activity
20082023
most citedRepresentation Learning on Graphs with Jumping Knowledge Networks

970 citations · 1.7k across the 49 of their papers we have counts for

collaborators

66 papers

cs.LG2023

A Neighbourhood-Aware Differential Privacy Mechanism for Static Word Embeddings

Danushka Bollegala, Shuichi Otake, Tomoya Machide +1

We propose a Neighbourhood-Aware Differential Privacy (NADP) mechanism considering the neighbourhood of a word in a pretrained static word embedding space to determine the minimal…

math.CO2023★ 1 cited

Decomposition of (infinite) digraphs along directed 1-separations

Nathan Bowler, Florian Gut, Meike Hatzel +3

We introduce torsoids, a canonical structure in matching covered graphs, corresponding to the bricks and braces of the graph. This allows a more fine-grained understanding of the s…

cs.LG2022★ 1 cited

Intrinsic Dimensionality Estimation within Tight Localities: A Theoretical and Experimental Analysis

Laurent Amsaleg, Oussama Chelly, Michael E. Houle +3

Accurate estimation of Intrinsic Dimensionality (ID) is of crucial importance in many data mining and machine learning tasks, including dimensionality reduction, outlier detection,…

cs.DS2022★ 1 cited

Online Task Assignment Problems with Reusable Resources

Hanna Sumita, Shinji Ito, Kei Takemura +4

We study online task assignment problem with reusable resources, motivated by practical applications such as ridesharing, crowdsourcing and job hiring. In the problem, we are given…

cs.DS2021

Embeddings of Planar Quasimetrics into Directed \ell_1$ and Polylogarithmic Approximation for Directed Sparsest-Cut

Ken-ichi Kawarabayashi, Anastasios Sidiropoulos

The multi-commodity flow-cut gap is a fundamental parameter that affects the performance of several divide \& conquer algorithms, and has been extensively studied for various class…

stat.ML2021

Near-Optimal Regret Bounds for Contextual Combinatorial Semi-Bandits with Linear Payoff Functions

Kei Takemura, Shinji Ito, Daisuke Hatano +4

The contextual combinatorial semi-bandit problem with linear payoff functions is a decision-making problem in which a learner chooses a set of arms with the feature vectors in each…