83 citations · 160 across the 26 of their papers we have counts for
27 papers
Training Graph Foundation Models on The Web Graph
Ryoma Sato
We introduce Acacia, a graph foundation model, trained on the web graph. Acacia (i) supports arbitrary feature dimensionalities and semantics without additional training, (ii) supp…
Parameter-free Clipped Gradient Descent Meets Polyak
Yuki Takezawa, Han Bao, Ryoma Sato +2
Gradient descent and its variants are de facto standard algorithms for training machine learning models. As gradient descent is sensitive to its hyperparameters, we need to tune th…
Embarrassingly Simple Text Watermarks
Ryoma Sato, Yuki Takezawa, Han Bao +2
We propose Easymark, a family of embarrassingly simple yet effective watermarks. Text watermarking is becoming increasingly important with the advent of Large Language Models (LLM)…
Necessary and Sufficient Watermark for Large Language Models
Yuki Takezawa, Ryoma Sato, Han Bao +2
In recent years, large language models (LLMs) have achieved remarkable performances in various NLP tasks. They can generate texts that are indistinguishable from those written by h…
Beyond Exponential Graph: Communication-Efficient Topologies for Decentralized Learning via Finite-time Convergence
Yuki Takezawa, Ryoma Sato, Han Bao +2
Decentralized learning has recently been attracting increasing attention for its applications in parallel computation and privacy preservation. Many recent studies stated that the…
Graph Neural Networks can Recover the Hidden Features Solely from the Graph Structure
Ryoma Sato
Graph Neural Networks (GNNs) are popular models for graph learning problems. GNNs show strong empirical performance in many practical tasks. However, the theoretical properties hav…
Active Learning from the Web
Ryoma Sato
Labeling data is one of the most costly processes in machine learning pipelines. Active learning is a standard approach to alleviating this problem. Pool-based active learning firs…
Momentum Tracking: Momentum Acceleration for Decentralized Deep Learning on Heterogeneous Data
Yuki Takezawa, Han Bao, Kenta Niwa +2
SGD with momentum is one of the key components for improving the performance of neural networks. For decentralized learning, a straightforward approach using momentum is Distribute…
Towards Principled User-side Recommender Systems
Ryoma Sato
Traditionally, recommendation algorithms have been designed for service developers. However, recently, a new paradigm called user-side recommender systems has been proposed and the…
Twin Papers: A Simple Framework of Causal Inference for Citations via Coupling
Ryoma Sato, Makoto Yamada, Hisashi Kashima
The research process includes many decisions, e.g., how to entitle and where to publish the paper. In this paper, we introduce a general framework for investigating the effects of…
Approximating 1-Wasserstein Distance with Trees
Makoto Yamada, Yuki Takezawa, Ryoma Sato +3
Wasserstein distance, which measures the discrepancy between distributions, shows efficacy in various types of natural language processing (NLP) and computer vision (CV) applicatio…
CLEAR: A Fully User-side Image Search System
Ryoma Sato
We use many search engines on the Internet in our daily lives. However, they are not perfect. Their scoring function may not model our intent or they may accept only text queries e…
Word Tour: One-dimensional Word Embeddings via the Traveling Salesman Problem
Ryoma Sato
Word embeddings are one of the most fundamental technologies used in natural language processing. Existing word embeddings are high-dimensional and consume considerable computation…
Retrieving Black-box Optimal Images from External Databases
Ryoma Sato
Suppose we have a black-box function (e.g., deep neural network) that takes an image as input and outputs a value that indicates preference. How can we retrieve optimal images with…
Fixed Support Tree-Sliced Wasserstein Barycenter
Yuki Takezawa, Ryoma Sato, Zornitsa Kozareva +2
The Wasserstein barycenter has been widely studied in various fields, including natural language processing, and computer vision. However, it requires a high computational cost to…
Enumerating Fair Packages for Group Recommendations
Ryoma Sato
Package-to-group recommender systems recommend a set of unified items to a group of people. Different from conventional settings, it is not easy to measure the utility of group rec…
Re-evaluating Word Mover's Distance
Ryoma Sato, Makoto Yamada, Hisashi Kashima
The word mover's distance (WMD) is a fundamental technique for measuring the similarity of two documents. As the crux of WMD, it can take advantage of the underlying geometry of th…
Private Recommender Systems: How Can Users Build Their Own Fair Recommender Systems without Log Data?
Ryoma Sato
Fairness is a crucial property in recommender systems. Although some online services have adopted fairness aware systems recently, many other services have not adopted them yet. In…
Supervised Tree-Wasserstein Distance
Yuki Takezawa, Ryoma Sato, Makoto Yamada
To measure the similarity of documents, the Wasserstein distance is a powerful tool, but it requires a high computational cost. Recently, for fast computation of the Wasserstein di…
Poincare: Recommending Publication Venues via Treatment Effect Estimation
Ryoma Sato, Makoto Yamada, Hisashi Kashima
Choosing a publication venue for an academic paper is a crucial step in the research process. However, in many cases, decisions are based solely on the experience of researchers, w…
Fast Unbalanced Optimal Transport on a Tree
Ryoma Sato, Makoto Yamada, Hisashi Kashima
This study examines the time complexities of the unbalanced optimal transport problems from an algorithmic perspective for the first time. We reveal which problems in unbalanced op…
A Survey on The Expressive Power of Graph Neural Networks
Ryoma Sato
Graph neural networks (GNNs) are effective machine learning models for various graph learning problems. Despite their empirical successes, the theoretical limitations of GNNs have…
Random Features Strengthen Graph Neural Networks
Ryoma Sato, Makoto Yamada, Hisashi Kashima
Graph neural networks (GNNs) are powerful machine learning models for various graph learning tasks. Recently, the limitations of the expressive power of various GNN models have bee…
Fast and Robust Comparison of Probability Measures in Heterogeneous Spaces
Ryoma Sato, Marco Cuturi, Makoto Yamada +1
Comparing two probability measures supported on heterogeneous spaces is an increasingly important problem in machine learning. Such problems arise when comparing for instance two p…