Papers
Generalized and Scalable Optimal Sparse Decision Trees
Jimmy Lin, Chudi Zhong, Diane Hu et al.
Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data
Marc Finzi, Samuel Stanton, Pavel Izmailov et al.
Generating Programmatic Referring Expressions via Program Synthesis
Jiani Huang, Calvin Smith, Osbert Bastani et al.
Generative Adversarial Imitation Learning with Neural Network Parameterization: Global Optimality and Convergence Rate
Yufeng Zhang, Qi Cai, Zhuoran Yang et al.
Generative Flows with Matrix Exponential
Changyi Xiao, Ligang Liu
Generative Pretraining From Pixels
Mark Chen, Alec Radford, Rewon Child et al.
Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data
Felipe Petroski Such, Aditya Rawal, Joel Lehman et al.
Global Concavity and Optimization in a Class of Dynamic Discrete Choice Models
Yiding Feng, Ekaterina Khmelnitskaya, Denis Nekipelov
GNN-FiLM: Graph Neural Networks with Feature-wise Linear Modulation
Marc Brockschmidt
Goal-Aware Prediction: Learning to Model What Matters
Suraj Nair, Silvio Savarese, Chelsea Finn
Goodness-of-Fit Tests for Inhomogeneous Random Graphs
Soham Dan, Bhaswar B. Bhattacharya
Good Subnetworks Provably Exist: Pruning via Greedy Forward Selection
Mao Ye, Chengyue Gong, Lizhen Nie et al.
Go Wide, Then Narrow: Efficient Training of Deep Thin Networks
Denny Zhou, Mao Ye, Chen Chen et al.
GradientDICE: Rethinking Generalized Offline Estimation of Stationary Values
Shangtong Zhang, Bo Liu, Shimon Whiteson
Gradient-free Online Learning in Continuous Games with Delayed Rewards
Amélie Héliou, Panayotis Mertikopoulos, Zhengyuan Zhou
Gradient Temporal-Difference Learning with Regularized Corrections
Sina Ghiassian, Andrew Patterson, Shivam Garg et al.
Graph-based Nearest Neighbor Search: From Practice to Theory
Liudmila Prokhorenkova, Aleksandr Shekhovtsov
Graph-based, Self-Supervised Program Repair from Diagnostic Feedback
Michihiro Yasunaga, Percy Liang
Graph Convolutional Network for Recommendation with Low-pass Collaborative Filters
Wenhui Yu, Zheng Qin
Graph Filtration Learning
Christoph Hofer, Florian Graf, Bastian Rieck et al.
Graph Homomorphism Convolution
Hoang Nguyen, Takanori Maehara
Graphical Models Meet Bandits: A Variational Thompson Sampling Approach
Tong Yu, Branislav Kveton, Zheng Wen et al.
Graph Optimal Transport for Cross-Domain Alignment
Liqun Chen, Zhe Gan, Yu Cheng et al.
GraphOpt: Learning Optimization Models of Graph Formation
Rakshit Trivedi, Jiachen Yang, Hongyuan Zha
Graph Random Neural Features for Distance-Preserving Graph Representations
Daniele Zambon, Cesare Alippi, Lorenzo Livi