450 citations · 1.2k across the 59 of their papers we have counts for
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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…
Nystrom Method for Accurate and Scalable Implicit Differentiation
Ryuichiro Hataya, Makoto Yamada
The essential difficulty of gradient-based bilevel optimization using implicit differentiation is to estimate the inverse Hessian vector product with respect to neural network para…
Feature Selection for Discovering Distributional Treatment Effect Modifiers
Yoichi Chikahara, Makoto Yamada, Hisashi Kashima
Finding the features relevant to the difference in treatment effects is essential to unveil the underlying causal mechanisms. Existing methods seek such features by measuring how g…
Communication Compression for Decentralized Learning with Operator Splitting Methods
Yuki Takezawa, Kenta Niwa, Makoto Yamada
In decentralized learning, operator splitting methods using a primal-dual formulation (e.g., the Edge-Consensus Learning (ECL)) has been shown to be robust to heterogeneous data an…
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…
Computationally Efficient Wasserstein Loss for Structured Labels
Ayato Toyokuni, Sho Yokoi, Hisashi Kashima +1
The problem of estimating the probability distribution of labels has been widely studied as a label distribution learning (LDL) problem, whose applications include age estimation,…