Search Results for author: Yuya Sasaki

Found 25 papers, 4 papers with code

Extreme Changes in Changes

no code implementations27 Nov 2022 Yuya Sasaki, Yulong Wang

Policy analysts are often interested in treating subpopulations in the limit, such as infants with extremely lowest birth weights.

Estimation of Average Derivatives of Latent Regressors: With an Application to Inference on Buffer-Stock Saving

no code implementations13 Sep 2022 Hao Dong, Yuya Sasaki

Based on the proposed estimator, we construct a formal test on the sub-unity of the marginal propensity to consume out of permanent income (MPCP) under a nonparametric consumption model and a permanent-transitory model of income dynamics with nonparametric distribution.

Unity

GNN Transformation Framework for Improving Efficiency and Scalability

1 code implementation25 Jul 2022 Seiji Maekawa, Yuya Sasaki, George Fletcher, Makoto Onizuka

We propose a framework that automatically transforms non-scalable GNNs into precomputation-based GNNs which are efficient and scalable for large-scale graphs.

Scaling Private Deep Learning with Low-Rank and Sparse Gradients

no code implementations6 Jul 2022 Ryuichi Ito, Seng Pei Liew, Tsubasa Takahashi, Yuya Sasaki, Makoto Onizuka

Applying Differentially Private Stochastic Gradient Descent (DPSGD) to training modern, large-scale neural networks such as transformer-based models is a challenging task, as the magnitude of noise added to the gradients at each iteration scales with model dimension, hindering the learning capability significantly.

An Empirical Study of Personalized Federated Learning

1 code implementation27 Jun 2022 Koji Matsuda, Yuya Sasaki, Chuan Xiao, Makoto Onizuka

Federated learning is a distributed machine learning approach in which a single server and multiple clients collaboratively build machine learning models without sharing datasets on clients.

BIG-bench Machine Learning Personalized Federated Learning

Predicting Parking Lot Availability by Graph-to-Sequence Model: A Case Study with SmartSantander

1 code implementation21 Jun 2022 Yuya Sasaki, Junya Takayama, Juan Ramón Santana, Shohei Yamasaki, Tomoya Okuno, Makoto Onizuka

Nowadays, so as to improve services and urban areas livability, multiple smart city initiatives are being carried out throughout the world.

Graph-to-Sequence

Capital and Labor Income Pareto Exponents in the United States, 1916-2019

no code implementations9 Jun 2022 Ji Hyung Lee, Yuya Sasaki, Alexis Akira Toda, Yulong Wang

Accurately estimating income Pareto exponents is challenging due to limitations in data availability and the applicability of statistical methods.

Tuning Parameter-Free Nonparametric Density Estimation from Tabulated Summary Data

no code implementations12 Apr 2022 Ji Hyung Lee, Yuya Sasaki, Alexis Akira Toda, Yulong Wang

Administrative data are often easier to access as tabulated summaries than in the original format due to confidentiality concerns.

Density Estimation

Non-Existent Moments of Earnings Growth

no code implementations15 Mar 2022 Silvia Sarpietro, Yuya Sasaki, Yulong Wang

We empirically show that the population kurtosis, skewness, and even variance often fail to exist for the conditional distribution of earnings growths.

Similarity Search on Computational Notebooks

no code implementations30 Jan 2022 Misato Horiuchi, Yuya Sasaki, Chuan Xiao, Makoto Onizuka

In this paper, we propose a similarity search on computational notebooks and develop a new framework for the similarity search.

Standard errors for two-way clustering with serially correlated time effects

no code implementations27 Jan 2022 Harold D Chiang, Bruce E Hansen, Yuya Sasaki

We propose improved standard errors and an asymptotic distribution theory for two-way clustered panels.

Slow Movers in Panel Data

no code implementations22 Oct 2021 Yuya Sasaki, Takuya Ura

Panel data often contain stayers (units with no within-variations) and slow movers (units with little within-variations).

FedMe: Federated Learning via Model Exchange

no code implementations15 Oct 2021 Koji Matsuda, Yuya Sasaki, Chuan Xiao, Makoto Onizuka

First, to optimize the model architectures for local data, clients tune their own personalized models by comparing to exchanged models and picking the one that yields the best performance.

BIG-bench Machine Learning Federated Learning

Dyadic double/debiased machine learning for analyzing determinants of free trade agreements

no code implementations8 Oct 2021 Harold D Chiang, Yukun Ma, Joel Rodrigue, Yuya Sasaki

Together with the use of Neyman orthogonal scores, this novel cross fitting method enables root-$n$ consistent estimation and inference robustly against dyadic dependence.

BIG-bench Machine Learning

Inference in high-dimensional regression models without the exact or $L^p$ sparsity

no code implementations21 Aug 2021 Jooyoung Cha, Harold D. Chiang, Yuya Sasaki

This paper proposes a new method of inference in high-dimensional regression models and high-dimensional IV regression models.

regression

AIREX: Neural Network-based Approach for Air Quality Inference in Unmonitored Cities

no code implementations16 Aug 2021 Yuya Sasaki, Kei Harada, Shohei Yamasaki, Makoto Onizuka

Since existing methods aim to infer air quality of locations only in monitored cities, they do not assume inferring air quality in unmonitored cities.

Air Quality Inference

Fixed-k Tail Regression: New Evidence on Tax and Wealth Inequality from Forbes 400

no code implementations20 May 2021 Ji Hyung Lee, Yuya Sasaki, Alexis Akira Toda, Yulong Wang

We develop a novel fixed-k tail regression method that accommodates the unique feature in the Forbes 400 data that observations are truncated from below at the 400th largest order statistic.

regression Time Series

Nonparametric Difference-in-Differences in Repeated Cross-Sections with Continuous Treatments

no code implementations29 Apr 2021 Xavier D'Haultfoeuille, Stefan Hoderlein, Yuya Sasaki

Under our conditions, the time trend can be identified using a control group, as in the binary difference-in-differences literature.

Algorithmic subsampling under multiway clustering

no code implementations28 Feb 2021 Harold D. Chiang, Jiatong Li, Yuya Sasaki

This paper proposes a novel method of algorithmic subsampling (data sketching) for multiway cluster dependent data.

Linear programming approach to nonparametric inference under shape restrictions: with an application to regression kink designs

no code implementations12 Feb 2021 Harold D. Chiang, Kengo Kato, Yuya Sasaki, Takuya Ura

We develop a novel method of constructing confidence bands for nonparametric regression functions under shape constraints.

regression

Welfare Analysis via Marginal Treatment Effects

no code implementations14 Dec 2020 Yuya Sasaki, Takuya Ura

Consider a causal structure with endogeneity (i. e., unobserved confoundedness) in empirical data, where an instrumental variable is available.

Inference for high-dimensional exchangeable arrays

no code implementations10 Sep 2020 Harold D. Chiang, Kengo Kato, Yuya Sasaki

We consider inference for high-dimensional separately and jointly exchangeable arrays where the dimensions may be much larger than the sample sizes.

Density Estimation regression

Unconditional Quantile Regression with High Dimensional Data

no code implementations27 Jul 2020 Yuya Sasaki, Takuya Ura, Yichong Zhang

This paper considers estimation and inference for heterogeneous counterfactual effects with high-dimensional data.

regression

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