Papers
4,025 papers found
Graph Spectral Embedding using the Geodesic Betweenness Centrality
Shay Deutsch, Stefano Soatto
Group Distributionally Robust Reinforcement Learning with Hierarchical Latent Variables
Mengdi Xu, Peide Huang, Yaru Niu et al.
Heavy Sets with Applications to Interpretable Machine Learning Diagnostics
Dmitry Malioutov, Sanjeeb Dash, Dennis Wei
Hedging against Complexity: Distributionally Robust Optimization with Parametric Approximation
Garud Iyengar, Henry Lam, Tianyu Wang
HeteRSGD: Tackling Heterogeneous Sampling Costs via Optimal Reweighted Stochastic Gradient Descent
Ziang Chen, Jianfeng Lu, Huajie Qian et al.
Hierarchical-Hyperplane Kernels for Actively Learning Gaussian Process Models of Nonstationary Systems
Matthias Bitzer, Mona Meister, Christoph Zimmer
High-Dimensional Private Empirical Risk Minimization by Greedy Coordinate Descent
Paul Mangold, Aurélien Bellet, Joseph Salmon et al.
High Probability Bounds for Stochastic Continuous Submodular Maximization
Evan Becker, Jingdong Gao, Ted Zadouri et al.
How Does Pseudo-Labeling Affect the Generalization Error of the Semi-Supervised Gibbs Algorithm?
Haiyun He, Gholamali Aminian, Yuheng Bu et al.
Huber-robust confidence sequences
Hongjian Wang, Aaditya Ramdas
Ideal Abstractions for Decision-Focused Learning
Michael Poli, Stefano Massaroli, Stefano Ermon et al.
Identification of Blackwell Optimal Policies for Deterministic MDPs
Victor Boone, Bruno Gaujal
Implications of sparsity and high triangle density for graph representation learning
Hannah Sansford, Alexander Modell, Nick Whiteley et al.
Implicit Graphon Neural Representation
Xinyue Xia, Gal Mishne, Yusu Wang
Improved Approximation for Fair Correlation Clustering
Sara Ahmadian, Maryam Negahbani
Improved Bound on Generalization Error of Compressed KNN Estimator
Hang Zhang, Ping Li
Improved Generalization Bound and Learning of Sparsity Patterns for Data-Driven Low-Rank Approximation
Shinsaku Sakaue, Taihei Oki
Improved Rate of First Order Algorithms for Entropic Optimal Transport
Yiling Luo, Yiling Xie, Xiaoming Huo
Improved Representation Learning Through Tensorized Autoencoders
Pascal Esser, Satyaki Mukherjee, Mahalakshmi Sabanayagam et al.
Improved Sample Complexity Bounds for Distributionally Robust Reinforcement Learning
Zaiyan Xu, Kishan Panaganti, Dileep Kalathil
Improving Adaptive Conformal Prediction Using Self-Supervised Learning
Nabeel Seedat, Alan Jeffares, Fergus Imrie et al.
Improving Adversarial Robustness via Joint Classification and Multiple Explicit Detection Classes
Sina Baharlouei, Fatemeh Sheikholeslami, Meisam Razaviyayn et al.
Improving Dual-Encoder Training through Dynamic Indexes for Negative Mining
Nicholas Monath, Manzil Zaheer, Kelsey Allen et al.
Incentive-aware Contextual Pricing with Non-parametric Market Noise
Negin Golrezaei, Patrick Jaillet, Jason Cheuk Nam Liang