← Authors

Atsushi Nitanda

33 papers · 2014–2025 · 5 conferences · across top CS/AI conferences

Achievements

πŸƒ Academic Marathon (11) πŸŒ‰ Interdisciplinary Bridge πŸ—ΊοΈ Taxonomy Completionist (10) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🌍 Conference Polyglot (5) 🐝 Cross-Pollinator (8) 🐺 Lone Wolf (3) 🌱 Topic Pioneer πŸ‘‘ Triple Crown πŸ† Keyword Champion (2) 🀝 Dynamic Duo (25) πŸ—ƒοΈ Keyword Collector (107) ❓ The Questioner (2) ⚑ Prolific Year (7) πŸ’Ž Century Club (33) πŸ”₯ Unstoppable (10) πŸ“ˆ Trend Setter

Conferences

NIPS (10) AISTATS (9) ICLR (7) ICML (6) ACML (1)

Top co-authors

Taiji Suzuki (25) Denny Wu (11) Kazusato Oko (5) Kenji Yamanishi (4) Jing Wang (4) Atsushi Suzuki (4) Feng Tian (2) Linchuan Xu (2) Hau-San Wong (2) Dake Bu (2)

Keywords

stochastic gradient descent (7) neural network (5) neural network optimization (5) hyperbolic space (4) variance reduction (3) exponential convergence (3) gradient boosting (3) ordinal embedding (2) functional gradient (2) mean-field langevin dynamics (2) generalization error bound (2) convex optimization (2) binary classification (2) residual network (2) convergence analysis (2) graph embedding (2) global convergence (2) gradient descent (2) stochastic optimization (2) propagation of chao (2)

Papers

Propagation of Chaos for Mean-Field Langevin Dynamics and its Application to Model Ensemble ICML 2025
Provable In-Context Vector Arithmetic via Retrieving Task Concepts ICML 2025
Clustered Invariant Risk Minimization AISTATS 2025
Direct Distributional Optimization for Provable Alignment of Diffusion Models ICLR 2025
Improved Particle Approximation Error for Mean Field Neural Networks NIPS 2024
Provably Transformers Harness Multi-Concept Word Semantics for Efficient In-Context Learning NIPS 2024
Improved statistical and computational complexity of the mean-field Langevin dynamics under structured data ICLR 2024
Koopman-based generalization bound: New aspect for full-rank weights ICLR 2024
Why is parameter averaging beneficial in SGD? An objective smoothing perspective AISTATS 2024
Uniform-in-time propagation of chaos for the mean-field gradient Langevin dynamics ICLR 2023
Convergence of mean-field Langevin dynamics: time-space discretization, stochastic gradient, and variance reduction NIPS 2023
Feature learning via mean-field Langevin dynamics: classifying sparse parities and beyond NIPS 2023
Tight and fast generalization error bound of graph embedding in metric space ICML 2023
Primal and Dual Analysis of Entropic Fictitious Play for Finite-sum Problems ICML 2023
Particle Stochastic Dual Coordinate Ascent: Exponential convergent algorithm for mean field neural network optimization ICLR 2022
Two-layer neural network on infinite dimensional data: global optimization guarantee in the mean-field regime NIPS 2022
Convex Analysis of the Mean Field Langevin Dynamics AISTATS 2022
Exponential Convergence Rates of Classification Errors on Learning with SGD and Random Features AISTATS 2021
Generalization Bounds for Graph Embedding Using Negative Sampling: Linear vs Hyperbolic NIPS 2021
Deep learning is adaptive to intrinsic dimensionality of model smoothness in anisotropic Besov space NIPS 2021
Particle Dual Averaging: Optimization of Mean Field Neural Network with Global Convergence Rate Analysis NIPS 2021
When does preconditioning help or hurt generalization? ICLR 2021
Optimal Rates for Averaged Stochastic Gradient Descent under Neural Tangent Kernel Regime ICLR 2021
Generalization Error Bound for Hyperbolic Ordinal Embedding ICML 2021
Functional Gradient Boosting for Learning Residual-like Networks with Statistical Guarantees AISTATS 2020
Stochastic Gradient Descent with Exponential Convergence Rates of Expected Classification Errors AISTATS 2019
Data Cleansing for Models Trained with SGD NIPS 2019
Hyperbolic Ordinal Embedding ACML 2019
Gradient Layer: Enhancing the Convergence of Adversarial Training for Generative Models AISTATS 2018
Functional Gradient Boosting based on Residual Network Perception ICML 2018
Stochastic Difference of Convex Algorithm and its Application to Training Deep Boltzmann Machines AISTATS 2017
Accelerated Stochastic Gradient Descent for Minimizing Finite Sums AISTATS 2016
Stochastic Proximal Gradient Descent with Acceleration Techniques NIPS 2014