no code implementations • NAACL (SocialNLP) 2021 • Hayato Kobayashi, Hiroaki Taguchi, Yoshimune Tabuchi, Chahine Koleejan, Ken Kobayashi, Soichiro Fujita, Kazuma Murao, Takeshi Masuyama, Taichi Yatsuka, Manabu Okumura, Satoshi Sekine
Ranking the user comments posted on a news article is important for online news services because comment visibility directly affects the user experience.
1 code implementation • 30 Nov 2023 • Haruka Kiyohara, Ren Kishimoto, Kosuke Kawakami, Ken Kobayashi, Kazuhide Nakata, Yuta Saito
Existing evaluation metrics for OPE estimators primarily focus on the "accuracy" of OPE or that of downstream policy selection, neglecting risk-return tradeoff in the subsequent online policy deployment.
1 code implementation • 30 Nov 2023 • Haruka Kiyohara, Ren Kishimoto, Kosuke Kawakami, Ken Kobayashi, Kazuhide Nakata, Yuta Saito
This paper introduces SCOPE-RL, a comprehensive open-source Python software designed for offline reinforcement learning (offline RL), off-policy evaluation (OPE), and selection (OPS).
no code implementations • 14 Jul 2023 • Keisho Oh, Naoki Nishimura, Minje Sung, Ken Kobayashi, Kazuhide Nakata
On the basis of this observation, we extend the IPW estimator and propose a new estimator, named two-sided IPW, to address the position bases in two-sided markets.
no code implementations • 28 Apr 2023 • Kentaro Kanamori, Takuya Takagi, Ken Kobayashi, Yuichi Ike
Then, we propose a new framework of CE, named Counterfactual Explanation by Pairs of Imputation and Action (CEPIA), that enables users to obtain valid actions even with missing values and clarifies how actions are affected by imputation of the missing values.
no code implementations • 23 May 2022 • Akiyoshi Sannai, Yasunari Hikima, Ken Kobayashi, Akinori Tanaka, Naoki Hamada
In this paper, we propose a strategy to construct a multi-objective optimization algorithm from a single-objective optimization algorithm by using the B\'ezier simplex model.
1 code implementation • 29 Mar 2022 • Ryoji Tanabe, Youhei Akimoto, Ken Kobayashi, Hiroshi Umeki, Shinichi Shirakawa, Naoki Hamada
The first phase in TPB aims to approximate a few Pareto optimal solutions by optimizing a sequence of single-objective scalar problems.
no code implementations • 10 Apr 2021 • Akinori Tanaka, Akiyoshi Sannai, Ken Kobayashi, Naoki Hamada
B\'ezier simplex fitting algorithms have been recently proposed to approximate the Pareto set/front of multi-objective continuous optimization problems.
1 code implementation • 22 Dec 2020 • Kentaro Kanamori, Takuya Takagi, Ken Kobayashi, Yuichi Ike, Kento Uemura, Hiroki Arimura
One of the popular methods is Counterfactual Explanation (CE), also known as Actionable Recourse, which provides a user with a perturbation vector of features that alters the prediction result.
no code implementations • NeurIPS Workshop TDA_and_Beyond 2020 • Mari Kajitani, Ken Kobayashi, Yuichi Ike, Takehiko Yamanashi, Yuhei Umeda, Yoshimasa Kadooka, Gen Shinozaki
We propose a new scoring algorithm for detecting delirium from one-channel EEG, based on topological data analysis.
no code implementations • 30 Jul 2020 • Tomokaze Shiratori, Ken Kobayashi, Yuichi Takano
Previous methods for making coherent forecasts consist of two phases: first computing base (incoherent) forecasts and then reconciling those forecasts based on their inherent hierarchical structure.
no code implementations • 17 Jun 2019 • Akinori Tanaka, Akiyoshi Sannai, Ken Kobayashi, Naoki Hamada
In this paper, we analyze the asymptotic risks of those B\'ezier simplex fitting methods and derive the optimal subsample ratio for the inductive skeleton fitting.
no code implementations • NAACL 2019 • Kazuma Murao, Ken Kobayashi, Hayato Kobayashi, Taichi Yatsuka, Takeshi Masuyama, Tatsuru Higurashi, Yoshimune Tabuchi
There have been many studies on neural headline generation models trained with a lot of (article, headline) pairs.