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Peng Zhao

29 papers hereh-index 221.5k citations58 works total

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

author position
  • first author9
  • middle author17

Across the 26 of 29 papers where every author was matched, so the position is known.

fields
  • cs.LG28
  • cs.DB1
same name
  • Peng Zhao — 19 papers, h 13
  • Peng Zhao — 10 papers, h 2
  • Peng Zhao — 8 papers, h 5
  • Peng Zhao — 8 papers, h 3
  • Peng Zhao — 7 papers, h 12
  • Peng Zhao — 7 papers, h 16

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
20172026
most citedAdaptivity and Non-stationarity: Problem-dependent Dynamic Regret for Online Convex Optimization

12 citations · 22 across the 18 of their papers we have counts for

collaborators
Showing 2020Show all

4 papers · 1 filter

cs.LG2020

Storage Fit Learning with Feature Evolvable Streams

Bo-Jian Hou, Yu-Hu Yan, Peng Zhao +1

Feature evolvable learning has been widely studied in recent years where old features will vanish and new features will emerge when learning with streams. Conventional methods usua…

cs.LG2020

Dynamic Regret of Convex and Smooth Functions

Peng Zhao, Yu-Jie Zhang, Lijun Zhang +1

We investigate online convex optimization in non-stationary environments and choose the dynamic regret as the performance measure, defined as the difference between cumulative loss…

cs.LG2020

Improved Analysis for Dynamic Regret of Strongly Convex and Smooth Functions

Peng Zhao, Lijun Zhang

In this paper, we present an improved analysis for dynamic regret of strongly convex and smooth functions. Specifically, we investigate the Online Multiple Gradient Descent (OMGD)…

cs.LG2020

Exploratory Machine Learning with Unknown Unknowns

Peng Zhao, Jia-Wei Shan, Yu-Jie Zhang +1

In conventional supervised learning, a training dataset is given with ground-truth labels from a known label set, and the learned model will classify unseen instances to known labe…

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