Search Results for author: Masahiro Kato

Found 28 papers, 5 papers with code

Spatially-Varying Bayesian Predictive Synthesis for Flexible and Interpretable Spatial Prediction

no code implementations10 Mar 2022 Danielle Cabel, Shonosuke Sugasawa, Masahiro Kato, Kosaku Takanashi, Kenichiro McAlinn

Spatial data are characterized by their spatial dependence, which is often complex, non-linear, and difficult to capture with a single model.

Model Selection Variational Inference

Benign-Overfitting in Conditional Average Treatment Effect Prediction with Linear Regression

no code implementations10 Feb 2022 Masahiro Kato, Masaaki Imaizumi

We study the benign overfitting theory in the prediction of the conditional average treatment effect (CATE), with linear regression models.

Causal Inference

Unified Perspective on Probability Divergence via Maximum Likelihood Density Ratio Estimation: Bridging KL-Divergence and Integral Probability Metrics

no code implementations31 Jan 2022 Masahiro Kato, Masaaki Imaizumi, Kentaro Minami

This paper provides a unified perspective for the Kullback-Leibler (KL)-divergence and the integral probability metrics (IPMs) from the perspective of maximum likelihood density-ratio estimation (DRE).

Density Ratio Estimation

Optimal Best Arm Identification in Two-Armed Bandits with a Fixed Budget under a Small Gap

no code implementations12 Jan 2022 Masahiro Kato, Kaito Ariu, Masaaki Imaizumi, Masahiro Nomura, Chao Qin

One of the longstanding open questions is a tight lower bound on the probability of misidentifying the best arm and a strategy whose upper bound matches the lower bound when the optimal target allocation ratio of arm draws is unknown.

Causal Inference

Optimal Simple Regret in Bayesian Best Arm Identification

1 code implementation18 Nov 2021 Junpei Komiyama, Kaito Ariu, Masahiro Kato, Chao Qin

We consider Bayesian best arm identification in the multi-armed bandit problem.

Policy Choice and Best Arm Identification: Asymptotic Analysis of Exploration Sampling

no code implementations16 Sep 2021 Kaito Ariu, Masahiro Kato, Junpei Komiyama, Kenichiro McAlinn, Chao Qin

We consider the "policy choice" problem -- otherwise known as best arm identification in the bandit literature -- proposed by Kasy and Sautmann (2021) for adaptive experimental design.

Decision Making Experimental Design

The Role of Contextual Information in Best Arm Identification

1 code implementation26 Jun 2021 Masahiro Kato, Kaito Ariu

We demonstrate that contextual information can be used to improve the efficiency of the identification of the best marginalized mean reward compared with the results of Garivier & Kaufmann (2016).

Scalable Personalised Item Ranking through Parametric Density Estimation

no code implementations11 May 2021 Riku Togashi, Masahiro Kato, Mayu Otani, Tetsuya Sakai, Shin'ichi Satoh

However, such methods have two main drawbacks particularly in large-scale applications; (1) the pairwise approach is severely inefficient due to the quadratic computational cost; and (2) even recent model-based samplers (e. g. IRGAN) cannot achieve practical efficiency due to the training of an extra model.

Density Estimation Learning-To-Rank

Adaptive Doubly Robust Estimator from Non-stationary Logging Policy under a Convergence of Average Probability

no code implementations17 Feb 2021 Masahiro Kato

To mitigate this limitation, we propose another assumption that the average logging policy converges to a time-invariant function and show the doubly robust (DR) estimator's asymptotic normality.

Counterfactual Inference

Density-Ratio Based Personalised Ranking from Implicit Feedback

no code implementations19 Jan 2021 Riku Togashi, Masahiro Kato, Mayu Otani, Shin'ichi Satoh

Learning from implicit user feedback is challenging as we can only observe positive samples but never access negative ones.

Density Ratio Estimation

ATRO: Adversarial Training with a Rejection Option

no code implementations24 Oct 2020 Masahiro Kato, Zhenghang Cui, Yoshihiro Fukuhara

In this paper, in order to acquire a more reliable classifier against adversarial attacks, we propose the method of Adversarial Training with a Rejection Option (ATRO).

Off-Policy Evaluation of Bandit Algorithm from Dependent Samples under Batch Update Policy

no code implementations23 Oct 2020 Masahiro Kato, Yusuke Kaneko

The goal of off-policy evaluation (OPE) is to evaluate a new policy using historical data obtained via a behavior policy.

A Practical Guide of Off-Policy Evaluation for Bandit Problems

no code implementations23 Oct 2020 Masahiro Kato, Kenshi Abe, Kaito Ariu, Shota Yasui

Based on the properties of the evaluation policy, we categorize OPE situations.

Mean-Variance Efficient Reinforcement Learning by Expected Quadratic Utility Maximization

no code implementations3 Oct 2020 Masahiro Kato, Kei Nakagawa, Kenshi Abe, Tetsuro Morimura

To achieve this purpose, we train an agent to maximize the expected quadratic utility function, a common objective of risk management in finance and economics.

Decision Making Decision Making Under Uncertainty +1

Policy Gradient with Expected Quadratic Utility Maximization: A New Mean-Variance Approach in Reinforcement Learning

no code implementations28 Sep 2020 Masahiro Kato, Kei Nakagawa

In this paper, we suggest expected quadratic utility maximization (EQUM) as a new framework for policy gradient style reinforcement learning (RL) algorithms with mean-variance control.

Decision Making reinforcement-learning

Learning Classifiers under Delayed Feedback with a Time Window Assumption

no code implementations28 Sep 2020 Masahiro Kato, Shota Yasui

We consider training a binary classifier under delayed feedback (\emph{DF learning}).

Selection bias

Non-Negative Bregman Divergence Minimization for Deep Direct Density Ratio Estimation

1 code implementation12 Jun 2020 Masahiro Kato, Takeshi Teshima

Density ratio estimation (DRE) is at the core of various machine learning tasks such as anomaly detection and domain adaptation.

Anomaly Detection Density Ratio Estimation +2

Confidence Interval for Off-Policy Evaluation from Dependent Samples via Bandit Algorithm: Approach from Standardized Martingales

no code implementations12 Jun 2020 Masahiro Kato

The goal of OPE is to evaluate a new policy using historical data obtained from behavior policies generated by the bandit algorithm.

Off-Policy Evaluation and Learning for External Validity under a Covariate Shift

1 code implementation NeurIPS 2020 Masahiro Kato, Masatoshi Uehara, Shota Yasui

Then, we propose doubly robust and efficient estimators for OPE and OPL under a covariate shift by using a nonparametric estimator of the density ratio between the historical and evaluation data distributions.

Efficient Adaptive Experimental Design for Average Treatment Effect Estimation

no code implementations13 Feb 2020 Masahiro Kato, Takuya Ishihara, Junya Honda, Yusuke Narita

In adaptive experimental design, the experimenter is allowed to change the probability of assigning a treatment using past observations for estimating the ATE efficiently.

Experimental Design

Model Specification Test with Unlabeled Data: Approach from Covariate Shift

no code implementations2 Nov 2019 Masahiro Kato, Hikaru Kawarazaki

By applying the proposed method, we can obtain a model that predicts the label for the unlabeled test data well without losing the interpretability of the model.

Learning with Protection: Rejection of Suspicious Samples under Adversarial Environment

no code implementations25 Sep 2019 Masahiro Kato, Yoshihiro Fukuhara, Hirokatsu Kataoka, Shigeo Morishima

Our main idea is to apply a framework of learning with rejection and adversarial examples to assist in the decision making for such suspicious samples.

Decision Making Multi-class Classification +1

Learning from Positive and Unlabeled Data with a Selection Bias

1 code implementation ICLR 2019 Masahiro Kato, Takeshi Teshima, Junya Honda

However, this assumption is unrealistic in many instances of PU learning because it fails to capture the existence of a selection bias in the labeling process.

Selection bias

Alternate Estimation of a Classifier and the Class-Prior from Positive and Unlabeled Data

no code implementations15 Sep 2018 Masahiro Kato, Liyuan Xu, Gang Niu, Masashi Sugiyama

In this paper, we propose a novel unified approach to estimating the class-prior and training a classifier alternately.

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