activity
20222024
most citedAnomaly Attribution with Likelihood Compensation

6 citations · 20 across the 7 of their papers we have counts for

collaborators

7 papers

cs.LG2024

A resource-constrained stochastic scheduling algorithm for homeless street outreach and gleaning edible food

Conor M. Artman, Aditya Mate, Ezinne Nwankwo +10

We developed a common algorithmic solution addressing the problem of resource-constrained outreach encountered by social change organizations with different missions and operations…

cs.LG2023★ 5 cited

Generative Perturbation Analysis for Probabilistic Black-Box Anomaly Attribution

Tsuyoshi Idé, Naoki Abe

We address the task of probabilistic anomaly attribution in the black-box regression setting, where the goal is to compute the probability distribution of the attribution score of…

cs.LG2023★ 5 cited

Diagnostic Spatio-temporal Transformer with Faithful Encoding

Jokin Labaien, Tsuyoshi Idé, Pin-Yu Chen +2

This paper addresses the task of anomaly diagnosis when the underlying data generation process has a complex spatio-temporal (ST) dependency. The key technical challenge is to extr…

cs.LG2022

Decentralized Collaborative Learning with Probabilistic Data Protection

Tsuyoshi Idé, Rudy Raymond

We discuss future directions of Blockchain as a collaborative value co-creation platform, in which network participants can gain extra insights that cannot be accessed when disconn…

cs.LG2022★ 6 cited

Anomaly Attribution with Likelihood Compensation

Tsuyoshi Idé, Amit Dhurandhar, Jiří Navrátil +2

This paper addresses the task of explaining anomalous predictions of a black-box regression model. When using a black-box model, such as one to predict building energy consumption…

cs.LG2022★ 3 cited

Cardinality-Regularized Hawkes-Granger Model

Tsuyoshi Idé, Georgios Kollias, Dzung T. Phan +1

We propose a new sparse Granger-causal learning framework for temporal event data. We focus on a specific class of point processes called the Hawkes process. We begin by pointing o…

cs.SI2022★ 1 cited

Targeted Advertising on Social Networks Using Online Variational Tensor Regression

Tsuyoshi Idé, Keerthiram Murugesan, Djallel Bouneffouf +1

This paper is concerned with online targeted advertising on social networks. The main technical task we address is to estimate the activation probability for user pairs, which quan…