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researcher

Yakun Wang

5 papers hereh-index 12 citations6 works total

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

author position
  • first author3
  • middle author1
  • last author1

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

fields
  • stat.ML3
  • cs.LG1
  • eess.SP1
same name
  • Yakun Wang — 4 papers, h 2
  • Yakun Wang — 3 papers, h 1
  • Yakun Wang — 2 papers, h 2
  • Yakun Wang — 1 paper, h 1
  • Yakun Wang — 1 paper, h 4
  • Yakun Wang — 1 paper, 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

5 papers

cs.LG2026

Zero-Flow Two-Sample Tests

Yakun Wang, Leyang Wang, Song Liu +1

We propose a new approach to two-sample testing for deciding whether two sets of samples are drawn from the same distribution. The test is built on a statistical discrepancy based…

stat.ML2026

Direct Fisher Score Estimation for Likelihood Maximization

Sherman Khoo, Yakun Wang, Song Liu +1

We study the problem of likelihood maximization when the likelihood function is intractable but model simulations are readily available. We propose a sequential, gradient-based opt…

stat.ML2026

Zero-Flow Encoders

Yakun Wang, Leyang Wang, Song Liu +1

Flow-based methods have achieved significant success in various generative modeling tasks, capturing nuanced details within complex data distributions. However, few existing works…

eess.SP2025

Machine Learning Based Probe Skew Correction for High-frequency BH Loop Measurements

Yakun Wang, Song Liu, Jun Wang +2

Experimental characterization of magnetic components has grown to be increasingly important to understand and model their behaviours in high-frequency PWM converters. The BH loop m…

stat.ML2025

Guiding Time-Varying Generative Models with Natural Gradients on Exponential Family Manifold

Song Liu, Leyang Wang, Yakun Wang

Optimising probabilistic models is a well-studied field in statistics. However, its connection with the training of generative models remains largely under-explored. In this paper,…

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