Search Results for author: Ken Kobayashi

Found 13 papers, 4 papers with code

Towards Assessing and Benchmarking Risk-Return Tradeoff of Off-Policy Evaluation

1 code implementation30 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.

Benchmarking counterfactual +1

SCOPE-RL: A Python Library for Offline Reinforcement Learning and Off-Policy Evaluation

1 code implementation30 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).

Offline RL Off-policy evaluation

An IPW-based Unbiased Ranking Metric in Two-sided Markets

no code implementations14 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.

Learning-To-Rank Position +1

Counterfactual Explanation with Missing Values

no code implementations28 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.

counterfactual Counterfactual Explanation +2

Bézier Flow: a Surface-wise Gradient Descent Method for Multi-objective Optimization

no code implementations23 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.

PAC learning

A Two-phase Framework with a Bézier Simplex-based Interpolation Method for Computationally Expensive Multi-objective Optimization

1 code implementation29 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.

Approximate Bayesian Computation of Bézier Simplices

no code implementations10 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.

Ordered Counterfactual Explanation by Mixed-Integer Linear Optimization

1 code implementation22 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.

counterfactual Counterfactual Explanation +1

Prediction of hierarchical time series using structured regularization and its application to artificial neural networks

no code implementations30 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.

Computational Efficiency Time Series +1

Asymptotic Risk of Bezier Simplex Fitting

no code implementations17 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.

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