Papers
Graph Convolution for Semi-Supervised Classification: Improved Linear Separability and Out-of-Distribution Generalization
Aseem Baranwal, Kimon Fountoulakis, Aukosh Jagannath
Graph Cuts Always Find a Global Optimum for Potts Models (With a Catch)
Hunter Lang, David Sontag, Aravindan Vijayaraghavan
GraphDF: A Discrete Flow Model for Molecular Graph Generation
Youzhi Luo, Keqiang Yan, Shuiwang Ji
Graph Mixture Density Networks
Federico Errica, Davide Bacciu, Alessio Micheli
Graph Neural Networks Inspired by Classical Iterative Algorithms
Yongyi Yang, Tang Liu, Yangkun Wang et al.
GraphNorm: A Principled Approach to Accelerating Graph Neural Network Training
Tianle Cai, Shengjie Luo, Keyulu Xu et al.
Grey-box Extraction of Natural Language Models
Santiago Zanella-Beguelin, Shruti Tople, Andrew Paverd et al.
Grid-Functioned Neural Networks
Javier Dehesa, Andrew Vidler, Julian Padget et al.
Grounding Language to Entities and Dynamics for Generalization in Reinforcement Learning
Austin W. Hanjie, Victor Y Zhong, Karthik Narasimhan
Group Fisher Pruning for Practical Network Compression
Liyang Liu, Shilong Zhang, Zhanghui Kuang et al.
Group-Sparse Matrix Factorization for Transfer Learning of Word Embeddings
Kan Xu, Xuanyi Zhao, Hamsa Bastani et al.
Guarantees for Tuning the Step Size using a Learning-to-Learn Approach
Xiang Wang, Shuai Yuan, Chenwei Wu et al.
Guided Exploration with Proximal Policy Optimization using a Single Demonstration
Gabriele Libardi, Gianni De Fabritiis, Sebastian Dittert
HardCoRe-NAS: Hard Constrained diffeRentiable Neural Architecture Search
Niv Nayman, Yonathan Aflalo, Asaf Noy et al.
HAWQ-V3: Dyadic Neural Network Quantization
Zhewei Yao, Zhen Dong, Zhangcheng Zheng et al.
Heterogeneity for the Win: One-Shot Federated Clustering
Don Kurian Dennis, Tian Li, Virginia Smith
Heterogeneous Risk Minimization
Jiashuo Liu, Zheyuan Hu, Peng Cui et al.
"Hey, that’s not an ODE": Faster ODE Adjoints via Seminorms
Patrick Kidger, Ricky T. Q. Chen, Terry J Lyons
Hierarchical Agglomerative Graph Clustering in Nearly-Linear Time
Laxman Dhulipala, David Eisenstat, Jakub Łącki et al.
Hierarchical Clustering of Data Streams: Scalable Algorithms and Approximation Guarantees
Anand Rajagopalan, Fabio Vitale, Danny Vainstein et al.
Hierarchical VAEs Know What They Don’t Know
Jakob D. Havtorn, Jes Frellsen, Søren Hauberg et al.
High Confidence Generalization for Reinforcement Learning
James Kostas, Yash Chandak, Scott M Jordan et al.
High-dimensional Experimental Design and Kernel Bandits
Romain Camilleri, Kevin Jamieson, Julian Katz-Samuels
High-Dimensional Gaussian Process Inference with Derivatives
Filip de Roos, Alexandra Gessner, Philipp Hennig