Papers
4,122 papers found
On the Approximation of Kernel functions
Paul Dommel, Alois Pichler
On the Convergence of Projected Policy Gradient for Any Constant Step Sizes
Jiacai Liu, Wenye Li, Dachao Lin et al.
On the Natural Gradient of the Evidence Lower Bound
Nihat Ay, Jesse van Oostrum, Adwait Datar
On the O(sqrt(d)/T^(1/4)) Convergence Rate of RMSProp and Its Momentum Extension Measured by l_1 Norm
Huan Li, Yiming Dong, Zhouchen Lin
On the Representation of Pairwise Causal Background Knowledge and Its Applications in Causal Inference
Zhuangyan Fang, Ruiqi Zhao, Yue Liu et al.
On the Robustness of Kernel Goodness-of-Fit Tests
Xing Liu, François-Xavier Briol
On the Statistical Properties of Generative Adversarial Models for Low Intrinsic Data Dimension
Saptarshi Chakraborty, Peter L. Bartlett
On the Utility of Equal Batch Sizes for Inference in Stochastic Gradient Descent
Rahul Singh, Abhinek Shukla, Dootika Vats
Ontolearn---A Framework for Large-scale OWL Class Expression Learning in Python
Caglar Demir, Alkid Baci, N'Dah Jean Kouagou et al.
Operator Learning for Hyperbolic PDEs
Christopher Wang, Alex Townsend
Optimal and Efficient Algorithms for Decentralized Online Convex Optimization
Yuanyu Wan, Tong Wei, Bo Xue et al.
Optimal Complexity in Byzantine-Robust Distributed Stochastic Optimization with Data Heterogeneity
Qiankun Shi, Jie Peng, Kun Yuan et al.
Optimal Decentralized Composite Optimization for Strongly Convex Functions
Haishan Ye, Xiangyu Chang
Optimal Denoising in Score-Based Generative Models: The Role of Data Regularity
Eliot Beyler, Francis Bach
Optimal Experiment Design for Causal Effect Identification
Sina Akbari, Jalal Etesami, Negar Kiyavash
Optimal Functional Bilinear Regression with Two-dimensional Functional Covariates via Reproducing Kernel Hilbert Space
Dan Yang, Jianlong Shao, Haipeng Shen et al.
Optimal Rates of Kernel Ridge Regression under Source Condition in Large Dimensions
Haobo Zhang, Yicheng Li, Weihao Lu et al.
Optimal Sample Selection Through Uncertainty Estimation and Its Application in Deep Learning
Yong Lin, Chen Liu, Chenlu Ye et al.
Optimal subsampling for high-dimensional partially linear models via machine learning methods
Yujing Shao, Lei Wang, Heng Lian et al.
Optimization Over a Probability Simplex
James Chok, Geoffrey M. Vasil
Optimizing Data Collection for Machine Learning
Rafid Mahmood, James Lucas, Jose M. Alvarez et al.
Optimizing Return Distributions with Distributional Dynamic Programming
Bernardo Ávila Pires, Mark Rowland, Diana Borsa et al.
Orthogonal Bases for Equivariant Graph Learning with Provable k-WL Expressive Power
Jia He, Maggie Cheng
Outlier Robust and Sparse Estimation of Linear Regression Coefficients
Takeyuki Sasai, Hironori Fujisawa
PFLlib: A Beginner-Friendly and Comprehensive Personalized Federated Learning Library and Benchmark
Jianqing Zhang, Yang Liu, Yang Hua et al.