38 citations · 148 across the 20 of their papers we have counts for
20 papers
A More Accurate Algorithm Comparison through A/B Testing using Offline Evaluation Methods
Koki Konishi, Masataka Ushiku, Yuta Saito
A/B testing is the gold standard for selecting the better algorithm in online services. While offline evaluation has attracted attention as a safer alternative due to the high expe…
Off-Policy Evaluation of Ranking Policies under Diverse User Behavior
Haruka Kiyohara, Masatoshi Uehara, Yusuke Narita +3
Ranking interfaces are everywhere in online platforms. There is thus an ever growing interest in their Off-Policy Evaluation (OPE), aiming towards an accurate performance evaluatio…
Off-Policy Evaluation for Large Action Spaces via Conjunct Effect Modeling
Yuta Saito, Qingyang Ren, Thorsten Joachims
We study off-policy evaluation (OPE) of contextual bandit policies for large discrete action spaces where conventional importance-weighting approaches suffer from excessive varianc…
Policy-Adaptive Estimator Selection for Off-Policy Evaluation
Takuma Udagawa, Haruka Kiyohara, Yusuke Narita +2
Off-policy evaluation (OPE) aims to accurately evaluate the performance of counterfactual policies using only offline logged data. Although many estimators have been developed, the…
Fair Ranking as Fair Division: Impact-Based Individual Fairness in Ranking
Yuta Saito, Thorsten Joachims
Rankings have become the primary interface in two-sided online markets. Many have noted that the rankings not only affect the satisfaction of the users (e.g., customers, listeners,…
A Real-World Implementation of Unbiased Lift-based Bidding System
Daisuke Moriwaki, Yuta Hayakawa, Akira Matsui +3
In display ad auctions of Real-Time Bid-ding (RTB), a typical Demand-Side Platform (DSP)bids based on the predicted probability of click and conversion right after an ad impression…
Doubly Robust Off-Policy Evaluation for Ranking Policies under the Cascade Behavior Model
Haruka Kiyohara, Yuta Saito, Tatsuya Matsuhiro +3
In real-world recommender systems and search engines, optimizing ranking decisions to present a ranked list of relevant items is critical. Off-policy evaluation (OPE) for ranking p…
Off-Policy Evaluation for Large Action Spaces via Embeddings
Yuta Saito, Thorsten Joachims
Off-policy evaluation (OPE) in contextual bandits has seen rapid adoption in real-world systems, since it enables offline evaluation of new policies using only historic log data. U…
Data-Driven Off-Policy Estimator Selection: An Application in User Marketing on An Online Content Delivery Service
Yuta Saito, Takuma Udagawa, Kei Tateno
Off-policy evaluation (OPE) is the method that attempts to estimate the performance of decision making policies using historical data generated by different policies without conduc…
Accelerating Offline Reinforcement Learning Application in Real-Time Bidding and Recommendation: Potential Use of Simulation
Haruka Kiyohara, Kosuke Kawakami, Yuta Saito
In recommender systems (RecSys) and real-time bidding (RTB) for online advertisements, we often try to optimize sequential decision making using bandit and reinforcement learning (…
Evaluating the Robustness of Off-Policy Evaluation
Yuta Saito, Takuma Udagawa, Haruka Kiyohara +3
Off-policy Evaluation (OPE), or offline evaluation in general, evaluates the performance of hypothetical policies leveraging only offline log data. It is particularly useful in app…
Optimal Off-Policy Evaluation from Multiple Logging Policies
Nathan Kallus, Yuta Saito, Masatoshi Uehara
We study off-policy evaluation (OPE) from multiple logging policies, each generating a dataset of fixed size, i.e., stratified sampling. Previous work noted that in this setting th…
Open Bandit Dataset and Pipeline: Towards Realistic and Reproducible Off-Policy Evaluation
Yuta Saito, Shunsuke Aihara, Megumi Matsutani +1
Off-policy evaluation (OPE) aims to estimate the performance of hypothetical policies using data generated by a different policy. Because of its huge potential impact in practice,…
Unbiased Lift-based Bidding System
Daisuke Moriwaki, Yuta Hayakawa, Isshu Munemasa +2
Conventional bidding strategies for online display ad auction heavily relies on observed performance indicators such as clicks or conversions. A bidding strategy naively pursuing t…
Efficient Hyperparameter Optimization under Multi-Source Covariate Shift
Masahiro Nomura, Yuta Saito
A typical assumption in supervised machine learning is that the train (source) and test (target) datasets follow completely the same distribution. This assumption is, however, ofte…
Towards Resolving Propensity Contradiction in Offline Recommender Learning
Yuta Saito, Masahiro Nomura
We study offline recommender learning from explicit rating feedback in the presence of selection bias. A current promising solution for the bias is the inverse propensity score (IP…
Dual Learning Algorithm for Delayed Conversions
Yuta Saito, Gota Morishita, Shota Yasui
In display advertising, predicting the conversion rate (CVR), meaning the probability that a user takes a predefined action on an advertiser's website, is a fundamental task for es…
Asymmetric Tri-training for Debiasing Missing-Not-At-Random Explicit Feedback
Yuta Saito
In most real-world recommender systems, the observed rating data are subject to selection bias, and the data are thus missing-not-at-random. Developing a method to facilitate the l…
Counterfactual Cross-Validation: Stable Model Selection Procedure for Causal Inference Models
Yuta Saito, Shota Yasui
We study the model selection problem in conditional average treatment effect (CATE) prediction. Unlike previous works on this topic, we focus on preserving the rank order of the pe…
Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback
Yuta Saito, Suguru Yaginuma, Yuta Nishino +2
Recommender systems widely use implicit feedback such as click data because of its general availability. Although the presence of clicks signals the users' preference to some exten…