Papers
Group Equivariant Fourier Neural Operators for Partial Differential Equations
Jacob Helwig, Xuan Zhang, Cong Fu et al.
GuardHFL: Privacy Guardian for Heterogeneous Federated Learning
Hanxiao Chen, Meng Hao, Hongwei Li et al.
Guiding Pretraining in Reinforcement Learning with Large Language Models
Yuqing Du, Olivia Watkins, Zihan Wang et al.
Half-Hop: A graph upsampling approach for slowing down message passing
Mehdi Azabou, Venkataramana Ganesh, Shantanu Thakoor et al.
Hardness of Independent Learning and Sparse Equilibrium Computation in Markov Games
Dylan J Foster, Noah Golowich, Sham M. Kakade
Hardware-Aware Compression with Random Operation Access Specific Tile (ROAST) Hashing
Aditya Desai, Keren Zhou, Anshumali Shrivastava
Harmonic Neural Networks
Atiyo Ghosh, Antonio Andrea Gentile, Mario Dagrada et al.
HarsanyiNet: Computing Accurate Shapley Values in a Single Forward Propagation
Lu Chen, Siyu Lou, Keyan Zhang et al.
HETAL: Efficient Privacy-preserving Transfer Learning with Homomorphic Encryption
Seewoo Lee, Garam Lee, Jung Woo Kim et al.
Hidden Symmetries of ReLU Networks
Elisenda Grigsby, Kathryn Lindsey, David Rolnick
Hiding Data Helps: On the Benefits of Masking for Sparse Coding
Muthu Chidambaram, Chenwei Wu, Yu Cheng et al.
Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles
Chaitanya Ryali, Yuan-Ting Hu, Daniel Bolya et al.
Hierarchical Diffusion for Offline Decision Making
Wenhao Li, Xiangfeng Wang, Bo Jin et al.
Hierarchical Grammar-Induced Geometry for Data-Efficient Molecular Property Prediction
Minghao Guo, Veronika Thost, Samuel W Song et al.
Hierarchical Imitation Learning with Vector Quantized Models
Kalle Kujanpää, Joni Pajarinen, Alexander Ilin
Hierarchical Neural Coding for Controllable CAD Model Generation
Xiang Xu, Pradeep Kumar Jayaraman, Joseph George Lambourne et al.
Hierarchical Programmatic Reinforcement Learning via Learning to Compose Programs
Guan-Ting Liu, En-Pei Hu, Pu-Jen Cheng et al.
Hierarchies of Reward Machines
Daniel Furelos-Blanco, Mark Law, Anders Jonsson et al.
High-dimensional Clustering onto Hamiltonian Cycle
Tianyi Huang, Shenghui Cheng, Stan Z. Li et al.
High-dimensional Location Estimation via Norm Concentration for Subgamma Vectors
Shivam Gupta, Jasper C.H. Lee, Eric Price
High Fidelity Image Counterfactuals with Probabilistic Causal Models
Fabio De Sousa Ribeiro, Tian Xia, Miguel Monteiro et al.
High-Probability Bounds for Stochastic Optimization and Variational Inequalities: the Case of Unbounded Variance
Abdurakhmon Sadiev, Marina Danilova, Eduard Gorbunov et al.
High Probability Convergence of Stochastic Gradient Methods
Zijian Liu, Ta Duy Nguyen, Thien Hang Nguyen et al.
Hindsight Learning for MDPs with Exogenous Inputs
Sean R. Sinclair, Felipe Vieira Frujeri, Ching-An Cheng et al.