Anomaly Attribution with Likelihood Compensation
- 2021
- AAAI 2021
Tsuyoshi "Ide-san" Ide
Title
Head of Data Science, IBM Semiconductors
Bio
I am passionate about modeling real-world business problems using advanced machine learning methods. I have a strong track record in successfully leading numerous customer engagements to deliver first-of-a-kind AI solutions across many different industries. Most papers detailing these solutions have been published in top AI/data mining conferences. In particular, I am recognized as an expert in industrial anomaly detection and diagnosis, as evidenced by two textbooks I authored on that topic. Here is one recent lecture note on anomaly detection.
- Publication list (most of the papers are available in pdf)
- Projects
- DBLP
Publications
Efficient protocol for collaborative dictionary learning in decentralized networks
- Tsuyoshi Idé
- Rudy Raymond
- et al.
- 2019
- IJCAI 2019
ℓ0-regularized sparsity for probabilistic mixture models
- Dzung T. Phan
- Tsuyoshi Idé
- 2019
- SDM 2019
Collaborative anomaly detection on blockchain from noisy sensor data
- 2018
- ICDM 2018
Tensorial change analysis using probabilistic tensor regression
- 2019
- AAAI 2019
Predicting Nocturnal Hypoglycemia from Continuous Glucose Monitoring Data with Extended Prediction Horizon
- Long Vu
- Sarah Kefayati
- et al.
- 2019
- AMIA Annual Symposium
Multi-task multi-modal models for collective anomaly detection
- Tsuyoshi Idé
- Dzung T. Phan
- et al.
- 2017
- ICDM 2017
A novel l0-constrained gaussian graphical model for anomaly localization
- Dzung T. Phan
- Tsuyoshi Idé
- et al.
- 2017
- ICDMW 2017
City-Wide Traffic Flow Estimation from a Limited Number of Low-Quality Cameras
- 2017
- IEEE T-ITS
Sparse Gaussian markov random field mixtures for anomaly detection
- Tsuyoshi Idé
- Ankush Khandelwal
- et al.
- 2016
- ICDM 2016
Patents
- CN
- ZL202080066780.3
- JP
- 7560222
- US
- 11487650
- US
- 11216743
- US
- 10754310
- US
- 10733813
- US
- 10612999
- US
- 10378997
- US
- 10133703
- DE
- 112010004005
