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Machine Learning Theory Lab
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The lab studies online decision making and computational learning theory.
AboutAbout the lab
Exploring the theory of online decision making and learning.
The main research themes of the Machine Learning Theory Lab are online decision making, computational learning theory, and computational complexity.
Based on the principles of the theory of computation, Prof. Takimoto conducts broad research on online decision making, learning theory, and computational complexity theory.
Prof. Hatano works on computer science, particularly machine learning.
In education, Prof. Hatano is in charge of the Information Science Course in the Department of Physics at the School of Science, the Department of Electrical Engineering and Computer Science at the School of Engineering, and the Department of Information Science and Technology at the Graduate School of Information Science and Electrical Engineering.
Key terms
- Online decision making
- A research field dealing with the problem of choosing decisions one at a time as data keeps arriving
- Computational learning theory
- A field that uses mathematical theory to study when machine learning works well
- Computational complexity
- A field that mathematically studies how large the computational effort needed to solve a problem becomes
ResearchResearch themes
The main research themes of the Machine Learning Theory Lab are online decision making, computational learning theory, and computational complexity. Prof. Hatano's research themes also include theoretical analysis and algorithm design for machine learning, and the design and analysis of online prediction algorithms.
*Illustrative imageOnline Decision Making
The lab studies online decision making, with keywords including online prediction, online algorithms, competitive analysis, and game theory.
*Illustrative imageComputational Learning Theory
The lab studies computational learning theory, with keywords including PAC learning, learning with queries, online learning, and boosting.
*Illustrative imageComputational Complexity
The lab studies computational complexity, with keywords including computational complexity theory and logic circuits.
MembersFaculty
The members of the Machine Learning Theory Lab consist of faculty members, postdocs, doctoral, master's, and fourth-year undergraduate students, and research students.As of 2026
- ProfessorEiji Takimoto
- ProfessorKohei Hatano
VoicesStudent voices
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “DOV-1X - National Semiconductor LM393N on printed circuit board-9800” Raimond Spekking / CC BY-SA 4.0 (cropped)
- Online Decision Making: “Decision making in a social context” Glascher Lab / CC BY-SA 4.0 (cropped)
- Computational Learning Theory: “Banana Plant Flask by Max Gruber” Max Gruber / CC BY 4.0 (cropped)
- Computational Complexity: “Chess board 3” 3steph14 / CC BY-SA 4.0 (cropped)
- Related lab (数理工学研究室): “Vodno Telecommunication Tower” kallerna / CC BY-SA 4.0 (cropped)
- Related lab (システム数理研究室): “An UAV displayed by Vyomik drones at Amaravati Drone summit (01)” iMahesh / CC BY-SA 4.0 (cropped)
- Related lab (感覚情報処理研究室): “Auditory System” This work by Cenveo is licensed under a Creative Commons Attribution 3.0 United States (https://creativecommons.org/licenses/by/3.0/us/). / CC BY 3.0 (cropped)
- Related lab (ヒューマン・
データ・インタラクション研究室): “APRENDIZAJE SIGNIFICATIVO” SAMMYTQM / CC BY-SA 4.0 (cropped)
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