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researcher

Kun Zhang

6 papers hereh-index 339 citations13 works total

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

author position
  • middle author5
  • last author1

Across the 6 of 6 papers where every author was matched, so the position is known.

fields
  • cs.LG4
  • cs.CV1
  • stat.ML1
same name
  • Kun Zhang — 14 papers, h 4
  • Kun Zhang — 13 papers, h 6
  • Kun Zhang — 10 papers, h 7
  • Kun Zhang — 10 papers, h 7
  • Kun Zhang — 10 papers, h 6
  • Kun Zhang — 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

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2026

TRACE: Trajectory Recovery for Continuous Mechanism Evolution in Causal Representation Learning

Shicheng Fan, Kun Zhang, Lu Cheng

Temporal causal representation learning methods assume that causal mechanisms switch instantaneously between discrete domains, yet real-world systems often exhibit continuous mecha…

cs.LG2026

Causal Discovery in Linear Models with Unobserved Variables and Measurement Error

Yuqin Yang, Mohamed Nafea, Negar Kiyavash +2

The presence of unobserved common causes and measurement error poses two major obstacles to causal structure learning, since ignoring either source of complexity can induce spuriou…

cs.LG2026

Diversified Scaling Inference in Time Series Foundation Models

Ruijin Hua, Zichuan Liu, Kun Zhang +1

The advancement of Time Series Foundation Models (TSFMs) has been driven primarily by large-scale pre-training, but inference-time compute potential remains largely untapped. This…

cs.LG2025

The Third Pillar of Causal Analysis? A Measurement Perspective on Causal Representations

Dingling Yao, Shimeng Huang, Riccardo Cadei +2

Causal reasoning and discovery, two fundamental tasks of causal analysis, often face challenges in applications due to the complexity, noisiness, and high-dimensionality of real-wo…

◍wovepaper

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