Papers
4,122 papers found
Generalization and Stability of Interpolating Neural Networks with Minimal Width
Hossein Taheri, Christos Thrampoulidis
Generalization on the Unseen, Logic Reasoning and Degree Curriculum
Emmanuel Abbe, Samy Bengio, Aryo Lotfi et al.
Generalized Independent Noise Condition for Estimating Causal Structure with Latent Variables
Feng Xie, Biwei Huang, Zhengming Chen et al.
Generative Adversarial Ranking Nets
Yinghua Yao, Yuangang Pan, Jing Li et al.
Geometric Learning with Positively Decomposable Kernels
Nathael Da Costa, Cyrus Mostajeran, Juan-Pablo Ortega et al.
GGD: Grafting Gradient Descent
Yanjing Feng, Yongdao Zhou
Goal-Space Planning with Subgoal Models
Chunlok Lo, Kevin Roice, Parham Mohammad Panahi et al.
Gradient-free optimization of highly smooth functions: improved analysis and a new algorithm
Arya Akhavan, Evgenii Chzhen, Massimiliano Pontil et al.
Gradual Domain Adaptation: Theory and Algorithms
Yifei He, Haoxiang Wang, Bo Li et al.
Granger Causal Inference in Multivariate Hawkes Processes by Minimum Message Length
Katerina Hlaváčková-Schindler, Anna Melnykova, Irene Tubikanec
Graphical Dirichlet Process for Clustering Non-Exchangeable Grouped Data
Arhit Chakrabarti, Yang Ni, Ellen Ruth A. Morris et al.
Grokking phase transitions in learning local rules with gradient descent
Bojan Žunkovič, Enej Ilievski
Guaranteed Nonconvex Factorization Approach for Tensor Train Recovery
Zhen Qin, Michael B. Wakin, Zhihui Zhu
Hamiltonian Monte Carlo for efficient Gaussian sampling: long and random steps
Simon Apers, Sander Gribling, Dániel Szilágyi
Heterogeneity-aware Clustered Distributed Learning for Multi-source Data Analysis
Yuanxing Chen, Qingzhao Zhang, Shuangge Ma et al.
Heterogeneous-Agent Reinforcement Learning
Yifan Zhong, Jakub Grudzien Kuba, Xidong Feng et al.
High Probability and Risk-Averse Guarantees for a Stochastic Accelerated Primal-Dual Method
Yassine Laguel, Necdet Serhat Aybat, Mert Gürbüzbalaban
High Probability Convergence Bounds for Non-convex Stochastic Gradient Descent with Sub-Weibull Noise
Liam Madden, Emiliano Dall'Anese, Stephen Becker
Homeomorphic Projection to Ensure Neural-Network Solution Feasibility for Constrained Optimization
Enming Liang, Minghua Chen, Steven H. Low
How Two-Layer Neural Networks Learn, One (Giant) Step at a Time
Yatin Dandi, Florent Krzakala, Bruno Loureiro et al.
Identifiability and Asymptotics in Learning Homogeneous Linear ODE Systems from Discrete Observations
Yuanyuan Wang, Wei Huang, Mingming Gong et al.
Identifying Causal Effects using Instrumental Time Series: Nuisance IV and Correcting for the Past
Nikolaj Thams, Rikke Søndergaard, Sebastian Weichwald et al.
Improved Random Features for Dot Product Kernels
Jonas Wacker, Motonobu Kanagawa, Maurizio Filippone
Improving Lipschitz-Constrained Neural Networks by Learning Activation Functions
Stanislas Ducotterd, Alexis Goujon, Pakshal Bohra et al.