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Baihan Lin

Columbia University

22 papers hereh-index 17885 citations75 works total

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

author position
  • sole author10
  • first author9
  • middle author2
  • last author1

Across the 22 of 22 papers where every author was matched, so the position is known.

fields
  • q-bio.NC6
  • cs.CL4
  • cs.LG4
  • cs.CY2
  • cs.HC2
  • cs.AI1
affiliations
  • Columbia University
Homepage
same name
  • Baihan Lin — 7 papers
  • Baihan Lin — 3 papers, h 5
  • Baihan Lin — 2 papers, h 1
  • Baihan Lin — 1 paper

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 citedComputational Inference in Cognitive Science: Operational, Societal and Ethical Considerations

5 citations · 14 across the 13 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2022★ 4 cited

Geometric and Topological Inference for Deep Representations of Complex Networks

Baihan Lin

Understanding the deep representations of complex networks is an important step of building interpretable and trustworthy machine learning applications in the age of internet. Glob…

cs.LG2020

Online Semi-Supervised Learning in Contextual Bandits with Episodic Reward

Baihan Lin

We considered a novel practical problem of online learning with episodically revealed rewards, motivated by several real-world applications, where the contexts are nonstationary ov…

cs.LG2019

Split Q Learning: Reinforcement Learning with Two-Stream Rewards

Baihan Lin, Djallel Bouneffouf, Guillermo Cecchi

Drawing an inspiration from behavioral studies of human decision making, we propose here a general parametric framework for a reinforcement learning problem, which extends the stan…

cs.LG2019

A Story of Two Streams: Reinforcement Learning Models from Human Behavior and Neuropsychiatry

Baihan Lin, Guillermo Cecchi, Djallel Bouneffouf +2

Drawing an inspiration from behavioral studies of human decision making, we propose here a more general and flexible parametric framework for reinforcement learning that extends st…

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