Papers citing 'Stochastic Particle-Optimization Sampling and the Non-Asymptotic Convergence Theory'

Title
SLED: Self Logits Evolution Decoding for Improving Factuality in Large Language ModelsNeural Information Processing Systems (NeurIPS), 2024 Jianyi Zhang Da-Cheng Juan Cyrus Rashtchian Chun-Sung Ferng Heinrich Jiang Yiran Chen 315 12 0 01 Nov 2024
Stein Variational Evolution StrategiesConference on Uncertainty in Artificial Intelligence (UAI), 2024 Cornelius V. Braun Robert T. Lange Marc Toussaint 417 3 0 14 Oct 2024
Particle Guidance: non-I.I.D. Diverse Sampling with Diffusion Models Gabriele Corso Yilun Xu Valentin De Bortoli Regina Barzilay Tommi Jaakkola DiffM 313 38 0 19 Oct 2023
Towards Building the Federated GPT: Federated Instruction TuningIEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2023 Jianyi Zhang Saeed Vahidian Martin Kuo Chunyuan Li Ruiyi Zhang Tong Yu Jiuxiang Gu Guoyin Wang Yiran Chen ALM FedML 220 175 0 09 May 2023
Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance ReductionInternational Conference on Machine Learning (ICML), 2022 Jianyi Zhang Ang Li Minxue Tang Jingwei Sun Xiang Chen Fan Zhang Chang Chen Yiran Chen Xue Yang FedML 118 69 0 30 Sep 2022
Feature Space Particle Inference for Neural Network EnsemblesInternational Conference on Machine Learning (ICML), 2022 Shingo Yashima Teppei Suzuki Kohta Ishikawa Ikuro Sato Rei Kawakami BDL 180 12 0 02 Jun 2022
DPVI: A Dynamic-Weight Particle-Based Variational Inference Framework Chao Zhang Zhijian Li Hui Qian Xin Du 131 10 0 02 Dec 2021
De-randomizing MCMC dynamics with the diffusion Stein operator Zheyan Shen Markus Heinonen Samuel Kaski DiffM 182 4 0 07 Oct 2021
Stein Variational Gradient Descent with Multiple KernelCognitive Computation (Cogn Comput), 2021 Qingzhong Ai Shiyu Liu Lirong He Zenglin Xu 287 8 0 20 Jul 2021
On Stein Variational Neural Network Ensembles Francesco DÁngelo Vincent Fortuin F. Wenzel UQCV BDL 191 30 0 20 Jun 2021
Learning Equivariant Energy Based Models with Equivariant Stein Variational Gradient DescentNeural Information Processing Systems (NeurIPS), 2021 P. Jaini Lars Holdijk Max Welling 272 12 0 15 Jun 2021
Repulsive Attention: Rethinking Multi-head Attention as Bayesian InferenceConference on Empirical Methods in Natural Language Processing (EMNLP), 2020 Bang An Jie Lyu Zhenyi Wang Chunyuan Li Changwei Hu Fei Tan Ruiyi Zhang Yifan Hu Changyou Chen AAML 249 29 0 20 Sep 2020
Blindness of score-based methods to isolated components and mixing proportions Wenliang K. Li Heishiro Kanagawa 312 35 0 23 Aug 2020
Influence Diagram Bandits: Variational Thompson Sampling for Structured Bandit ProblemsInternational Conference on Machine Learning (ICML), 2020 Tong Yu Branislav Kveton Zheng Wen Ruiyi Zhang Ole J. Mengshoel TDI 156 2 0 09 Jul 2020
SVGD as a kernelized Wasserstein gradient flow of the chi-squared divergenceNeural Information Processing Systems (NeurIPS), 2020 Sinho Chewi Thibaut Le Gouic Chen Lu Tyler Maunu Philippe Rigollet 276 76 0 03 Jun 2020
Stabilizing Training of Generative Adversarial Nets via Langevin Stein Variational Gradient Descent Dong Wang Xiaoqian Qin F. Song Li Cheng GAN 165 26 0 22 Apr 2020
Stein Self-Repulsive Dynamics: Benefits From Past SamplesNeural Information Processing Systems (NeurIPS), 2020 Mao Ye Zhaolin Ren Qiang Liu 154 8 0 21 Feb 2020
A Wasserstein Minimum Velocity Approach to Learning Unnormalized ModelsInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2020 Ziyu Wang Shuyu Cheng Yueru Li Jun Zhu Bo Zhang 137 14 0 18 Feb 2020
Cyclical Stochastic Gradient MCMC for Bayesian Deep LearningInternational Conference on Learning Representations (ICLR), 2019 Ruqi Zhang Chunyuan Li Jianyi Zhang Changyou Chen A. Wilson BDL 223 290 0 11 Feb 2019
Sliced-Wasserstein Flows: Nonparametric Generative Modeling via Optimal Transport and Diffusions Antoine Liutkus Umut Simsekli Szymon Majewski Alain Durmus Fabian-Robert Stöter DiffM 276 126 0 21 Jun 2018