Stochastic Proximal Gradient Descent with Acceleration Techniques. - Atsushi Nitanda (NIPS, 2014)

Accelerated Stochastic Gradient Descent for Minimizing Finite Sums. - Atsushi Nitanda (AISTATS, 2016)

Stochastic Difference of Convex Algorithm and its Application to Training Deep Boltzmann Machines. - Atsushi Nitanda; Taiji Suzuki (AISTATS, 2017)

Stochastic Particle Gradient Descent for Infinite Ensembles. - Atsushi Nitanda; Taiji Suzuki (CoRR, 2017)

Gradient Layer: Enhancing the Convergence of Adversarial Training for Generative Models. - Atsushi Nitanda; Taiji Suzuki (AISTATS, 2018)

Functional Gradient Boosting based on Residual Network Perception. - Atsushi Nitanda; Taiji Suzuki (ICML, 2018)

Hyperbolic Ordinal Embedding. - Atsushi Suzuki; Jing Wang 023; Feng Tian 006; Atsushi Nitanda; Kenji Yamanishi (ACML, 2019)

Stochastic Gradient Descent with Exponential Convergence Rates of Expected Classification Errors. - Atsushi Nitanda; Taiji Suzuki (AISTATS, 2019)

Data Cleansing for Models Trained with SGD. - Satoshi Hara 001; Atsushi Nitanda; Takanori Maehara (NeurIPS, 2019)

Refined Generalization Analysis of Gradient Descent for Over-parameterized Two-layer Neural Networks with Smooth Activations on Classification Problems. - Atsushi Nitanda; Taiji Suzuki (CoRR, 2019)

Functional Gradient Boosting for Learning Residual-like Networks with Statistical Guarantees. - Atsushi Nitanda; Taiji Suzuki (AISTATS, 2020)

Online Robust and Adaptive Learning from Data Streams. - Shintaro Fukushima; Atsushi Nitanda; Kenji Yamanishi (CoRR, 2020)

A Novel Global Spatial Attention Mechanism in Convolutional Neural Network for Medical Image Classification. - Linchuan Xu; Jun Huang 003; Atsushi Nitanda; Ryo Asaoka; Kenji Yamanishi (CoRR, 2020)

Particle Dual Averaging: Optimization of Mean Field Neural Networks with Global Convergence Rate Analysis. - Atsushi Nitanda; Denny Wu; Taiji Suzuki (CoRR, 2020)

Exponential Convergence Rates of Classification Errors on Learning with SGD and Random Features. - Shingo Yashima; Atsushi Nitanda; Taiji Suzuki (AISTATS, 2021)

When does preconditioning help or hurt generalization? - Shun-ichi Amari; Jimmy Ba; Roger Baker Grosse; Xuechen Li 005; Atsushi Nitanda; Taiji Suzuki; Denny Wu; Ji Xu (ICLR, 2021)

Optimal Rates for Averaged Stochastic Gradient Descent under Neural Tangent Kernel Regime. - Atsushi Nitanda; Taiji Suzuki (ICLR, 2021)

Generalization Error Bound for Hyperbolic Ordinal Embedding. - Atsushi Suzuki; Atsushi Nitanda; Jing Wang 023; Linchuan Xu; Kenji Yamanishi; Marc Cavazza (ICML, 2021)

Particle Dual Averaging: Optimization of Mean Field Neural Network with Global Convergence Rate Analysis. - Atsushi Nitanda; Denny Wu; Taiji Suzuki (NeurIPS, 2021)

Deep learning is adaptive to intrinsic dimensionality of model smoothness in anisotropic Besov space. - Taiji Suzuki; Atsushi Nitanda (NeurIPS, 2021)

Generalization Bounds for Graph Embedding Using Negative Sampling: Linear vs Hyperbolic. - Atsushi Suzuki; Atsushi Nitanda; Jing Wang 023; Linchuan Xu; Kenji Yamanishi; Marc Cavazza (NeurIPS, 2021)

BODAME: Bilevel Optimization for Defense Against Model Extraction. - Yuto Mori; Atsushi Nitanda; Akiko Takeda (CoRR, 2021)

Sharp characterization of optimal minibatch size for stochastic finite sum convex optimization. - Atsushi Nitanda; Tomoya Murata; Taiji Suzuki (Knowl. Inf. Syst., 2021)

Convex Analysis of the Mean Field Langevin Dynamics. - Atsushi Nitanda; Denny Wu; Taiji Suzuki (AISTATS, 2022)

Particle Stochastic Dual Coordinate Ascent: Exponential convergent algorithm for mean field neural network optimization. - Kazusato Oko; Taiji Suzuki; Atsushi Nitanda; Denny Wu (ICLR, 2022)

Two-layer neural network on infinite dimensional data: global optimization guarantee in the mean-field regime. - Naoki Nishikawa; Taiji Suzuki; Atsushi Nitanda; Denny Wu (NeurIPS, 2022)

Uniform-in-time propagation of chaos for the mean-field gradient Langevin dynamics. - Taiji Suzuki; Atsushi Nitanda; Denny Wu (ICLR, 2023)

Primal and Dual Analysis of Entropic Fictitious Play for Finite-sum Problems. - Atsushi Nitanda; Kazusato Oko; Denny Wu; Nobuhito Takenouchi; Taiji Suzuki (ICML, 2023)

Tight and fast generalization error bound of graph embedding in metric space. - Atsushi Suzuki; Atsushi Nitanda; Taiji Suzuki; Jing Wang 023; Feng Tian 006; Kenji Yamanishi (ICML, 2023)

Mean-field Langevin dynamics: Time-space discretization, stochastic gradient, and variance reduction. - Taiji Suzuki; Denny Wu; Atsushi Nitanda (NeurIPS, 2023)

Feature learning via mean-field Langevin dynamics: classifying sparse parities and beyond. - Taiji Suzuki; Denny Wu; Kazusato Oko; Atsushi Nitanda (NeurIPS, 2023)

Koopman-Based Bound for Generalization: New Aspect of Neural Networks Regarding Nonlinear Noise Filtering. - Yuka Hashimoto; Sho Sonoda; Isao Ishikawa; Atsushi Nitanda; Taiji Suzuki (CoRR, 2023)

Parameter Averaging for SGD Stabilizes the Implicit Bias towards Flat Regions. - Atsushi Nitanda; Ryuhei Kikuchi; Shugo Maeda (CoRR, 2023)

Convergence of mean-field Langevin dynamics: Time and space discretization, stochastic gradient, and variance reduction. - Taiji Suzuki; Denny Wu; Atsushi Nitanda (CoRR, 2023)

Why is parameter averaging beneficial in SGD? An objective smoothing perspective. - Atsushi Nitanda; Ryuhei Kikuchi; Shugo Maeda; Denny Wu (AISTATS, 2024)

Koopman-based generalization bound: New aspect for full-rank weights. - Yuka Hashimoto; Sho Sonoda; Isao Ishikawa; Atsushi Nitanda; Taiji Suzuki (ICLR, 2024)

Improved statistical and computational complexity of the mean-field Langevin dynamics under structured data. - Atsushi Nitanda; Kazusato Oko; Taiji Suzuki; Denny Wu (ICLR, 2024)

Provably Transformers Harness Multi-Concept Word Semantics for Efficient In-Context Learning. - Dake Bu; Wei Huang 034; Andi Han; Atsushi Nitanda; Taiji Suzuki; Qingfu Zhang 001; Hau-San Wong (NeurIPS, 2024)

Improved Particle Approximation Error for Mean Field Neural Networks. - Atsushi Nitanda (NeurIPS, 2024)

Clustered Invariant Risk Minimization. - Tomoya Murata; Atsushi Nitanda; Taiji Suzuki (AISTATS, 2025)

Direct Distributional Optimization for Provable Alignment of Diffusion Models. - Ryotaro Kawata; Kazusato Oko; Atsushi Nitanda; Taiji Suzuki (ICLR, 2025)

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 (CoRR, 2025)

Uniform convergence of the smooth calibration error and its relationship with functional gradient. - Futoshi Futami; Atsushi Nitanda (CoRR, 2025)

Mirror Descent Policy Optimisation for Robust Constrained Markov Decision Processes. - David Bossens; Atsushi Nitanda (CoRR, 2025)

Provable In-Context Vector Arithmetic via Retrieving Task Concepts. - Dake Bu; Wei Huang 034; Andi Han; Atsushi Nitanda; Qingfu Zhang 001; Hau-San Wong; Taiji Suzuki (CoRR, 2025)