Kenji Kawaguchi and Yoshua Bengio. Depth with Nonlinearity Creates No Bad Local Minima in ResNets.
Neural Networks, 118, 167-174, 2019.
[pdf] [BibTex] [Video] One of the 25 most downloaded articles from Neural Networks in the last 90 days
Kenji Kawaguchi, Jiaoyang Huang and Leslie Pack Kaelbling. Every Local Minimum Value is the Global Minimum Value of Induced Model in Non-convex Machine Learning. Neural Computation, accepted, MIT press, 2019.
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.
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 and System Safety (RESS), 138, 253-262, 2015.
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 (JNST), 52(3), 308-315, 2015.
Kenji Kawaguchi and Jiaoyang Huang. Gradient Descent Finds Global Minima for Generalizable Deep Neural Networks of Practical Sizes.
In Proceedings of the 57th Allerton Conference on Communication, Control, and Computing (Allerton), 2019.
[pdf] [BibTex] [Video]
Kenji Kawaguchi*, Bo Xie*, Vikas Verma, and Le Song. Deep Semi-Random Features for Nonlinear Function Approximation.
In Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI), 2018.
Kenji Kawaguchi. Deep Learning without Poor Local Minima.
In Advances in Neural Information Processing (NeurIPS), 2016.
[pdf] [BibTex] [Spotlight Video] [Talk] Selected for NeurIPS oral presentation (2% submissions)
Kenji Kawaguchi, Leslie Pack Kaelbling and Yoshua Bengio. Generalization in Deep Learning. In Mathematics of Deep Learning, Cambridge University Press, to appear.
Prepint available as: MIT-CSAIL-TR-2018-014, Massachusetts Institute of Technology, 2018.
[pdf] [BibTex] [Code]
Tomaso Poggio, Kenji Kawaguchi, Qianli Liao, Brando Miranda, Lorenzo Rosasco, Xavier Boix, Jack Hidary and Hrushikesh Mhaskar. Theory of Deep Learning III: explaining the
Massachusetts Institute of Technology CBMM Memo No. 73, 2018.
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.
Invited talk at International Conference:
• Minisymposium on Theoretical Foundations of Deep Learning, ICIAM 2019, Spain.
Seminar, university, and research lab:
• Stanford / CS theory lunch, 2019.
• Harvard University / Professor Horng-Tzer Yau lab, 2019.
• Carnegie Mellon University (CMU) / AI Seminar Series, 2019.
• Carnegie Mellon University (CMU) / Professor Eric P. Xing lab, 2019.
• Toyota Technological Institute at Chicago (TTIC) / Young Researcher Seminar Series, 2019.
• PhILMs center / invited by Professor George Em Karniadakis at Brown University, 2019.
• Google Research Cambridge / invited by Dr. Dilip Krishnan, 2017.
Massachusetts Institute of Technology:
• MIT / Professor David Sontag lab, 2017.
• MIT / Professor Tomaso Poggio lab, 2016.
• MIT / Machine Learning Tea, 2016.
• Institute for Advanced Study (IAS): Workshop on Theory of Deep Learning: Where next?, organized by Professors Sanjeev Arora, Joan Bruna, Rong Ge, Suriya Gunasekar, Jason Lee, Bin Yu, 2019.
• CRM/CIFAR Deep Learning Summer School, organized by Professors Aaron Courville and Yoshua Bengio, 2016.
Invited research visits:
• Microsoft Research (MSR), Redmond, Summer 2018.
• TTIC, Chicago, Fall 2019.
• Isaac Newton Institute for Mathematical Sciences at the University of Cambridge: Invited to be in the participant list for the proposal of a 2021 program by the organization team of Prof. Peter Bartlett, Prof. Arnulf Jentzen, Prof. Anders Hansen, Prof. Gitta Kutyniok, Prof. Stephane Mallat, and Prof. Carola Schönlieb.
Program Committee Member:
• AAAI Conference on Artificial Intelligence (AAAI), 2019.
• Conference on Uncertainty in Artificial Intelligence (UAI), 2019.
• AAAI Conference on Artificial Intelligence (AAAI), 2020.
Invited Journal reviewer: Journal of Machine Learning Research (JMLR), Annals of Statistics (Ann. Stat.), Neural Computation (MIT press), IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS)
Invited Conference reviewer: Conference on Neural Information Processing Systems (NeurIPS), 2019
Massachusetts Institute of Technology present
Ph.D. student, Electrical Engineering and Computer Science
Advisor: Leslie Pack Kaelbling
Thesis committee: Yoshua Bengio and Suvrit Sra
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)
Funai Overseas Scholarship
April 2014 - August 2016
Nakajima Foundation Fellowship
Additional Honors & Awards:
• AAAI Student Scholarship 2018
• NeurIPS Travel Award 2016
• A research fund from the Japan Science and Technology Agency
• More than 10 awards for leadership, sports and academics