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Zhao Kang

5 papers hereh-index 336 citations6 works total

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

author position
  • middle author5

Across the 5 of 5 papers where every author was matched, so the position is known.

fields
  • cs.LG4
  • cs.CL1
same name
  • Zhao Kang — 27 papers, h 33
  • Zhao Kang — 16 papers, h 22
  • Zhao Kang — 14 papers, h 7
  • Zhao Kang — 10 papers, h 6
  • Zhao Kang — 5 papers, h 3
  • Zhao Kang — 2 papers, h 2

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

most citedTieFake: Title-Text Similarity and Emotion-Aware Fake News Detection

3 citations · 3 across the 5 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2025

ITPP: Learning Disentangled Event Dynamics in Marked Temporal Point Processes

Wang-Tao Zhou, Zhao Kang, Ke Yan +1

Marked Temporal Point Processes (MTPPs) provide a principled framework for modeling asynchronous event sequences by conditioning on the history of past events. However, most existi…

cs.LG2025

Fine-grained Spatio-temporal Event Prediction with Self-adaptive Anchor Graph

Wang-Tao Zhou, Zhao Kang, Sicong Liu +2

Event prediction tasks often handle spatio-temporal data distributed in a large spatial area. Different regions in the area exhibit different characteristics while having latent co…

cs.LG2023

Non-Autoregressive Diffusion-based Temporal Point Processes for Continuous-Time Long-Term Event Prediction

Wang-Tao Zhou, Zhao Kang, Ling Tian

Continuous-time long-term event prediction plays an important role in many application scenarios. Most existing works rely on autoregressive frameworks to predict event sequences,…

cs.LG2023

Intensity-free Convolutional Temporal Point Process: Incorporating Local and Global Event Contexts

Wang-Tao Zhou, Zhao Kang, Ling Tian +1

Event prediction in the continuous-time domain is a crucial but rather difficult task. Temporal point process (TPP) learning models have shown great advantages in this area. Existi…

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