems via the Burer-Monterio factorization approach. For the rank- case, we prove that there is no s…
Global and Local Analyses of Nonlinear Low-Rank Matrix Recovery Problems
Yingjie Bi, Javad Lavaei
The restricted isometry property (RIP) is a well-known condition that guarantees the absence of spurious local minima in low-rank matrix recovery problems with linear measurements.…
When Does MAML Objective Have Benign Landscape?
Igor Molybog, Javad Lavaei
The paper studies the complexity of the optimization problem behind the Model-Agnostic Meta-Learning (MAML) algorithm. The goal of the study is to determine the global convergence…