Papers
4,122 papers found
Variance-Aware Estimation of Kernel Mean Embedding
Geoffrey Wolfer, Pierre Alquier
Variational Inference for Uncertainty Quantification: an Analysis of Trade-offs
Charles C. Margossian, Loucas Pillaud-Vivien, Lawrence K. Saul
Wasserstein Convergence Guarantees for a General Class of Score-Based Generative Models
Xuefeng Gao, Hoang M. Nguyen, Lingjiong Zhu
Wasserstein F-tests for Frechet regression on Bures-Wasserstein manifolds
Haoshu Xu, Hongzhe Li
WEFE: A Python Library for Measuring and Mitigating Bias in Word Embeddings
Pablo Badilla, Felipe Bravo-Marquez, María José Zambrano et al.
"What is Different Between These Datasets?" A Framework for Explaining Data Distribution Shifts
Varun Babbar*, Zhicheng Guo*, Cynthia Rudin
Zono-Conformal Prediction: Zonotope-Based Uncertainty Quantification for Regression and Classification Tasks
Laura Lützow, Michael Eichelbeck, Mykel J. Kochenderfer et al.
Accelerated Gradient Tracking over Time-varying Graphs for Decentralized Optimization
Huan Li, Zhouchen Lin
Accelerating Nuclear-norm Regularized Low-rank Matrix Optimization Through Burer-Monteiro Decomposition
Ching-pei Lee, Ling Liang, Tianyun Tang et al.
A Characterization of Multioutput Learnability
Vinod Raman, Unique Subedi, Ambuj Tewari
A Comparison of Continuous-Time Approximations to Stochastic Gradient Descent
Stefan Ankirchner, Stefan Perko
Adam-family Methods for Nonsmooth Optimization with Convergence Guarantees
Nachuan Xiao, Xiaoyin Hu, Xin Liu et al.
Adaptive Latent Feature Sharing for Piecewise Linear Dimensionality Reduction
Adam Farooq, Yordan P. Raykov, Petar Raykov et al.
Adaptivity and Non-stationarity: Problem-dependent Dynamic Regret for Online Convex Optimization
Peng Zhao, Yu-Jie Zhang, Lijun Zhang et al.
A Data-Adaptive RKHS Prior for Bayesian Learning of Kernels in Operators
Neil K. Chada, Quanjun Lang, Fei Lu et al.
Additive smoothing error in backward variational inference for general state-space models
Mathis Chagneux, Elisabeth Gassiat, Pierre Gloaguen et al.
Adjusted Wasserstein Distributionally Robust Estimator in Statistical Learning
Yiling Xie, Xiaoming Huo
aeon: a Python Toolkit for Learning from Time Series
Matthew Middlehurst, Ali Ismail-Fawaz, Antoine Guillaume et al.
Aequitas Flow: Streamlining Fair ML Experimentation
Sérgio Jesus, Pedro Saleiro, Inês Oliveira e Silva et al.
A flexible empirical Bayes approach to multiple linear regression and connections with penalized regression
Youngseok Kim, Wei Wang, Peter Carbonetto et al.
A Framework for Improving the Reliability of Black-box Variational Inference
Manushi Welandawe, Michael Riis Andersen, Aki Vehtari et al.
A General Framework for the Analysis of Kernel-based Tests
Tamara Fernández, Nicolás Rivera
A Kernel Test for Causal Association via Noise Contrastive Backdoor Adjustment
Robert Hu, Dino Sejdinovic, Robin J. Evans