450 citations · 1.2k across the 59 of their papers we have counts for
7 papers · 2 filters
Kernel Stein Tests for Multiple Model Comparison
Jen Ning Lim, Makoto Yamada, Bernhard Schölkopf +1
We address the problem of non-parametric multiple model comparison: given candidate models, decide whether each candidate is as good as the best one(s) or worse than it. We pro…
More Powerful Selective Kernel Tests for Feature Selection
Jen Ning Lim, Makoto Yamada, Wittawat Jitkrittum +3
Refining one's hypotheses in the light of data is a common scientific practice; however, the dependency on the data introduces selection bias and can lead to specious statistical a…
Transformer Dissection: A Unified Understanding of Transformer's Attention via the Lens of Kernel
Yao-Hung Hubert Tsai, Shaojie Bai, Makoto Yamada +2
Transformer is a powerful architecture that achieves superior performance on various sequence learning tasks, including neural machine translation, language understanding, and sequ…
Approximation Ratios of Graph Neural Networks for Combinatorial Problems
Ryoma Sato, Makoto Yamada, Hisashi Kashima
In this paper, from a theoretical perspective, we study how powerful graph neural networks (GNNs) can be for learning approximation algorithms for combinatorial problems. To this e…
Topological Bayesian Optimization with Persistence Diagrams
Tatsuya Shiraishi, Tam Le, Hisashi Kashima +1
Finding an optimal parameter of a black-box function is important for searching stable material structures and finding optimal neural network structures, and Bayesian optimization…
Learning to Sample Hard Instances for Graph Algorithms
Ryoma Sato, Makoto Yamada, Hisashi Kashima
Hard instances, which require a long time for a specific algorithm to solve, help (1) analyze the algorithm for accelerating it and (2) build a good benchmark for evaluating the pe…