Atsushi Nitanda
Associate Professor
Studies the mathematical foundations of machine learning, stochastic optimization and sampling methods.
Atsushi Nitanda's lab at NTU works on learning theory & optimization and data management & big data. 10 papers since 2023, mostly Machine Learning. 3 PhD students and 1 master's student. 1 alumnus.
At a glance
- papers, 2023–2026
- 3/ year
- at T1 venues
- 0%
- PhD students
- 3
- MS students
- 1
- alumni listed
- 1
Papers by yearfrom the lab homepage, 2023–2026
- 1 papers2026 · T1 0 · collecting
- 4 papers2025 · T1 0
- 1 papers2024 · T1 0
- 4 papers2023 · T1 0
T1 venuesOtherVenues: Machine Learning
Research focuspaper titles that use each topic's words
- Learning theory & optimization2 papers
- Data management & big data2 papers
- sampling methods0 papers
- mathematical foundations0 papers
- stochastic optimization0 papers
Paper list
From the lab homepage.
20261 paper
- The International Conference on Learning Representations (ICLR2026), . [openreview]Smooth Calibration Error: Uniform Convergence and Functional Gradient Analysis
20254 papers
- Machine LearningAuto-clustering with Continuous Distribution Estimation on Centroids
- The International Conference on Artificial Intelligence and Statistics (AISTATS2025), Proceedings of Machine Learning Research, 258:1612--1620, .Clustered Invariant Risk Minimization
- The International Conference on Machine Learning (ICML2025), Proceedings of Machine Learning Research, 267:5669--5724, .Provable In-Context Vector Arithmetic via Retrieving Task Concepts
- Transactions on Machine Learning Research (TMLR), .Unlearning Misalignment for Personalized LLM Adaptation via Instance-Response-Dependent Discrepancies
20241 paper
- The International Conference on Learning Representations (ICLR2024), . [openreview]Improved Statistical and Computational Complexity of the Mean-field Langevin Dynamics under Structured Data
20234 papers
- The Annual Conference on Neural Information Processing Systems (NeurIPS2023), In Advances in Neural Information Processing Systems, 36:34536--34556, .Feature Learning via Mean-field Langevin Dynamics: Classifying Sparse Parities and Beyond
- The International Conference on Machine Learning (ICML2023), Proceedings of Machine Learning Research, 202:33268--33284, .Tight and Fast Generalization Error Bound of Graph Embedding in Metric Space
- Journal of Statistical Mechanics: Theory and Experiment (JSTAT), (11):114007, .Two-layer Neural Network on Infinite-dimensional Data: Global Optimization Guarantee in the mean-field regime
- The International Conference on Learning Representations (ICLR2023), . [openreview]Uniform-in-time Propagation of Chaos for the Mean Field Gradient Langevin Dynamics
Where alumni went1 listed
Apply to NTUFall 2027
Not announced yet
Computing and Data Science PhD
- Round
- Main
- Opens
- —
- GRE
- not required
- English
- TOEFL/IELTS
- Fee
- See program page
Not yet confirmed for this intake.
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Data
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- Papers
- The lab homepage
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