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Young-Han Kim

8 papers hereh-index 338 citations14 works total

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

author position
  • middle author3
  • last author5

Across the 8 of 8 papers where every author was matched, so the position is known.

fields
  • cs.IT3
  • math.ST2
  • cs.IR1
  • cs.LG1
  • q-bio.GN1
same name
  • Young-Han Kim — 44 papers, h 30
  • Young-Han Kim — 3 papers, h 3
  • Young-Han Kim — 3 papers, h 8

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
20152022
most citedEnergy-Based Sequence GANs for Recommendation and Their Connection to Imitation Learning

8 citations · 17 across the 8 of their papers we have counts for

collaborators
Showing 2022Show all

4 papers · 1 filter

cs.IT2022★ 1 cited

An Information-Theoretic Proof of the Kac--Bernstein Theorem

J. Jon Ryu, Young-Han Kim

A short, information-theoretic proof of the Kac--Bernstein theorem, which is stated as follows, is presented: For any independent random variables X and Y, if X+Y and X−Y a…

cs.IT2022

On Universal Portfolios with Continuous Side Information

Alankrita Bhatt, J. Jon Ryu, Young-Han Kim

A new portfolio selection strategy that adapts to a continuous side-information sequence is presented, with a universal wealth guarantee against a class of state-constant rebalance…

cs.IT2022★ 1 cited

Parameter-free Online Linear Optimization with Side Information via Universal Coin Betting

J. Jon Ryu, Alankrita Bhatt, Young-Han Kim

A class of parameter-free online linear optimization algorithms is proposed that harnesses the structure of an adversarial sequence by adapting to some side information. These algo…

math.ST2022

Minimax Optimal Algorithms with Fixed-k-Nearest Neighbors

J. Jon Ryu, Young-Han Kim

This paper presents how to perform minimax optimal classification, regression, and density estimation based on fixed-k nearest neighbor (NN) searches. We consider a distributed l…

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