1 citations · 1 across the 3 of their papers we have counts for
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Pointwise or Pairwise: When Do Pairwise Losses Help Reward Learning, Provably?
Junghyun Lee, Minsoo Ha, Sanghwa Kim +4
Pairwise losses are increasingly used for reward learning even when pointwise rewards are observed, with mixed empirical results. When and why do pairwise losses outperform pointwi…
Uniform Race: Parameter-Free Approximate Rejection Sampling
Seiyun Shin, Juhyeong Pang, Kwang-Sung Jun
We study approximate sampling: given independent samples from a proposal distribution , the goal is to select one whose distribution is close to a target specified only…
Dynamic DBSCAN with Euler Tour Sequences
Seiyun Shin, Ilan Shomorony, Peter Macgregor
We propose a fast and dynamic algorithm for Density-Based Spatial Clustering of Applications with Noise (DBSCAN) that efficiently supports online updates. Traditional DBSCAN algori…
Transfer Learning in Bandits with Latent Continuity
Hyejin Park, Seiyun Shin, Kwang-Sung Jun +1
Structured stochastic multi-armed bandits provide accelerated regret rates over the standard unstructured bandit problems. Most structured bandits, however, assume the knowledge of…