Slides
Slides in English
- 2016/9/20: Quantized Stochastic Gradient Descent: Communication versus Convergence (@WITMSE 2016)
- 2016/3/18: Understanding the role of invariances in training neural networks (@AI & ML in Cambridge 2016)
- 2016/9/1: Tensor decompositions: old, new, and beyond (@Machine Learning Summer School '15, Kyoto, Japan)
- 2014/3/18: Towards better computation-statistics trade-off in tensor decomposition (@Trends in Machine Learning at Kyoto University)
- 2012/8/15: Convex Optimization: Old Tricks for New Problems (@PhD Summer Course at Technical University of Denmark)
- 2012/5/7: Introduction to Tensor Decomposition Methods (@ Miyano Lab., Human Genome Center, Institute of Medical Science)
- 2012/1/26: Statistical Performance of Convex Tensor Decomposition (@Perspectives in Informatics 4B, Kyoto University)
- 2011/8/26: Convex Optimization: Old Tricks for New Problems (@PhD Summer Course at Technical University of Denmark)
- 2011/3/23: Estimation of low-rank tensors via convex optimization (@TU Berlin)
- 2010/12/11: Regularization Strategies and Empirical Bayesian Learning for MKL [Video] (@NIPS2010 Workshop: New Directions in Multiple Kernel Learning
- 2010/12/10: On the Extension of Trace Norm to Tensors (@NIPS2010 Workshop: Tensors, Kernels, and Machine Learning)
- 2010/6/22: A Fast Augmented Lagrangian Algorithm for Learning Low-Rank Matrices (@ICML2010)
- 2009/12/12: Super-Linear Convergence of Dual Augmented Lagrangian Algorithm for Sparse Learning [Video] (@ NIPS Optimization Workshop 2009)
- 2009/9/15: Dual Augmented Lagrangian, Proximal Minimization, and MKL (@TU Berlin)
Slides in Japanese