12 citations · 50 across the 23 of their papers we have counts for
13 papers · 1 filter
Automatic Debiased Learning from Positive, Unlabeled, and Exposure Data
Masahiro Kato, Shuting Wu, Kodai Kureishi +1
We address the issue of binary classification from positive and unlabeled data (PU classification) with a selection bias in the positive data. During the observation process, (i) a…
Asymptotically Optimal Fixed-Budget Best Arm Identification with Variance-Dependent Bounds
Masahiro Kato, Masaaki Imaizumi, Takuya Ishihara +1
We investigate the problem of fixed-budget best arm identification (BAI) for minimizing expected simple regret. In an adaptive experiment, a decision maker draws one of multiple tr…
Best Arm Identification with Contextual Information under a Small Gap
Masahiro Kato, Masaaki Imaizumi, Takuya Ishihara +1
We study the best-arm identification (BAI) problem with a fixed budget and contextual (covariate) information. In each round of an adaptive experiment, after observing contextual i…
Unified Perspective on Probability Divergence via Maximum Likelihood Density Ratio Estimation: Bridging KL-Divergence and Integral Probability Metrics
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-ra…
Rate-optimal Bayesian Simple Regret in Best Arm Identification
Junpei Komiyama, Kaito Ariu, Masahiro Kato +1
We consider best arm identification in the multi-armed bandit problem. Assuming certain continuity conditions of the prior, we characterize the rate of the Bayesian simple regret.…
Scalable Personalised Item Ranking through Parametric Density Estimation
Riku Togashi, Masahiro Kato, Mayu Otani +2
Learning from implicit feedback is challenging because of the difficult nature of the one-class problem: we can observe only positive examples. Most conventional methods use a pair…
ATRO: Adversarial Training with a Rejection Option
Masahiro Kato, Zhenghang Cui, Yoshihiro Fukuhara
This paper proposes a classification framework with a rejection option to mitigate the performance deterioration caused by adversarial examples. While recent machine learning algor…
Off-Policy Evaluation of Bandit Algorithm from Dependent Samples under Batch Update Policy
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. However, because the contextual bandit algorithm updates t…
A Practical Guide of Off-Policy Evaluation for Bandit Problems
Masahiro Kato, Kenshi Abe, Kaito Ariu +1
Off-policy evaluation (OPE) is the problem of estimating the value of a target policy from samples obtained via different policies. Recently, applying OPE methods for bandit proble…
The Adaptive Doubly Robust Estimator for Policy Evaluation in Adaptive Experiments and a Paradox Concerning Logging Policy
Masahiro Kato, Shota Yasui, Kenichiro McAlinn
The doubly robust (DR) estimator, which consists of two nuisance parameters, the conditional mean outcome and the logging policy (the probability of choosing an action), is crucial…
Learning Classifiers under Delayed Feedback with a Time Window Assumption
Masahiro Kato, Shota Yasui
We consider training a binary classifier under delayed feedback (\emph{DF learning}). For example, in the conversion prediction in online ads, we initially receive negative samples…
Non-Negative Bregman Divergence Minimization for Deep Direct Density Ratio Estimation
Masahiro Kato, Takeshi Teshima
Density ratio estimation (DRE) is at the core of various machine learning tasks such as anomaly detection and domain adaptation. In existing studies on DRE, methods based on Bregma…
Alternate Estimation of a Classifier and the Class-Prior from Positive and Unlabeled Data
Masahiro Kato, Liyuan Xu, Gang Niu +1
We consider a problem of learning a binary classifier only from positive data and unlabeled data (PU learning) and estimating the class-prior in unlabeled data under the case-contr…