Papers
4,122 papers found
Pygmtools: A Python Graph Matching Toolkit
Runzhong Wang, Ziao Guo, Wenzheng Pan et al.
PyGOD: A Python Library for Graph Outlier Detection
Kay Liu, Yingtong Dou, Xueying Ding et al.
PyPop7: A Pure-Python Library for Population-Based Black-Box Optimization
Qiqi Duan, Guochen Zhou, Chang Shao et al.
QDax: A Library for Quality-Diversity and Population-based Algorithms with Hardware Acceleration
Felix Chalumeau, Bryan Lim, Raphaël Boige et al.
Random Forest Weighted Local Fréchet Regression with Random Objects
Rui Qiu, Zhou Yu, Ruoqing Zhu
Random measure priors in Bayesian recovery from sketches
Mario Beraha, Stefano Favaro, Matteo Sesia
Random Smoothing Regularization in Kernel Gradient Descent Learning
Liang Ding, Tianyang Hu, Jiahang Jiang et al.
Random Subgraph Detection Using Queries
Wasim Huleihel, Arya Mazumdar, Soumyabrata Pal
Rates of convergence for density estimation with generative adversarial networks
Nikita Puchkin, Sergey Samsonov, Denis Belomestny et al.
Recursive Estimation of Conditional Kernel Mean Embeddings
Ambrus Tamás, Balázs Csanád Csáji
Regimes of No Gain in Multi-class Active Learning
Gan Yuan, Yunfan Zhao, Samory Kpotufe
Regret Analysis of Bilateral Trade with a Smoothed Adversary
Nicolò Cesa-Bianchi, Tommaso Cesari, Roberto Colomboni et al.
Representation Learning via Manifold Flattening and Reconstruction
Michael Psenka, Druv Pai, Vishal Raman et al.
Resource-Efficient Neural Networks for Embedded Systems
Wolfgang Roth, Günther Schindler, Bernhard Klein et al.
Rethinking Discount Regularization: New Interpretations, Unintended Consequences, and Solutions for Regularization in Reinforcement Learning
Sarah Rathnam, Sonali Parbhoo, Siddharth Swaroop et al.
Revisiting RIP Guarantees for Sketching Operators on Mixture Models
Ayoub Belhadji, Rémi Gribonval
Risk Measures and Upper Probabilities: Coherence and Stratification
Christian Fröhlich, Robert C. Williamson
RLtools: A Fast, Portable Deep Reinforcement Learning Library for Continuous Control
Jonas Eschmann, Dario Albani, Giuseppe Loianno
Robust Black-Box Optimization for Stochastic Search and Episodic Reinforcement Learning
Maximilian Hüttenrauch, Gerhard Neumann
Robust Principal Component Analysis using Density Power Divergence
Subhrajyoty Roy, Ayanendranath Basu, Abhik Ghosh
Robust Spectral Clustering with Rank Statistics
Joshua Cape, Xianshi Yu, Jonquil Z. Liao
Sample Complexity of Neural Policy Mirror Descent for Policy Optimization on Low-Dimensional Manifolds
Zhenghao Xu, Xiang Ji, Minshuo Chen et al.
Sample Complexity of Variance-Reduced Distributionally Robust Q-Learning
Shengbo Wang, Nian Si, Jose Blanchet et al.
Sample-efficient Adversarial Imitation Learning
Dahuin Jung, Hyungyu Lee, Sungroh Yoon