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Ming Jin

9 papers hereh-index 14851 citations35 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author1
  • middle author4
  • last author4

Across the 9 of 9 papers where every author was matched, so the position is known.

fields
  • cs.LG4
  • eess.SY2
  • cs.AI1
  • eess.SP1
  • math.OC1
same name
  • Ming Jin — 22 papers, h 12
  • Ming Jin — 16 papers, h 6
  • Ming Jin — 14 papers, h 5
  • Ming Jin — 12 papers, h 6
  • Ming Jin — 11 papers, h 10
  • Ming Jin — 8 papers, h 3

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20192024
most citedImitation Learning with Stability and Safety Guarantees

7 citations · 9 across the 5 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2024

A CMDP-within-online framework for Meta-Safe Reinforcement Learning

Vanshaj Khattar, Yuhao Ding, Bilgehan Sel +2

Meta-reinforcement learning has widely been used as a learning-to-learn framework to solve unseen tasks with limited experience. However, the aspect of constraint violations has no…

cs.LG2024

Pausing Policy Learning in Non-stationary Reinforcement Learning

Hyunin Lee, Ming Jin, Javad Lavaei +1

Real-time inference is a challenge of real-world reinforcement learning due to temporal differences in time-varying environments: the system collects data from the past, updates th…

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-…

cs.LG2022

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…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.