{"total":19,"rows":[{"id":"2212.03714","title":"Reconstructing Training Data from Model Gradient, Provably","created":"2022-12-07","cited_by_count":27,"area":null},{"id":"2406.15575","title":"Sketch-GNN: Scalable Graph Neural Networks with Sublinear Training Complexity","created":"2024-06-21","cited_by_count":14,"area":null},{"id":"2406.11151","title":"Recent and Upcoming Developments in Randomized Numerical Linear Algebra for Machine Learning","created":"2024-06-17","cited_by_count":11,"area":null},{"id":"2306.00406","title":"Faster Robust Tensor Power Method for Arbitrary Order","created":"2023-06-01","cited_by_count":8,"area":null},{"id":"2105.00105","title":"Tensor Random Projection for Low Memory Dimension Reduction","created":"2021-04-30","cited_by_count":7,"area":null},{"id":"2110.07631","title":"More Efficient Sampling for Tensor Decomposition With Worst-Case Guarantees","created":"2021-10-14","cited_by_count":7,"area":null},{"id":"2305.11495","title":"Nonconvex Robust High-Order Tensor Completion Using Randomized Low-Rank Approximation","created":"2023-05-19","cited_by_count":7,"area":null},{"id":"2108.05767","title":"Probabilistic methods for approximate archetypal analysis","created":"2021-08-12","cited_by_count":4,"area":null},{"id":"2203.12798","title":"DPar2: Fast and Scalable PARAFAC2 Decomposition for Irregular Dense Tensors","created":"2022-03-24","cited_by_count":4,"area":null},{"id":"2505.08146","title":"Tensor Sketch: Fast and Scalable Polynomial Kernel Approximation","created":"2025-05-13","cited_by_count":4,"area":null},{"id":"2206.01509","title":"Canonical convolutional neural networks","created":"2022-06-03","cited_by_count":3,"area":null},{"id":"2109.08346","title":"Comfetch: Federated Learning of Large Networks on Constrained Clients via Sketching","created":"2021-09-17","cited_by_count":2,"area":null},{"id":"2108.10129","title":"Effective Streaming Low-tubal-rank Tensor Approximation via Frequent Directions","created":"2021-08-23","cited_by_count":1,"area":null},{"id":"2110.13859","title":"Defensive Tensorization","created":"2021-10-26","cited_by_count":1,"area":null},{"id":"2510.03678","title":"Towards Sampling Data Structures for Tensor Products in Turnstile Streams","created":"2025-10-04","cited_by_count":1,"area":null},{"id":"2106.13062","title":"Efficient Tensor Contraction via Fast Count Sketch","created":"2021-06-24","cited_by_count":0,"area":null},{"id":"2209.00372","title":"Sample Efficient Learning of Factored Embeddings of Tensor Fields","created":"2022-09-01","cited_by_count":0,"area":null},{"id":"2211.14828","title":"Towards Efficient and Accurate Approximation: Tensor Decomposition Based on Randomized Block Krylov Iteration","created":"2022-11-27","cited_by_count":0,"area":null},{"id":"2305.17884","title":"Combining Monte Carlo and Tensor-network Methods for Partial Differential Equations via Sketching","created":"2023-05-29","cited_by_count":0,"area":null}],"next_cursor":null}