Kenji Kawaguchi

PhD student
Massachusetts Institute of Technology (MIT)
Computer Science and Artificial Intelligence Laboratory (CSAIL)
Machine Learning Group & Learning and Intelligent Systems Group


Recent Publications

Conference Papers

Kenji Kawaguchi. Deep Learning without Poor Local Minima. In Advances in Neural Information Processing (NIPS), 2016.
[pdf] [BibTex] [Spotlight Video] [Talk] Selected for NIPS oral presentation (top 2% submissions)

Kenji Kawaguchi*, Bo Xie*, and Le Song. Deep Semi-Random Features for Nonlinear Function Approximation. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI), 2018.
[pdf] [BibTex]

Kenji Kawaguchi. Bounded Optimal Exploration in MDP. In Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI), 2016.
[pdf] [BibTex]

Kenji Kawaguchi, Leslie Pack Kaelbling and Tomás Lozano-Pérez. Bayesian Optimization with Exponential Convergence. In Advances in Neural Information Processing (NIPS), 2015.
[pdf] [BibTex] [Code]

Book Chapter

Kenji Kawaguchi, Leslie Pack Kaelbling and Yoshua Bengio. Generalization in Deep Learning. In Mathematics of Deep Learning, Cambridge University Press, to appear. Prepint avaliable at: arXiv preprint arXiv:1710.05468, 2017.
[pdf] [BibTex] [Code]

Journal Articles

Kenji Kawaguchi, Yu Maruyama and Xiaoyu Zheng. Global Continuous Optimization with Error Bound and Fast Convergence. Journal of Artificial Intelligence Research (JAIR), 56: 153-195, 2016.
[pdf] [BibTex]

Xiaoyu Zheng, Hiroto Itoh, Kenji Kawaguchi, Hitoshi Tamaki and Yu Maruyama. Application of Bayesian nonparametric models to the uncertainty and sensitivity analysis of source term in a BWR severe accident. Reliability Engineering & System Safety, 138: 253-262, 2015.
[pdf] [BibTeX]

Jun Ishikawa, Kenji Kawaguchi and Yu Maruyama. Analysis for iodine release from unit 3 of Fukushima Dai-ichi nuclear power plant with consideration of water phase iodine chemistry. Journal of Nuclear Science and Technology, 52(3):308-315, 2015.
[pdf] [BibTeX]

Preprints

Kenji Kawaguchi, Jiaoyang Huang and Leslie Pack Kaelbling. Effect of Depth and Width on Local Minima in Deep Learning. arXiv preprint arXiv:1811.08150, 2018.
[pdf] [BibTex]

Kenji Kawaguchi and Yoshua Bengio. Depth with Nonlinearity Creates No Bad Local Minima in ResNets. arXiv preprint arXiv:1810.09038, 2018.
[pdf] [BibTex]

Kenji Kawaguchi, Yoshua Bengio, Vikas Verma and Leslie Pack Kaelbling. Towards Understanding Generalization via Analytical Learning Theory. arXiv preprint arXiv:1802.07426, 2018.
[pdf] [BibTex] [Code]

Technical Reports

Tomaso Poggio, Kenji Kawaguchi, Qianli Liao, Brando Miranda, Lorenzo Rosasco, Xavier Boix, Jack Hidary and Hrushikesh Mhaskar. Theory of Deep Learning III: explaining the non-overfitting puzzle. Massachusetts Institute of Technology CBMM Memo No. 73, 2018.
[pdf] [BibTex]

Qianli Liao, Kenji Kawaguchi and Tomaso Poggio. Streaming Normalization: Towards Simpler and More Biologically-plausible Normalizations for Online and Recurrent Learning. Massachusetts Institute of Technology CBMM Memo No. 57, 2016.
[pdf] [BibTeX]


Code

Bayesian optimization with exponential convergence

Generalization in Deep Learning

Towards Understanding Generalization via Analytical Learning Theory


Education

Massachusetts Institute of Technology             2014/09 - Present
Ph.D. student, Electrical Engineering and Computer Science
Advisors: Leslie Pack Kaelbling and Tomás Lozano-Pérez.
GPA 5.0/5.0 (+ highest internal grades: all A+ except one class where A was the highest offered)

Massachusetts Institute of Technology             2016/02
M.S., Electrical Engineering and Computer Science
Advisors: Leslie Pack Kaelbling and Tomás Lozano-Pérez.
Thesis: Towards Practical Theory: Bayesian Optimization and Optimal Exploration
GPA 5.0/5.0 (+ highest internal grades: all A+ except one class where A was the highest offered)


Awards

Funai Overseas Scholarship
FFIT
April 2014 - August 2016

Nakajima Foundation Fellowship
December 2013

Additional Honors & Awards


Selected Professional Services

Program Committee Member: AAAI Conference on Artificial Intelligence, 2019.

Invited Journal reviewer: Journal of Machine Learning Research (JMLR), Neural Computation (MIT press), IEEE Transactions on Neural Networks and Learning Systems