Papers
4,122 papers found
Topological Node2vec: Enhanced Graph Embedding via Persistent Homology
Yasuaki Hiraoka, Yusuke Imoto, Théo Lacombe et al.
TopoX: A Suite of Python Packages for Machine Learning on Topological Domains
Mustafa Hajij, Mathilde Papillon, Florian Frantzen et al.
Towards Explainable Evaluation Metrics for Machine Translation
Christoph Leiter, Piyawat Lertvittayakumjorn, Marina Fomicheva et al.
Towards Optimal Sobolev Norm Rates for the Vector-Valued Regularized Least-Squares Algorithm
Zhu Li, Dimitri Meunier, Mattes Mollenhauer et al.
Towards Unbiased Exploration in Partial Label Learning
Zsolt Zombori, Agapi Rissaki, Kristóf Szabó et al.
Trained Transformers Learn Linear Models In-Context
Ruiqi Zhang, Spencer Frei, Peter L. Bartlett
Training Integrable Parameterizations of Deep Neural Networks in the Infinite-Width Limit
Karl Hajjar, Lénaïc Chizat, Christophe Giraud
Transfer learning for tensor Gaussian graphical models
Mingyang Ren, Yaoming Zhen, Junhui Wang
Transfer Learning with Uncertainty Quantification: Random Effect Calibration of Source to Target (RECaST)
Jimmy Hickey, Jonathan P. Williams, Emily C. Hector
Transport-based Counterfactual Models
Lucas De Lara, Alberto González-Sanz, Nicholas Asher et al.
Triple Component Matrix Factorization: Untangling Global, Local, and Noisy Components
Naichen Shi, Salar Fattahi, Raed Al Kontar
Two is Better Than One: Regularized Shrinkage of Large Minimum Variance Portfolios
Taras Bodnar, Nestor Parolya, Erik Thorsen
Uncertainty Quantification of MLE for Entity Ranking with Covariates
Jianqing Fan, Jikai Hou, Mengxin Yu
Understanding Entropic Regularization in GANs
Daria Reshetova, Yikun Bai, Xiugang Wu et al.
Unified Binary and Multiclass Margin-Based Classification
Yutong Wang, Clayton Scott
Uniform Generalization Bounds on Data-Dependent Hypothesis Sets via PAC-Bayesian Theory on Random Sets
Benjamin Dupuis, Paul Viallard, George Deligiannidis et al.
Unlabeled Principal Component Analysis and Matrix Completion
Yunzhen Yao, Liangzu Peng, Manolis C. Tsakiris
Unsupervised Anomaly Detection Algorithms on Real-world Data: How Many Do We Need?
Roel Bouman, Zaharah Bukhsh, Tom Heskes
Unsupervised Tree Boosting for Learning Probability Distributions
Naoki Awaya, Li Ma
Value-Distributional Model-Based Reinforcement Learning
Carlos E. Luis, Alessandro G. Bottero, Julia Vinogradska et al.
Variance estimation in graphs with the fused lasso
Oscar Hernan Madrid Padilla
Variational Estimators of the Degree-corrected Latent Block Model for Bipartite Networks
Yunpeng Zhao, Ning Hao, Ji Zhu
Variation Spaces for Multi-Output Neural Networks: Insights on Multi-Task Learning and Network Compression
Joseph Shenouda, Rahul Parhi, Kangwook Lee et al.
Virtual-Event-Based Posterior Sampling and Inference for Neyman-Scott Processes
Chengkuan Hong, Christian R. Shelton, Jun Zhu
Volterra Neural Networks (VNNs)
Siddharth Roheda, Hamid Krim, Bo Jiang