Papers
Project and Forget: Solving Large-Scale Metric Constrained Problems
Rishi Sonthalia, Anna C. Gilbert
Projected Robust PCA with Application to Smooth Image Recovery
Long Feng, Junhui Wang
Projected Statistical Methods for Distributional Data on the Real Line with the Wasserstein Metric
Matteo Pegoraro, Mario Beraha
Projection-free Distributed Online Learning with Sublinear Communication Complexity
Yuanyu Wan, Guanghui Wang, Wei-Wei Tu et al.
Provable Tensor-Train Format Tensor Completion by Riemannian Optimization
Jian-Feng Cai, Jingyang Li, Dong Xia
Quantile regression with ReLU Networks: Estimators and minimax rates
Oscar Hernan Madrid Padilla, Wesley Tansey, Yanzhen Chen
Ranking and Tuning Pre-trained Models: A New Paradigm for Exploiting Model Hubs
Kaichao You, Yong Liu, Ziyang Zhang et al.
Recovering shared structure from multiple networks with unknown edge distributions
Keith Levin, Asad Lodhia, Elizaveta Levina
Recovery and Generalization in Over-Realized Dictionary Learning
Jeremias Sulam, Chong You, Zhihui Zhu
ReduNet: A White-box Deep Network from the Principle of Maximizing Rate Reduction
Kwan Ho Ryan Chan, Yaodong Yu, Chong You et al.
Regularized and Smooth Double Core Tensor Factorization for Heterogeneous Data
Davoud Ataee Tarzanagh, George Michailidis
Regularized K-means Through Hard-Thresholding
Jakob Raymaekers, Ruben H. Zamar
Representation Learning for Maximization of MI, Nonlinear ICA and Nonlinear Subspaces with Robust Density Ratio Estimation
Hiroaki Sasaki, Takashi Takenouchi
ReservoirComputing.jl: An Efficient and Modular Library for Reservoir Computing Models
Francesco Martinuzzi, Chris Rackauckas, Anas Abdelrehim et al.
Rethinking Nonlinear Instrumental Variable Models through Prediction Validity
Chunxiao Li, Cynthia Rudin, Tyler H. McCormick
Reverse-mode differentiation in arbitrary tensor network format: with application to supervised learning
Alex A. Gorodetsky, Cosmin Safta, John D. Jakeman
Riemannian Stochastic Proximal Gradient Methods for Nonsmooth Optimization over the Stiefel Manifold
Bokun Wang, Shiqian Ma, Lingzhou Xue
Robust and scalable manifold learning via landmark diffusion for long-term medical signal processing
Chao Shen, Yu-Ting Lin, Hau-Tieng Wu
Robust Distributed Accelerated Stochastic Gradient Methods for Multi-Agent Networks
Alireza Fallah, Mert Gürbüzbalaban, Asuman Ozdaglar et al.
Sampling Permutations for Shapley Value Estimation
Rory Mitchell, Joshua Cooper, Eibe Frank et al.
Scalable and Efficient Hypothesis Testing with Random Forests
Tim Coleman, Wei Peng, Lucas Mentch
Scalable Gaussian-process regression and variable selection using Vecchia approximations
Jian Cao, Joseph Guinness, Marc G. Genton et al.
Scaling and Scalability: Provable Nonconvex Low-Rank Tensor Estimation from Incomplete Measurements
Tian Tong, Cong Ma, Ashley Prater-Bennette et al.
Scaling Laws from the Data Manifold Dimension
Utkarsh Sharma, Jared Kaplan
Scaling-Translation-Equivariant Networks with Decomposed Convolutional Filters
Wei Zhu, Qiang Qiu, Robert Calderbank et al.