Papers
4,122 papers found
Individual-centered Partial Information in Social Networks
Xiao Han, Y. X. Rachel Wang, Qing Yang et al.
Infeasible Deterministic, Stochastic, and Variance-Reduction Algorithms for Optimization under Orthogonality Constraints
Pierre Ablin, Simon Vary, Bin Gao et al.
Inference on High-dimensional Single-index Models with Streaming Data
Dongxiao Han, Jinhan Xie, Jin Liu et al.
Infinite-Dimensional Diffusion Models
Jakiw Pidstrigach, Youssef Marzouk, Sebastian Reich et al.
Information Capacity Regret Bounds for Bandits with Mediator Feedback
Khaled Eldowa, Nicolò Cesa-Bianchi, Alberto Maria Metelli et al.
Information Processing Equalities and the Information–Risk Bridge
Robert C. Williamson, Zac Cranko
Instrumental Variable Value Iteration for Causal Offline Reinforcement Learning
Luofeng Liao, Zuyue Fu, Zhuoran Yang et al.
Interpretable algorithmic fairness in structured and unstructured data
Hari Bandi, Dimitris Bertsimas, Thodoris Koukouvinos et al.
Invariant and Equivariant Reynolds Networks
Akiyoshi Sannai, Makoto Kawano, Wataru Kumagai
Invariant Physics-Informed Neural Networks for Ordinary Differential Equations
Shivam Arora, Alex Bihlo, Francis Valiquette
Iterate Averaging in the Quest for Best Test Error
Diego Granziol, Nicholas P. Baskerville, Xingchen Wan et al.
Just Wing It: Near-Optimal Estimation of Missing Mass in a Markovian Sequence
Ashwin Pananjady, Vidya Muthukumar, Andrew Thangaraj
KerasCV and KerasNLP: Multi-framework Models
Matthew Watson, Divyashree Shivakumar Sreepathihalli, François Chollet et al.
Kernel Thinning
Raaz Dwivedi, Lester Mackey
Label Alignment Regularization for Distribution Shift
Ehsan Imani, Guojun Zhang, Runjia Li et al.
Label Noise Robustness of Conformal Prediction
Bat-Sheva Einbinder, Shai Feldman, Stephen Bates et al.
Law of Large Numbers and Central Limit Theorem for Wide Two-layer Neural Networks: The Mini-Batch and Noisy Case
Arnaud Descours, Arnaud Guillin, Manon Michel et al.
Learnability of Linear Port-Hamiltonian Systems
Juan-Pablo Ortega, Daiying Yin
Learning and scoring Gaussian latent variable causal models with unknown additive interventions
Armeen Taeb, Juan L. Gamella, Christina Heinze-Deml et al.
Learning Discretized Neural Networks under Ricci Flow
Jun Chen, Hanwen Chen, Mengmeng Wang et al.
Learning Dynamic Mechanisms in Unknown Environments: A Reinforcement Learning Approach
Shuang Qiu, Boxiang Lyu, Qinglin Meng et al.
Learning from many trajectories
Stephen Tu, Roy Frostig, Mahdi Soltanolkotabi
Learning Gaussian DAGs from Network Data
Hangjian Li, Oscar Hernan Madrid Padilla, Qing Zhou
Learning Non-Gaussian Graphical Models via Hessian Scores and Triangular Transport
Ricardo Baptista, Rebecca Morrison, Olivier Zahm et al.