Atsushi Nitanda

73 papers A* 23A 9C 1Journal 39Unranked 1

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Alternating Diffusion for Proximal Sampling with Zeroth Order Queries. CoRR Hirohane Takagi, Atsushi Nitanda
2026	J	`jnl`	Auto-Clustering with Continuous Distribution Estimation on Centroids. Mach. Learn. Yuangang Pan, Yinghua Yao, Atsushi Nitanda, Joey Tianyi Zhou, Ivor W. Tsang
2025	A	`conf`	Clustered Invariant Risk Minimization. AISTATS Tomoya Murata, Atsushi Nitanda, Taiji Suzuki
2025	J	`jnl`	Consistency Is Not Always Correct: Towards Understanding the Role of Exploration in Post-Training Reasoning. CoRR Dake Bu, Wei Huang, Andi Han, Atsushi Nitanda, Bo Xue, Qingfu Zhang, Hau-San Wong, Taiji Suzuki
2025	A*	`conf`	Direct Distributional Optimization for Provable Alignment of Diffusion Models. ICLR Ryotaro Kawata, Kazusato Oko, Atsushi Nitanda, Taiji Suzuki
2025	J	`jnl`	Direct Distributional Optimization for Provable Alignment of Diffusion Models. CoRR Ryotaro Kawata, Kazusato Oko, Atsushi Nitanda, Taiji Suzuki
2025	J	`jnl`	How Does Preconditioning Guide Feature Learning in Deep Neural Networks? CoRR Kotaro Yoshida, Atsushi Nitanda
2025	J	`jnl`	Mirror Descent Policy Optimisation for Robust Constrained Markov Decision Processes. CoRR David Bossens, Atsushi Nitanda
2025	J	`jnl`	Mirror Descent Policy Optimisation for Robust Constrained Markov Decision Processes. Trans. Mach. Learn. Res. David Mark Bossens, Atsushi Nitanda
2025	A*	`conf`	Propagation of Chaos for Mean-Field Langevin Dynamics and its Application to Model Ensemble. ICML Atsushi Nitanda, Anzelle Lee, Damian Tan Xing Kai, Mizuki Sakaguchi, Taiji Suzuki
2025	J	`jnl`	Propagation of Chaos for Mean-Field Langevin Dynamics and its Application to Model Ensemble. CoRR Atsushi Nitanda, Anzelle Lee, Damian Tan Xing Kai, Mizuki Sakaguchi, Taiji Suzuki
2025	J	`jnl`	Provable Benefit of Curriculum in Transformer Tree-Reasoning Post-Training. CoRR Dake Bu, Wei Huang, Andi Han, Atsushi Nitanda, Hau-San Wong, Qingfu Zhang, Taiji Suzuki
2025	A*	`conf`	Provable In-Context Vector Arithmetic via Retrieving Task Concepts. ICML Dake Bu, Wei Huang, Andi Han, Atsushi Nitanda, Qingfu Zhang, Hau-San Wong, Taiji Suzuki
2025	J	`jnl`	Provable In-Context Vector Arithmetic via Retrieving Task Concepts. CoRR Dake Bu, Wei Huang, Andi Han, Atsushi Nitanda, Qingfu Zhang, Hau-San Wong, Taiji Suzuki
2025	J	`jnl`	Statistical Analysis of the Sinkhorn Iterations for Two-Sample Schrödinger Bridge Estimation. CoRR Ibuki Maeda, Rentian Yao, Atsushi Nitanda
2025	J	`jnl`	Towards a Unified Analysis of Neural Networks in Nonparametric Instrumental Variable Regression: Optimization and Generalization. CoRR Zonghao Chen, Atsushi Nitanda, Arthur Gretton, Taiji Suzuki
2025	J	`jnl`	Uniform convergence of the smooth calibration error and its relationship with functional gradient. CoRR Futoshi Futami, Atsushi Nitanda
2025	J	`jnl`	Unlearning Misalignment for Personalized LLM Adaptation via Instance-Response-Dependent Discrepancies. Trans. Mach. Learn. Res. Cheng Chen, Atsushi Nitanda, Ivor W. Tsang
2024	A*	`conf`	Improved Particle Approximation Error for Mean Field Neural Networks. NeurIPS Atsushi Nitanda
2024	J	`jnl`	Improved Particle Approximation Error for Mean Field Neural Networks. CoRR Atsushi Nitanda
2024	A*	`conf`	Improved statistical and computational complexity of the mean-field Langevin dynamics under structured data. ICLR Atsushi Nitanda, Kazusato Oko, Taiji Suzuki, Denny Wu
2024	A*	`conf`	Koopman-based generalization bound: New aspect for full-rank weights. ICLR Yuka Hashimoto, Sho Sonoda, Isao Ishikawa, Atsushi Nitanda, Taiji Suzuki
2024	A*	`conf`	Provably Transformers Harness Multi-Concept Word Semantics for Efficient In-Context Learning. NeurIPS Dake Bu, Wei Huang, Andi Han, Atsushi Nitanda, Taiji Suzuki, Qingfu Zhang, Hau-San Wong
2024	J	`jnl`	Provably Transformers Harness Multi-Concept Word Semantics for Efficient In-Context Learning. CoRR Dake Bu, Wei Huang, Andi Han, Atsushi Nitanda, Taiji Suzuki, Qingfu Zhang, Hau-San Wong
2024	A	`conf`	Why is parameter averaging beneficial in SGD? An objective smoothing perspective. AISTATS Atsushi Nitanda, Ryuhei Kikuchi, Shugo Maeda, Denny Wu
2023	J	`jnl`	Convergence of mean-field Langevin dynamics: Time and space discretization, stochastic gradient, and variance reduction. CoRR Taiji Suzuki, Denny Wu, Atsushi Nitanda
2023	A*	`conf`	Feature learning via mean-field Langevin dynamics: classifying sparse parities and beyond. NeurIPS Taiji Suzuki, Denny Wu, Kazusato Oko, Atsushi Nitanda
2023	J	`jnl`	Koopman-Based Bound for Generalization: New Aspect of Neural Networks Regarding Nonlinear Noise Filtering. CoRR Yuka Hashimoto, Sho Sonoda, Isao Ishikawa, Atsushi Nitanda, Taiji Suzuki
2023	A*	`conf`	Mean-field Langevin dynamics: Time-space discretization, stochastic gradient, and variance reduction. NeurIPS Taiji Suzuki, Denny Wu, Atsushi Nitanda
2023	J	`jnl`	Parameter Averaging for SGD Stabilizes the Implicit Bias towards Flat Regions. CoRR Atsushi Nitanda, Ryuhei Kikuchi, Shugo Maeda
2023	A*	`conf`	Primal and Dual Analysis of Entropic Fictitious Play for Finite-sum Problems. ICML Atsushi Nitanda, Kazusato Oko, Denny Wu, Nobuhito Takenouchi, Taiji Suzuki
2023	J	`jnl`	Primal and Dual Analysis of Entropic Fictitious Play for Finite-sum Problems. CoRR Atsushi Nitanda, Kazusato Oko, Denny Wu, Nobuhito Takenouchi, Taiji Suzuki
2023	A*	`conf`	Tight and fast generalization error bound of graph embedding in metric space. ICML Atsushi Suzuki, Atsushi Nitanda, Taiji Suzuki, Jing Wang, Feng Tian, Kenji Yamanishi
2023	J	`jnl`	Tight and fast generalization error bound of graph embedding in metric space. CoRR Atsushi Suzuki, Atsushi Nitanda, Taiji Suzuki, Jing Wang, Feng Tian, Kenji Yamanishi
2023	A*	`conf`	Uniform-in-time propagation of chaos for the mean-field gradient Langevin dynamics. ICLR Taiji Suzuki, Atsushi Nitanda, Denny Wu
2022	A	`conf`	Convex Analysis of the Mean Field Langevin Dynamics. AISTATS Atsushi Nitanda, Denny Wu, Taiji Suzuki
2022	J	`jnl`	Convex Analysis of the Mean Field Langevin Dynamics. CoRR Atsushi Nitanda, Denny Wu, Taiji Suzuki
2022	A*	`conf`	Particle Stochastic Dual Coordinate Ascent: Exponential convergent algorithm for mean field neural network optimization. ICLR Kazusato Oko, Taiji Suzuki, Atsushi Nitanda, Denny Wu
2022	A*	`conf`	Two-layer neural network on infinite dimensional data: global optimization guarantee in the mean-field regime. NeurIPS Naoki Nishikawa, Taiji Suzuki, Atsushi Nitanda, Denny Wu
2021	J	`jnl`	BODAME: Bilevel Optimization for Defense Against Model Extraction. CoRR Yuto Mori, Atsushi Nitanda, Akiko Takeda
2021	A*	`conf`	Deep learning is adaptive to intrinsic dimensionality of model smoothness in anisotropic Besov space. NeurIPS Taiji Suzuki, Atsushi Nitanda
2021	A	`conf`	Exponential Convergence Rates of Classification Errors on Learning with SGD and Random Features. AISTATS Shingo Yashima, Atsushi Nitanda, Taiji Suzuki
2021	A*	`conf`	Generalization Bounds for Graph Embedding Using Negative Sampling: Linear vs Hyperbolic. NeurIPS Atsushi Suzuki, Atsushi Nitanda, Jing Wang, Linchuan Xu, Kenji Yamanishi, Marc Cavazza
2021	A*	`conf`	Generalization Error Bound for Hyperbolic Ordinal Embedding. ICML Atsushi Suzuki, Atsushi Nitanda, Jing Wang, Linchuan Xu, Kenji Yamanishi, Marc Cavazza
2021	J	`jnl`	Generalization Error Bound for Hyperbolic Ordinal Embedding. CoRR Atsushi Suzuki, Atsushi Nitanda, Jing Wang, Linchuan Xu, Marc Cavazza, Kenji Yamanishi
2021	A*	`conf`	Optimal Rates for Averaged Stochastic Gradient Descent under Neural Tangent Kernel Regime. ICLR Atsushi Nitanda, Taiji Suzuki
2021	A*	`conf`	Particle Dual Averaging: Optimization of Mean Field Neural Network with Global Convergence Rate Analysis. NeurIPS Atsushi Nitanda, Denny Wu, Taiji Suzuki
2021	J	`jnl`	Sharp characterization of optimal minibatch size for stochastic finite sum convex optimization. Knowl. Inf. Syst. Atsushi Nitanda, Tomoya Murata, Taiji Suzuki
2021	A*	`conf`	When does preconditioning help or hurt generalization? ICLR Shun-ichi Amari, Jimmy Ba, Roger Baker Grosse, Xuechen Li, Atsushi Nitanda, Taiji Suzuki, Denny Wu, Ji Xu
2020	J	`jnl`	A Novel Global Spatial Attention Mechanism in Convolutional Neural Network for Medical Image Classification. CoRR Linchuan Xu, Jun Huang, Atsushi Nitanda, Ryo Asaoka, Kenji Yamanishi
2020	A	`conf`	Functional Gradient Boosting for Learning Residual-like Networks with Statistical Guarantees. AISTATS Atsushi Nitanda, Taiji Suzuki
2020	J	`jnl`	Online Robust and Adaptive Learning from Data Streams. CoRR Shintaro Fukushima, Atsushi Nitanda, Kenji Yamanishi
2020	J	`jnl`	Optimal Rates for Averaged Stochastic Gradient Descent under Neural Tangent Kernel Regime. CoRR Atsushi Nitanda, Taiji Suzuki
2020	J	`jnl`	Particle Dual Averaging: Optimization of Mean Field Neural Networks with Global Convergence Rate Analysis. CoRR Atsushi Nitanda, Denny Wu, Taiji Suzuki
2020	J	`jnl`	When Does Preconditioning Help or Hurt Generalization? CoRR Shun-ichi Amari, Jimmy Ba, Roger B. Grosse, Xuechen Li, Atsushi Nitanda, Taiji Suzuki, Denny Wu, Ji Xu
2019	A*	`conf`	Data Cleansing for Models Trained with SGD. NeurIPS Satoshi Hara, Atsushi Nitanda, Takanori Maehara
2019	J	`jnl`	Data Cleansing for Models Trained with SGD. CoRR Satoshi Hara, Atsushi Nitanda, Takanori Maehara
2019	J	`jnl`	Deep learning is adaptive to intrinsic dimensionality of model smoothness in anisotropic Besov space. CoRR Taiji Suzuki, Atsushi Nitanda
2019	J	`jnl`	Exponential Convergence Rates of Classification Errors on Learning with SGD and Random Features. CoRR Shingo Yashima, Atsushi Nitanda, Taiji Suzuki
2019	C	`conf`	Hyperbolic Ordinal Embedding. ACML Atsushi Suzuki, Jing Wang, Feng Tian, Atsushi Nitanda, Kenji Yamanishi
2019	J	`jnl`	Refined Generalization Analysis of Gradient Descent for Over-parameterized Two-layer Neural Networks with Smooth Activations on Classification Problems. CoRR Atsushi Nitanda, Taiji Suzuki
2019	A*	`conf`	Sharp Characterization of Optimal Minibatch Size for Stochastic Finite Sum Convex Optimization. ICDM Atsushi Nitanda, Tomoya Murata, Taiji Suzuki
2019	A	`conf`	Stochastic Gradient Descent with Exponential Convergence Rates of Expected Classification Errors. AISTATS Atsushi Nitanda, Taiji Suzuki
2018	A*	`conf`	Functional Gradient Boosting based on Residual Network Perception. ICML Atsushi Nitanda, Taiji Suzuki
2018	J	`jnl`	Functional Gradient Boosting based on Residual Network Perception. CoRR Atsushi Nitanda, Taiji Suzuki
2018	A	`conf`	Gradient Layer: Enhancing the Convergence of Adversarial Training for Generative Models. AISTATS Atsushi Nitanda, Taiji Suzuki
2018	J	`jnl`	Gradient Layer: Enhancing the Convergence of Adversarial Training for Generative Models. CoRR Atsushi Nitanda, Taiji Suzuki
2018	J	`jnl`	Stochastic Gradient Descent with Exponential Convergence Rates of Expected Classification Errors. CoRR Atsushi Nitanda, Taiji Suzuki
2017	A	`conf`	Stochastic Difference of Convex Algorithm and its Application to Training Deep Boltzmann Machines. AISTATS Atsushi Nitanda, Taiji Suzuki
2017	J	`jnl`	Stochastic Particle Gradient Descent for Infinite Ensembles. CoRR Atsushi Nitanda, Taiji Suzuki
2016	A	`conf`	Accelerated Stochastic Gradient Descent for Minimizing Finite Sums. AISTATS Atsushi Nitanda
2015	J	`jnl`	Accelerated Stochastic Gradient Descent for Minimizing Finite Sums. CoRR Atsushi Nitanda
2014	—	`conf`	Stochastic Proximal Gradient Descent with Acceleration Techniques. NIPS Atsushi Nitanda