Papers

Published Papers

A Decision-Theoretic View of Test-Time Training: When, How Far, and Which Directions to Adapt
Tomoya Wakayama
Proceedings of the 43rd International Conference on Machine Learning (ICML 2026), 2026
A decision-theoretic analysis of test-time training, including adaptation distance and direction selection.
In-Context Learning Is Provably Bayesian Inference: A Generalization Theory for Meta-Learning
Tomoya Wakayama, Taiji Suzuki
Proceedings of the 43rd International Conference on Machine Learning (ICML 2026), 2026
A generalization theory showing when in-context learning implements Bayesian inference in meta-learning.
arXiv
Ensemble Prediction via Covariate-dependent Stacking
Tomoya Wakayama, Shonosuke Sugasawa
Statistics and Computing, 2025
A covariate-dependent stacking framework for ensemble prediction with oracle-type theoretical guarantees.
Publication
Similarity-based Random Partition Distribution for Clustering Functional Data
Tomoya Wakayama, Shonosuke Sugasawa, Genya Kobayashi
Journal of the Royal Statistical Society, Series C, 2025
A nonparametric Bayesian clustering method for functional data that incorporates similarity information.
Publication
Reconciling Functional Data Regression with Excess Bases
Tomoya Wakayama, Hidetoshi Matsui
Behaviormetrika, 2025
A study of overparameterization and double-descent phenomena in functional data regression with basis expansions.
arXiv
Clustered Factor Analysis for Multivariate Spatial Data
Yanxiu Jin, Tomoya Wakayama, Renhe Jiang, Shonosuke Sugasawa
Spatial Statistics, 2025
A clustered factor analysis method for heterogeneous multivariate spatial data.
Publication Code
Spatiotemporal Factor Models for Functional Data with Application to Population Map Forecast
Tomoya Wakayama, Shonosuke Sugasawa
Spatial Statistics, 2024
A Bayesian spatio-temporal factor model for functional data with an application to population flow forecasting.
Publication Code
Functional Horseshoe Smoothing for Functional Trend Estimation
Tomoya Wakayama, Shonosuke Sugasawa
Statistica Sinica, 2024
A shrinkage-prior approach to locally adaptive smoothing and trend estimation for functional time series.
Publication
Fast Convergence on Perfect Classification for Functional Data
Tomoya Wakayama, Masaaki Imaizumi
Statistica Sinica, 2024
A theoretical analysis of perfect classification and exponential convergence for functional data classifiers.
Publication
Trend Filtering for Functional Data
Tomoya Wakayama, Shonosuke Sugasawa
Stat, 2023
A trend filtering method for functional data based on ADMM, fused lasso, grouped lasso, and basis expansion.
Publication Code

Preprints

The Geometry of Statistical Feature Learning in Mean-Field Langevin Dynamics
Zong Shang, Tomoya Wakayama, Guillaume Lecué, Taiji Suzuki
Preprint, 2026
A geometric formulation of statistical feature learning for supervised regression through mean-field Langevin dynamics.
arXiv
On Misspecified Error Distributions in Bayesian Functional Clustering: Consequences and Remedies
Fumiya Iwashige, Tomoya Wakayama, Shonosuke Sugasawa, Shintaro Hashimoto
Preprint, 2025
A study of error misspecification in Bayesian functional clustering and remedies based on Gaussian process error modeling.
arXiv
Process-based Inference for Spatial Energetics Using Bayesian Predictive Stacking
Tomoya Wakayama, Sudipto Banerjee
Preprint, 2024
Bayesian predictive stacking for process-based inference in spatial energetics and mobile health data.
arXiv Code
Bayesian Inference for Consistent Predictions in Overparameterized Nonlinear Regression
Tomoya Wakayama
Preprint, 2024
Bayesian adaptive priors for consistent prediction and uncertainty quantification in overparameterized nonlinear regression.
arXiv Code
Bayesian Analysis for Over-parameterized Linear Model without Sparsity
Tomoya Wakayama, Masaaki Imaizumi
Preprint, 2023
A Bayesian theory for overparameterized linear models without sparsity, using spectral priors and posterior contraction analysis.
arXiv

Papers

Published Papers

A Decision-Theoretic View of Test-Time Training: When, How Far, and Which Directions to Adapt

In-Context Learning Is Provably Bayesian Inference: A Generalization Theory for Meta-Learning

Ensemble Prediction via Covariate-dependent Stacking

Similarity-based Random Partition Distribution for Clustering Functional Data

Reconciling Functional Data Regression with Excess Bases

Clustered Factor Analysis for Multivariate Spatial Data

Spatiotemporal Factor Models for Functional Data with Application to Population Map Forecast

Functional Horseshoe Smoothing for Functional Trend Estimation

Fast Convergence on Perfect Classification for Functional Data

Trend Filtering for Functional Data

Preprints

The Geometry of Statistical Feature Learning in Mean-Field Langevin Dynamics

On Misspecified Error Distributions in Bayesian Functional Clustering: Consequences and Remedies

Process-based Inference for Spatial Energetics Using Bayesian Predictive Stacking

Bayesian Inference for Consistent Predictions in Overparameterized Nonlinear Regression

Bayesian Analysis for Over-parameterized Linear Model without Sparsity