Atsushi Nitanda

Clustered Invariant Risk Minimization AISTATS 2025 Direct Distributional Optimization for Provable Alignment of Diffusion Models ICLR 2025 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 Improved Particle Approximation Error for Mean Field Neural Networks NeurIPS 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 Provably Transformers Harness Multi-Concept Word Semantics for Efficient In-Context Learning NeurIPS 2024 Why is parameter averaging beneficial in SGD? An objective smoothing perspective AISTATS 2024 Convergence of mean-field Langevin dynamics: time-space discretization, stochastic gradient, and variance reduction NeurIPS 2023 Feature learning via mean-field Langevin dynamics: classifying sparse parities and beyond NeurIPS 2023 Primal and Dual Analysis of Entropic Fictitious Play for Finite-sum Problems ICML 2023 Tight and fast generalization error bound of graph embedding in metric space ICML 2023 Uniform-in-time propagation of chaos for the mean-field gradient Langevin dynamics ICLR 2023 Convex Analysis of the Mean Field Langevin Dynamics AISTATS 2022 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 NeurIPS 2022 Deep learning is adaptive to intrinsic dimensionality of model smoothness in anisotropic Besov space NeurIPS 2021 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 NeurIPS 2021 Generalization Error Bound for Hyperbolic Ordinal Embedding ICML 2021 Optimal Rates for Averaged Stochastic Gradient Descent under Neural Tangent Kernel Regime ICLR 2021 Particle Dual Averaging: Optimization of Mean Field Neural Network with Global Convergence Rate Analysis NeurIPS 2021 When does preconditioning help or hurt generalization? ICLR 2021 Functional Gradient Boosting for Learning Residual-like Networks with Statistical Guarantees AISTATS 2020 Data Cleansing for Models Trained with SGD NeurIPS 2019 Hyperbolic Ordinal Embedding ACML 2019 Stochastic Gradient Descent with Exponential Convergence Rates of Expected Classification Errors AISTATS 2019 Functional Gradient Boosting based on Residual Network Perception ICML 2018 Gradient Layer: Enhancing the Convergence of Adversarial Training for Generative Models AISTATS 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 NeurIPS 2014