1 citations · 1 across the 14 of their papers we have counts for
17 papers
Open-ended Scientific Discovery with Possibilistic Reasoning
Anita Yang, Siu Lun Chau, Tomoya Wakayama +2
Autonomous scientific discovery with LLMs requires generating and testing hypotheses adaptively as evidence accumulates while maintaining statistical validity. Existing anytime-val…
Diffusion Transformers are Provably Optimal In-context Generators
Guoji Fu, Tomoya Wakayama, Ryotaro Kawata +3
Generative foundation models are attracting interest for their ability to produce desired outputs from demonstrations given at inference time, without updating parameters. However,…
The Geometry of Statistical Feature Learning in Mean-Field Langevin Dynamics
Zong Shang, Tomoya Wakayama, Guillaume Lecué +1
We introduce a geometric formulation of statistical feature learning for supervised regression. Feature learning is defined through a base--fiber decomposition: the base is the fea…
A Decision-Theoretic View of Test-Time Training: When, How Far, and Which Directions to Adapt
Tomoya Wakayama
Test-time training (TTT) adapts a pretrained model to each prompt via parameter updates, improving accuracy under pretraining-to-test distribution shifts. Yet, its performance ofte…
On Misspecified Error Distributions in Bayesian Functional Clustering: Consequences and Remedies
Fumiya Iwashige, Tomoya Wakayama, Shonosuke Sugasawa +1
Nonparametric Bayesian approaches provide a flexible framework for clustering without pre-specifying the number of groups, yet they are well known to overestimate the number of clu…
In-Context Learning Is Provably Bayesian Inference: A Generalization Theory for Meta-Learning
Tomoya Wakayama, Taiji Suzuki
This paper develops a finite-sample statistical theory for in-context learning (ICL), analyzed within a meta-learning framework that accommodates mixtures of diverse task types. We…
Clustered Factor Analysis for Multivariate Spatial Data
Yanxiu Jin, Tomoya Wakayama, Renhe Jiang +1
Factor analysis has been extensively used to reveal the dependence structures among multivariate variables, offering valuable insight in various fields. However, it cannot incorpor…
Ensemble Prediction via Covariate-dependent Stacking
Tomoya Wakayama, Shonosuke Sugasawa
This study proposes a novel approach to ensemble prediction, called "covariate-dependent stacking" (CDST). Unlike traditional stacking and model averaging methods, CDST allows mode…
Process-based Inference for Spatial Energetics Using Bayesian Predictive Stacking
Tomoya Wakayama, Sudipto Banerjee
Rapid developments in streaming data technologies have enabled real-time monitoring of human activity that can deliver high-resolution data on health variables over trajectories or…
Bayesian Inference for Consistent Predictions in Overparameterized Nonlinear Regression
Tomoya Wakayama
The remarkable generalization performance of large-scale models has been challenging the conventional wisdom of the statistical learning theory. Although recent theoretical studies…
Reconciling Functional Data Regression with Excess Bases
Tomoya Wakayama, Hidetoshi Matsui
As the development of measuring instruments and computers has accelerated the collection of massive amounts of data, functional data analysis (FDA) has experienced a surge of atten…
Similarity-based Random Partition Distribution for Clustering Functional Data
Tomoya Wakayama, Shonosuke Sugasawa, Genya Kobayashi
Random partition distribution is a crucial tool for model-based clustering. This study advances the field of random partition in the context of functional spatial data, focusing on…
Bayesian Analysis for Over-parameterized Linear Model via Effective Spectra
Tomoya Wakayama, Masaaki Imaizumi
In high-dimensional Bayesian statistics, various methods have been developed, including prior distributions that induce parameter sparsity to handle many parameters. Yet, these app…
Spatiotemporal factor models for functional data with application to population map forecast
Tomoya Wakayama, Shonosuke Sugasawa
The proliferation of mobile devices has led to the collection of large amounts of population data. This situation has prompted the need to utilize this rich, multidimensional data…
Functional Horseshoe Smoothing for Functional Trend Estimation
Tomoya Wakayama, Shonosuke Sugasawa
Due to developments in instruments and computers, functional observations are increasingly popular. However, effective methodologies for flexibly estimating the underlying trends w…
Fast Convergence on Perfect Classification for Functional Data
Tomoya Wakayama, Masaaki Imaizumi
We investigate the availability of approaching perfect classification on functional data with finite samples. The seminal work (Delaigle and Hall (2012)) showed that perfect classi…
Trend Filtering for Functional Data
Tomoya Wakayama, Shonosuke Sugasawa
Despite increasing accessibility to function data, effective methods for flexibly estimating underlying functional trend are still scarce. We thereby develop functional version of…