178 citations · 241 across the 10 of their papers we have counts for
10 papers
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…
Black-Box Anomaly Attribution
Tsuyoshi Idé, Naoki Abe
When the prediction of a black-box machine learning model deviates from the true observation, what can be said about the reason behind that deviation? This is a fundamental and ubi…
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…
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…
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…
Learning Word Association Norms Using Tree Cut Pair Models
Naoki Abe, Hang Li
We consider the problem of learning co-occurrence information between two word categories, or more in general between two discrete random variables taking values in a hierarchicall…
Clustering Words with the MDL Principle
Hang Li, Naoki Abe
We address the problem of automatically constructing a thesaurus (hierarchically clustering words) based on corpus data. We view the problem of clustering words as that of estimati…
Learning Dependencies between Case Frame Slots
Hang Li, Naoki Abe
We address the problem of automatically acquiring case frame patterns (selectional patterns) from large corpus data. In particular, we propose a method of learning dependencies bet…
On-line Learning of Binary Lexical Relations Using Two-dimensional Weighted Majority Algorithms
Naoki Abe, Hang Li, Atsuyoshi Nakamura
We consider the problem of learning a certain type of lexical semantic knowledge that can be expressed as a binary relation between words, such as the so-called sub-categorization…
Generalizing Case Frames Using a Thesaurus and the MDL Principle
Hang Li, Naoki Abe
We address the problem of automatically acquiring case-frame patterns from large corpus data. In particular, we view this problem as the problem of estimating a (conditional) distr…