RIKEN Center for Advanced Intelligence Project Nonconvex Learning Theory Team
Team Leader: Takafumi Kanamori (Ph.D.)
The research team aims to develop machine learning algorithms using non-convex optimization problems and its theoretical foundations. Most of current learning algorithms are formalized as convex optimization problems. Though the convexity is advantageous for optimization, it is not necessarily preferable from the standpoint of statistical properties such as robustness and bias-reduction of estimators. The optimization of non-convex functions, however, encounters computational difficulty. We challenge to develop learning algorithm using non-convex optimization beyond the scope of convexity and to establish a theoretical foundation to analyze learning methods with non-convexity.
Main Research Fields
- Computer Science
Related Research Fields
- Theoretical analysis of learning algorithms using non-convex optimization
- Statistical inference for large-scale models using divergence measures
- Extension of multimodal information integration and information-transfer learning
Papers with an asterisk(*) are based on research conducted outside of RIKEN.
- 1.*T. Kanamori:
“Efficiency Bound of Local Z-Estimators on Discrete Sample Spaces”
Entropy, vol. 18, no. 7, pp. 273-287 (2016).
- 2.*T. Kanamori, and H. Fujisawa:
“Robust Estimation under Heavy Contamination using Unnormalized Models”
Biometrika, vol. 102, no. 3, pp. 559-572 (2015).
- 3.*A. Takeda, S. Fujiwara, and T. Kanamori:
“Extended Robust Support Vector Machine Based on Financial Risk Minimization”
Neural Computation, vol. 26, num. 11, pp. 2541-2569 (2014).
- 4.*T. Kanamori , and H. Fujisawa:
“Affine Invariant Divergences associated with Proper Composite Scoring Rules and their Applications”
Bernoulli, vol. 20, No. 4, pp. 2278-2304 (2014).
- 5.*T. Kanamori, and A. Takeda:
“A Numerical Study of Learning Algorithms on Stiefel Manifold”
Computational Management Science, vol. 11, Issue 4, pp 319-340 (2014).
- 6.*A. Takeda, and T. Kanamori:
“Using Financial Risk for Analyzing Generalization Performance of Machine Learning Models”
Neural Networks, vol. 57, pp. 29-38 (2014).
- 7.*T. D. Nguyen, M. C. du Plessis, T. Kanamori, and M. Sugiyama:
“Constrained Least-Squares Density-Difference Estimation”
IEICE Transactions on Information and Systems, vol. E97-D, no. 7, pp. 1822-1829 (2014).
- 8.*T. Kanamori:
“Scale-Invariant Divergences for Density Functions”
Entropy, vol 16(5), pp. 2611-2628 (2014).
- 9.*T. Kanamori, and M. Sugiyama:
“Statistical Analysis of Distance Estimators with Density Differences and Density Ratios”
Entropy, vol. 16 (2), pp. 921-942 (2014).
- 10.*T. Kanamori, and A. Ohara:
“A Bregman extension of quasi-Newton updates II: analysis of robustness properties”
Journal of Computational and Applied Mathematics, vol. 253, pp. 104-122 (2013).
- Takafumi Kanamori
- Team Leader
- Wataru Kumagai
- Research Scientist
- Kosaku Takanashi
- Postdoctoral Researcher
Department of Mathematical and Computing Science, Tokyo Institute of Technology,
Ookayama, Meguro-ku, Tokyo 152-8552 Japan
Email: kanamori [at] c.titech.ac.jp