activity
20222024
most citedThe landscape of deterministic and stochastic optimal control problems: One-shot Optimization versus Dynamic Programming

3 citations · 6 across the 10 of their papers we have counts for

collaborators

10 papers

eess.SY2024

Online Bandit Nonlinear Control with Dynamic Batch Length and Adaptive Learning Rate

Jihun Kim, Javad Lavaei

This paper is concerned with the online bandit nonlinear control, which aims to learn the best stabilizing controller from a pool of stabilizing and destabilizing controllers of un…

math.OC20243 cited

The landscape of deterministic and stochastic optimal control problems: One-shot Optimization versus Dynamic Programming

Jihun Kim, Yuhao Ding, Yingjie Bi +1

Optimal control problems can be solved via a one-shot (single) optimization or a sequence of optimization using dynamic programming (DP). However, the computation of their global o…

math.OC2024

Absence of spurious solutions far from ground truth: A low-rank analysis with high-order losses

Ziye Ma, Ying Chen, Javad Lavaei +1

Matrix sensing problems exhibit pervasive non-convexity, plaguing optimization with a proliferation of suboptimal spurious solutions. Avoiding convergence to these critical points…

cs.LG2023

Tempo Adaptation in Non-stationary Reinforcement Learning

Hyunin Lee, Yuhao Ding, Jongmin Lee +3

We first raise and tackle a ``time synchronization'' issue between the agent and the environment in non-stationary reinforcement learning (RL), a crucial factor hindering its real-…

math.OC20231 cited

Algorithmic Regularization in Tensor Optimization: Towards a Lifted Approach in Matrix Sensing

Ziye Ma, Javad Lavaei, Somayeh Sojoudi

Gradient descent (GD) is crucial for generalization in machine learning models, as it induces implicit regularization, promoting compact representations. In this work, we examine t…

cs.LG20231 cited

Scalable Primal-Dual Actor-Critic Method for Safe Multi-Agent RL with General Utilities

Donghao Ying, Yunkai Zhang, Yuhao Ding +2

We investigate safe multi-agent reinforcement learning, where agents seek to collectively maximize an aggregate sum of local objectives while satisfying their own safety constraint…