| 2025 | AISTATS | Clustered Invariant Risk Minimization. | Tomoya Murata, Atsushi Nitanda, Taiji Suzuki |
| 2025 | ICLR | Direct Distributional Optimization for Provable Alignment of Diffusion Models. | Ryotaro Kawata, Kazusato Oko, Atsushi Nitanda, Taiji Suzuki |
| 2025 | ICML | Provable In-Context Vector Arithmetic via Retrieving Task Concepts. | Dake Bu, Wei Huang, Andi Han, Atsushi Nitanda, Qingfu Zhang, Hau-San Wong, Taiji Suzuki |
| 2025 | ICML | Propagation of Chaos for Mean-Field Langevin Dynamics and its Application to Model Ensemble. | Atsushi Nitanda, Anzelle Lee, Damian Tan Xing Kai, Mizuki Sakaguchi, Taiji Suzuki |
| 2024 | AISTATS | Why is parameter averaging beneficial in SGD? An objective smoothing perspective. | Atsushi Nitanda, Ryuhei Kikuchi, Shugo Maeda, Denny Wu |
| 2024 | ICLR | Koopman-based generalization bound: New aspect for full-rank weights. | Yuka Hashimoto, Sho Sonoda, Isao Ishikawa, Atsushi Nitanda, Taiji Suzuki |
| 2024 | ICLR | Improved statistical and computational complexity of the mean-field Langevin dynamics under structured data. | Atsushi Nitanda, Kazusato Oko, Taiji Suzuki, Denny Wu |
| 2023 | ICLR | Uniform-in-time propagation of chaos for the mean-field gradient Langevin dynamics. | Taiji Suzuki, Atsushi Nitanda, Denny Wu |
| 2023 | ICML | Primal and Dual Analysis of Entropic Fictitious Play for Finite-sum Problems. | Atsushi Nitanda, Kazusato Oko, Denny Wu, Nobuhito Takenouchi, Taiji Suzuki |
| 2023 | ICML | Tight and fast generalization error bound of graph embedding in metric space. | Atsushi Suzuki, Atsushi Nitanda, Taiji Suzuki, Jing Wang, Feng Tian, Kenji Yamanishi |
| 2022 | AISTATS | Convex Analysis of the Mean Field Langevin Dynamics. | Atsushi Nitanda, Denny Wu, Taiji Suzuki |
| 2022 | ICLR | Particle Stochastic Dual Coordinate Ascent: Exponential convergent algorithm for mean field neural network optimization. | Kazusato Oko, Taiji Suzuki, Atsushi Nitanda, Denny Wu |
| 2021 | AISTATS | Exponential Convergence Rates of Classification Errors on Learning with SGD and Random Features. | Shingo Yashima, Atsushi Nitanda, Taiji Suzuki |
| 2021 | ICLR | When does preconditioning help or hurt generalization? | Shun-ichi Amari, Jimmy Ba, Roger Baker Grosse, Xuechen Li, Atsushi Nitanda, Taiji Suzuki, Denny Wu, Ji Xu |
| 2021 | ICLR | Optimal Rates for Averaged Stochastic Gradient Descent under Neural Tangent Kernel Regime. | Atsushi Nitanda, Taiji Suzuki |
| 2021 | ICML | Generalization Error Bound for Hyperbolic Ordinal Embedding. | Atsushi Suzuki, Atsushi Nitanda, Jing Wang, Linchuan Xu, Kenji Yamanishi, Marc Cavazza |
| 2020 | AISTATS | Functional Gradient Boosting for Learning Residual-like Networks with Statistical Guarantees. | Atsushi Nitanda, Taiji Suzuki |
| 2019 | ACML | Hyperbolic Ordinal Embedding. | Atsushi Suzuki, Jing Wang, Feng Tian, Atsushi Nitanda, Kenji Yamanishi |
| 2019 | AISTATS | Stochastic Gradient Descent with Exponential Convergence Rates of Expected Classification Errors. | Atsushi Nitanda, Taiji Suzuki |
| 2019 | ICDM | Sharp Characterization of Optimal Minibatch Size for Stochastic Finite Sum Convex Optimization. | Atsushi Nitanda, Tomoya Murata, Taiji Suzuki |
| 2018 | AISTATS | Gradient Layer: Enhancing the Convergence of Adversarial Training for Generative Models. | Atsushi Nitanda, Taiji Suzuki |
| 2018 | ICML | Functional Gradient Boosting based on Residual Network Perception. | Atsushi Nitanda, Taiji Suzuki |
| 2017 | AISTATS | Stochastic Difference of Convex Algorithm and its Application to Training Deep Boltzmann Machines. | Atsushi Nitanda, Taiji Suzuki |
| 2016 | AISTATS | Accelerated Stochastic Gradient Descent for Minimizing Finite Sums. | Atsushi Nitanda |