Yoshinobu Kawahara is an associate professor at The Institute of Scientific and Industrial Research (ISIR), Osaka Univeristy (Osaka, Japan).
He received his Ph.D. degree from The University of Tokyo in March, 2008.
His primary research interests lie in combinatorial methods for machine learning and statistical modeling for time-series data.
I am particularly interested in submodularity in machine learning, learning dynamical systems, statistical causal analysis, change-point detection in time-series data, bioinformatics and data-mining for engineering systems.
- My paper titled ""Dynamic Mode Decomposition with Reproducing Kernels for Koopman Spectral Analysis" has been accepted at NIPS'16 (2016/8/13).
- Our paper titled "Efficient Generalized Fused Lasso and its Applications" has been accepted to ACM Transactions on Intelligent Systems and Technology (2015/11/17).
- Our paper titled "Toxicogenomic Prediction with Group Sparse Regularization Based on Transcription Factor Network Information" has been accepted to Fundamental Toxicological Sciences (2015/9/7).
- Our paper titled "Higher Order Fused Regularization for Supervised Learning with Grouped Parameters" has been accepted to ECML-PKDD'2015 (2015/6/2).
- I won the Osaka University Presidential Award for Encouragement (2015/6/1).
- Our paper titled "A fault detection technique for the steel manufacturing process based on a normal pattern library" has been accepted to The 9th IFAC Symp. on Fault Detection, Supervision and Safety of Technical Processes (SafeProcess'15) (2015/3/15).
- Our paper titled "On approximate non-submodular minimization via tree-structured supermodularity" has been accepted to the 18th Int'l Conf. on Artificial Intelligence and Statistics (AISTATS'15) (2015/1/11).
My past news is also found [here].