arXiv subjects

Yuta Saito

Publications and source records attributed to Yuta Saito.

At least 55 records · Page 3Linked to original sources

arXiv2022

Photo-induced Tellurium segregation in $\mathrm{MoTe_2}$

We report on the coherent phonon spectroscopy of polymorphic $\mathrm{MoTe_2}$ single crystals using a femtosecond-pulsed laser to investigate the relationship between structural phase transitions and photo-thermal effects induced by high-density laser excitation. Even when a femtosecond pulsed laser was used, which generally induces fewer heat accumulation effects than the case for a CW laser, tellurium segregation was observed in coherent phonon spectra with increasing excitation density, while no structural phase transition among polymorphs of $\mathrm{MoTe_2}$ was observed. The use of higher photon-energy excitation, however, was found to suppress tellurium segregation.

cond-mat.mtrl-sci↗

arXiv2021

Open Bandit Dataset and Pipeline: Towards Realistic and Reproducible Off-Policy Evaluation

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, there has been growing research interest in this field. There is, however, no real-world public dataset that enables the evaluation of OPE, making its experimental studies unrealistic and irreproducible. With the goal of enabling realistic and reproducible OPE research, we present Open Bandit Dataset, a public logged bandit dataset collected on a large-scale fashion e-commerce platform, ZOZOTOWN. Our dataset is unique in that it contains a set of multiple logged bandit datasets collected by running different policies on the same platform. This enables experimental comparisons of different OPE estimators for the first time. We also develop Python software called Open Bandit Pipeline to streamline and standardize the implementation of batch bandit algorithms and OPE. Our open data and software will contribute to fair and transparent OPE research and help the community identify fruitful research directions. We provide extensive benchmark experiments of existing OPE estimators using our dataset and software. The results open up essential challenges and new avenues for future OPE research.

cs.LG↗

arXiv2021

Accelerating Offline Reinforcement Learning Application in Real-Time Bidding and Recommendation: Potential Use of Simulation

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 (RL) techniques. In these applications, offline reinforcement learning (offline RL) and off-policy evaluation (OPE) are beneficial because they enable safe policy optimization using only logged data without any risky online interaction. In this position paper, we explore the potential of using simulation to accelerate practical research of offline RL and OPE, particularly in RecSys and RTB. Specifically, we discuss how simulation can help us conduct empirical research of offline RL and OPE. We take a position to argue that we should effectively use simulations in the empirical research of offline RL and OPE. To refute the counterclaim that experiments using only real-world data are preferable, we first point out the underlying risks and reproducibility issue in real-world experiments. Then, we describe how these issues can be addressed by using simulations. Moreover, we show how to incorporate the benefits of both real-world and simulation-based experiments to defend our position. Finally, we also present an open challenge to further facilitate practical research of offline RL and OPE in RecSys and RTB, with respect to public simulation platforms. As a possible solution for the issue, we show our ongoing open source project and its potential use case. We believe that building and utilizing simulation-based evaluation platforms for offline RL and OPE will be of great interest and relevance for the RecSys and RTB community.

cs.LG↗

arXiv2021

Data-Driven Off-Policy Estimator Selection: An Application in User Marketing on An Online Content Delivery Service

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 conducting costly online A/B tests. Accurate OPE is essential in domains such as healthcare, marketing or recommender systems to avoid deploying poor performing policies, as such policies may hart human lives or destroy the user experience. Thus, many OPE methods with theoretical backgrounds have been proposed. One emerging challenge with this trend is that a suitable estimator can be different for each application setting. It is often unknown for practitioners which estimator to use for their specific applications and purposes. To find out a suitable estimator among many candidates, we use a data-driven estimator selection procedure for off-policy policy performance estimators as a practical solution. As proof of concept, we use our procedure to select the best estimator to evaluate coupon treatment policies on a real-world online content delivery service. In the experiment, we first observe that a suitable estimator might change with different definitions of the outcome variable, and thus the accurate estimator selection is critical in real-world applications of OPE. Then, we demonstrate that, by utilizing the estimator selection procedure, we can easily find out suitable estimators for each purpose.

cs.AI↗

arXiv2021

Evaluating the Robustness of Off-Policy Evaluation

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 applications where the online interaction involves high stakes and expensive setting such as precision medicine and recommender systems. Since many OPE estimators have been proposed and some of them have hyperparameters to be tuned, there is an emerging challenge for practitioners to select and tune OPE estimators for their specific application. Unfortunately, identifying a reliable estimator from results reported in research papers is often difficult because the current experimental procedure evaluates and compares the estimators' performance on a narrow set of hyperparameters and evaluation policies. Therefore, it is difficult to know which estimator is safe and reliable to use. In this work, we develop Interpretable Evaluation for Offline Evaluation (IEOE), an experimental procedure to evaluate OPE estimators' robustness to changes in hyperparameters and/or evaluation policies in an interpretable manner. Then, using the IEOE procedure, we perform extensive evaluation of a wide variety of existing estimators on Open Bandit Dataset, a large-scale public real-world dataset for OPE. We demonstrate that our procedure can evaluate the estimators' robustness to the hyperparamter choice, helping us avoid using unsafe estimators. Finally, we apply IEOE to real-world e-commerce platform data and demonstrate how to use our protocol in practice.

stat.ML↗

arXiv2021

Efficient Hyperparameter Optimization under Multi-Source Covariate Shift

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, often violated in uncertain real-world applications, which motivates the study of learning under covariate shift. In this setting, the naive use of adaptive hyperparameter optimization methods such as Bayesian optimization does not work as desired since it does not address the distributional shift among different datasets. In this work, we consider a novel hyperparameter optimization problem under the multi-source covariate shift whose goal is to find the optimal hyperparameters for a target task of interest using only unlabeled data in a target task and labeled data in multiple source tasks. To conduct efficient hyperparameter optimization for the target task, it is essential to estimate the target objective using only the available information. To this end, we construct the variance reduced estimator that unbiasedly approximates the target objective with a desirable variance property. Building on the proposed estimator, we provide a general and tractable hyperparameter optimization procedure, which works preferably in our setting with a no-regret guarantee. The experiments demonstrate that the proposed framework broadens the applications of automated hyperparameter optimization.

cs.LG↗

arXiv2021

Overconvergent Lubin-Tate $(ϕ, Γ)$-modules for different uniformizers

Let F be a non-archimedean local field. The construction of Lubin-Tate $(ϕ_q, Γ)$-modules attached to p-adic representations of $G_F$ depends on the choice of a uniformizer of F. In this paper, we give a description of a functor which relates categories of overconvergent Lubin-Tate $(ϕ_q, Γ)$-modules for different uniformizers. Further, we study this functor more explicitly for 2-dimensional trianguline representations.

math.NT↗

arXiv2021

Ultrafast scattering dynamics of coherent phonons in Bi$_{1-x}$Sb$_{x}$ in the Weyl semimetal phase

We investigate ultrafast phonon dynamics in the Bi$_{1-x}$Sb$_{x}$ alloy system for various compositions $x$ using a reflective femtosecond pump-probe technique. The coherent optical phonons corresponding to the A$_{1g}$ local vibrational modes of Bi-Bi, Bi-Sb, and Sb-Sb are generated and observed in the time domain with a few picoseconds dephasing time. The frequencies of the coherent optical phonons were found to change as the Sb composition $x$ was varied, and more importantly, the relaxation time of those phonon modes was dramatically reduced for $x$ values in the range 0.5--0.8. We argue that the phonon relaxation dynamics are not simply governed by alloy scattering, but are significantly modified by anharmonic phonon-phonon scattering with implied minor contributions from electron-phonon scattering in a Weyl-semimetal phase.

cond-mat.mtrl-sci↗

arXiv2020

Optimal Off-Policy Evaluation from Multiple Logging Policies

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 the ordering of the variances of different importance sampling estimators is instance-dependent, which brings up a dilemma as to which importance sampling weights to use. In this paper, we resolve this dilemma by finding the OPE estimator for multiple loggers with minimum variance for any instance, i.e., the efficient one. In particular, we establish the efficiency bound under stratified sampling and propose an estimator achieving this bound when given consistent $q$-estimates. To guard against misspecification of $q$-functions, we also provide a way to choose the control variate in a hypothesis class to minimize variance. Extensive experiments demonstrate the benefits of our methods' efficiently leveraging of the stratified sampling of off-policy data from multiple loggers.

cs.LG↗

arXiv2020

The effect of ion irradiation on dephasing of coherent optical phonons in GaP

The dephasing of coherent longitudinal optical (LO) phonons in ion-irradiated GaP has been investigated with a femtosecond pump-probe technique based on electro-optic sampling. The dephasing time of the coherent LO phonon is found to be dramatically prolonged by the introduction of a small amount of defects by means of Ga-ion irradiation. The maximum dephasing time observed at room temperature is 9.1 ps at a Ga$^{+}$ ion dose of 10$^{13}$/cm$^{2}$, which is significantly longer than the value of 8.3 ps for GaP before ion irradiation. The longer dephasing time is explained in terms of the suppression of electron-LO-phonon scattering by the presence of defect-induced deep levels.

cond-mat.mtrl-sci↗

arXiv2020

Counterfactual Cross-Validation: Stable Model Selection Procedure for Causal Inference Models

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 performance of candidate CATE predictors to enable accurate and stable model selection. To this end, we analyze the model performance ranking problem and formulate guidelines to obtain a better evaluation metric. We then propose a novel metric that can identify the ranking of the performance of CATE predictors with high confidence. Empirical evaluations demonstrate that our metric outperforms existing metrics in both model selection and hyperparameter tuning tasks.

stat.ML↗

arXiv2020

Unbiased Lift-based Bidding System

Conventional bidding strategies for online display ad auction heavily relies on observed performance indicators such as clicks or conversions. A bidding strategy naively pursuing these easily observable metrics, however, fails to optimize the profitability of the advertisers. Rather, the bidding strategy that leads to the maximum revenue is a strategy pursuing the performance lift of showing ads to a specific user. Therefore, it is essential to predict the lift-effect of showing ads to each user on their target variables from observed log data. However, there is a difficulty in predicting the lift-effect, as the training data gathered by a past bidding strategy may have a strong bias towards the winning impressions. In this study, we develop Unbiased Lift-based Bidding System, which maximizes the advertisers' profit by accurately predicting the lift-effect from biased log data. Our system is the first to enable high-performing lift-based bidding strategy by theoretically alleviating the inherent bias in the log. Real-world, large-scale A/B testing successfully demonstrates the superiority and practicability of the proposed system.

cs.LG↗

arXiv2020

Asymmetric Tri-training for Debiasing Missing-Not-At-Random Explicit Feedback

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 learning of a recommender with biased feedback is one of the most challenging problems, as it is widely known that naive approaches under selection bias often lead to suboptimal results. A well-established solution for the problem is using propensity scoring techniques. The propensity score is the probability of each data being observed, and unbiased performance estimation is possible by weighting each data by the inverse of its propensity. However, the performance of the propensity-based unbiased estimation approach is often affected by choice of the propensity estimation model or the high variance problem. To overcome these limitations, we propose a model-agnostic meta-learning method inspired by the asymmetric tri-training framework for unsupervised domain adaptation. The proposed method utilizes two predictors to generate data with reliable pseudo-ratings and another predictor to make the final predictions. In a theoretical analysis, a propensity-independent upper bound of the true performance metric is derived, and it is demonstrated that the proposed method can minimize this bound. We conduct comprehensive experiments using public real-world datasets. The results suggest that the previous propensity-based methods are largely affected by the choice of propensity models and the variance problem caused by the inverse propensity weighting. Moreover, we show that the proposed meta-learning method is robust to these issues and can facilitate in developing effective recommendations from biased explicit feedback.

cs.SI↗

arXiv2020

Dual Learning Algorithm for Delayed Conversions

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 estimating the value of displaying an advertisement to a user. There are two main challenges in CVR prediction due to delayed feedback. First, some positive labels are not correctly observed in training data because some conversions do not occur immediately after a click. Second, delay mechanisms are not uniform among instances, meaning some positive feedback are much more frequently observed than others. It is widely acknowledged that these problems lead to severe bias in CVR prediction. To overcome these challenges, we propose two unbiased estimators: one for CVR prediction and the other for bias estimation. Subsequently, we propose a dual learning algorithm in which a CVR predictor and a bias estimator are trained in alternating fashion using only observable conversions. The proposed algorithm is the first of its kind to address the two major challenges in a theoretically sophisticated manner. Empirical evaluations using synthetic datasets demonstrate the practical value of the proposed approach.

stat.ML↗

arXiv2020

Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback

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 extent, the lack of such clicks does not necessarily indicate a negative response from the users, as it is possible that the users were not exposed to the items (positive-unlabeled problem). This leads to a difficulty in predicting the users' preferences from implicit feedback. Previous studies addressed the positive-unlabeled problem by uniformly upweighting the loss for the positive feedback data or estimating the confidence of each data having relevance information via the EM-algorithm. However, these methods failed to address the missing-not-at-random problem in which popular or frequently recommended items are more likely to be clicked than other items even if a user does not have a considerable interest in them. To overcome these limitations, we first define an ideal loss function to be optimized to realize recommendations that maximize the relevance and propose an unbiased estimator for the ideal loss. Subsequently, we analyze the variance of the proposed unbiased estimator and further propose a clipped estimator that includes the unbiased estimator as a special case. We demonstrate that the clipped estimator is expected to improve the performance of the recommender system, by considering the bias-variance trade-off. We conduct semi-synthetic and real-world experiments and demonstrate that the proposed method largely outperforms the baselines. In particular, the proposed method works better for rare items that are less frequently observed in the training data. The findings indicate that the proposed method can better achieve the objective of recommending items with the highest relevance.

stat.ML↗

arXiv2019

All-optical detection of periodic structure of chalcogenide superlattice using coherent folded acoustic phonons

Chalcogenide superlattices (SL) consist of alternate stacking of GeTe and Sb$_{2}$Te$_{3}$ layers. The structure can become a 3D topological insulator depending on the constituent layer thicknesses, making the design of the SL period a central issue for advancing chalcogenide SL as potential candidates for spin devices as well as for optimization of the current generation of phase-change memory devices. Here we explore the periodic structure of chalcogenide SL by observing coherent folded longitudinal acoustic (FLA) phonons excited by femtosecond laser pulse irradiation. The peak frequency of the FLA modes was observed to change upon variation of the thickness of the GeTe layer, which was well reproduced by means of an elastic continuum model. In addition, a new SL structure is unveiled for a sample consisting of thin GeTe and Sb$_{2}$Te$_{3}$ layers, which suggests intermixing of Ge atoms. This all-optical technique based on observation of coherent FLA modes offers a non-destructive characterization of superlattice structures at atomic resolution.

cond-mat.mtrl-sci↗

arXiv2019

A topological phase buried in a chalcogenide superlattice monitored by a helicity dependent Kerr measurement

Chalcogenide superlattices (SL), formed by the alternate stacking of GeTe and Sb$_{2}$Te$_{3}$ layers, also referred to as interfacial phase change memory (iPCM), are a leading candidate for spin based memory device applications. Theoretically, the iPCM structure it has been predicted to form a 3D topological insulator or Dirac semimetal depending on the constituent layer thicknesses. Here, we experimentally investigate the topological insulating nature of chalcogenide SLs using a helicity dependent time-resolved Kerr measurement. The helicity dependent Kerr signal is observed to exhibit a four cycle oscillation with $π$/2 periodicity suggesting the existence of a Dirac-like cone in some chalcogenide SLs. Furthermore, we found that increasing the thickness of the GeTe layer dramatically changes the periodicity, indicating a phase transition from a Dirac semimetal into a trivial insulator. Our results demonstrate that thickness-tuned chalcogenide SLs can play an important role in the manipulation of topological states, which may open up new possibilities for spintronic devices based on chalcogenide SLs.

cond-mat.mes-hall↗

arXiv2019

Photon Energy Dependence of Kerr Rotation in Chalcogenide Superlattices

We report on pump-probe based helicity dependent time-resolved Kerr measurements of chalcogenide superlattices, consisting of alternately stacked GeTe and Sb$_{2}$Te$_{3}$ layers under infrared excitation. The Kerr rotation signal consists of the specular Inverse Faraday effect (SIFE) and the specular optical Kerr effect (SOKE), both of which are found to monotonically increase with decreasing photon energy over a sub-eV energy range. Although the dependence of the SIFE can be attributed to a response function of direct third-order nonlinear susceptibility, the magnitude of the SOKE reflects cascading second-order nonlinear susceptibility resulting from electronic transitions from bulk valence band to interface-originating Dirac states of the superlattice.

cond-mat.mtrl-sci↗