108 citations · 125 across the 34 of their papers we have counts for
14 papers · 1 filter
GRFBrain: Graph-Structured Rectified Flows for EEG Dynamic Modeling
Haohui Jia, Zheng Chen, Jathurshan Pradeepkumar +4
Forecasting time-varying functional connectivity from electroencephalography (EEG) requires modeling both history-dependent trends and structured variability across channels. Condi…
AdaKoop: Efficient Modeling of Nonlinear Dynamics from Nonstationary Data Streams with Koopman Operator Regression
Naoki Chihara, Ren Fujiwara, Yasuko Matsubara +1
Real-time data analysis requires the ability to accurately and adaptively address nonlinear dynamics in a nonstationary data stream while preserving computational efficiency. Howev…
Modeling Covariate Transition for Efficient Estimation of Longitudinal Treatment Effects in Randomized Experiments
Naoki Chihara, Tatsushi Oka, Yasuko Matsubara +2
We present a regression-adjustment framework designed for the estimation of longitudinal treatment effects in randomized experiments under static regimes. While regression-adjustme…
Modeling Dynamic Mixtures of Time-Delay Systems from Streaming Time Series
Ren Fujiwara, Yasuko Matsubara, Yasushi Sakurai
This research addresses the problem of adaptive modeling in time-series data streams with clear input-output relationships. This problem is challenging because rapid system changes…
EpiGraph: Building Generalists for Evidence-Intensive Epilepsy Reasoning in the Wild
Yuyang Dai, Zheng Chen, Jathurshan Pradeepkumar +4
Epilepsy diagnosis and treatment require evidence-intensive reasoning across heterogeneous clinical knowledge, including biosignal patterns, genetic mechanisms, pharmacogenomics, t…
When to Retrain after Drift: A Data-Only Test of Post-Drift Data Size Sufficiency
Ren Fujiwara, Yasuko Matsubara, Yasushi Sakurai
Sudden concept drift makes previously trained predictors unreliable, yet deciding when to retrain and what post-drift data size is sufficient is rarely addressed. We propose CALIPE…
Disentangled Mode-Specific Representations for Tensor Time Series via Contrastive Learning
Kohei Obata, Taichi Murayama, Zheng Chen +2
Multi-mode tensor time series (TTS) can be found in many domains, such as search engines and environmental monitoring systems. Learning representations of a TTS benefits various ap…
Selective Denoising Diffusion Model for Time Series Anomaly Detection
Kohei Obata, Zheng Chen, Yasuko Matsubara +2
Time series anomaly detection (TSAD) has been an important area of research for decades, with reconstruction-based methods, mostly based on generative models, gaining popularity an…
ODEBrain: Continuous-Time EEG Graph for Modeling Dynamic Brain Networks
Haohui Jia, Zheng Chen, Lingwei Zhu +6
Modeling neural population dynamics is crucial for foundational neuroscientific research and various clinical applications. Conventional latent variable methods typically model con…
RepSPD: Enhancing SPD Manifold Representation in EEGs via Dynamic Graphs
Haohui Jia, Zheng Chen, Lingwei Zhu +4
Decoding brain activity from electroencephalography (EEG) is crucial for neuroscience and clinical applications. Among recent advances in deep learning for EEG, geometric learning…
TIFO: Time-Invariant Frequency Operator for Stationarity-Aware Representation Learning in Time Series
Xihao Piao, Zheng Chen, Lingwei Zhu +3
Nonstationary time series forecasting suffers from the distribution shift issue due to the different distributions that produce the training and test data. Existing methods attempt…
Interpretable Dynamic Network Modeling of Tensor Time Series via Kronecker Time-Varying Graphical Lasso
Shingo Higashiguchi, Koki Kawabata, Yasuko Matsubara +1
With the rapid development of web services, large amounts of time series data are generated and accumulated across various domains such as finance, healthcare, and online platforms…
Multi-Aspect Mining and Anomaly Detection for Heterogeneous Tensor Streams
Soshi Kakio, Yasuko Matsubara, Ren Fujiwara +1
Analysis and anomaly detection in event tensor streams consisting of timestamps and multiple attributes - such as communication logs(time, IP address, packet length)- are essential…
Fast Mining and Dynamic Time-to-Event Prediction over Multi-sensor Data Streams
Kota Nakamura, Koki Kawabata, Yasuko Matsubara +1
Given real-time sensor data streams obtained from machines, how can we continuously predict when a machine failure will occur? This work aims to continuously forecast the timing of…