{"total":41,"rows":[{"id":"2303.01774","title":"Bayesian Optimization over High-Dimensional Combinatorial Spaces via Dictionary-based Embeddings","created":"2023-03-03","cited_by_count":24,"area":null},{"id":"2202.12793","title":"Towards Optimal Lower Bounds for k-median and k-means Coresets","created":"2022-02-25","cited_by_count":23,"area":null},{"id":"2302.00248","title":"A Nearly-Optimal Bound for Fast Regression with $\\ell_\\infty$ Guarantee","created":"2023-02-01","cited_by_count":14,"area":null},{"id":"2306.08013","title":"TopP&R: Robust Support Estimation Approach for Evaluating Fidelity and Diversity in Generative Models","created":"2023-06-13","cited_by_count":14,"area":null},{"id":"2110.08991","title":"Dimensionality Reduction for Wasserstein Barycenter","created":"2021-10-18","cited_by_count":13,"area":null},{"id":"2211.09403","title":"Learning Mixtures of Markov Chains and MDPs","created":"2022-11-17","cited_by_count":12,"area":null},{"id":"2103.00564","title":"An Introduction to Johnson-Lindenstrauss Transforms","created":"2021-02-28","cited_by_count":11,"area":null},{"id":"2108.04172","title":"Johnson-Lindenstrauss Lemma, Linear and Nonlinear Random Projections, Random Fourier Features, and Random Kitchen Sinks: Tutorial and Survey","created":"2021-08-09","cited_by_count":8,"area":null},{"id":"2204.10376","title":"Differentially Private Learning with Margin Guarantees","created":"2022-04-21","cited_by_count":7,"area":null},{"id":"2204.01800","title":"The Fast Johnson-Lindenstrauss Transform is Even Faster","created":"2022-04-04","cited_by_count":5,"area":null},{"id":"2306.07040","title":"Nonlinear SVD with Asymmetric Kernels: feature learning and asymmetric Nystr\\\"om method","created":"2023-06-12","cited_by_count":5,"area":null},{"id":"2202.02031","title":"Complex-to-Real Sketches for Tensor Products with Applications to the Polynomial Kernel","created":"2022-02-04","cited_by_count":4,"area":null},{"id":"2305.03710","title":"Data Encoding For Healthcare Data Democratisation and Information Leakage Prevention","created":"2023-05-05","cited_by_count":3,"area":null},{"id":"2403.10978","title":"Lambda: Learning Matchable Prior For Entity Alignment with Unlabeled Dangling Cases","created":"2024-03-16","cited_by_count":3,"area":null},{"id":"2406.08748","title":"Learning in Feature Spaces via Coupled Covariances: Asymmetric Kernel SVD and Nystr\\\"om method","created":"2024-06-13","cited_by_count":3,"area":null},{"id":"2210.00244","title":"On The Relative Error of Random Fourier Features for Preserving Kernel Distance","created":"2022-10-01","cited_by_count":2,"area":null},{"id":"2305.18755","title":"Dimensionality Reduction for General KDE Mode Finding","created":"2023-05-30","cited_by_count":2,"area":null},{"id":"2312.06887","title":"Understanding and Leveraging the Learning Phases of Neural Networks","created":"2023-12-11","cited_by_count":2,"area":null},{"id":"2106.01092","title":"Statistical optimality conditions for compressive ensembles","created":"2021-06-02","cited_by_count":1,"area":null},{"id":"2206.03376","title":"On Outer Bi-Lipschitz Extensions of Linear Johnson-Lindenstrauss Embeddings of Low-Dimensional Submanifolds of $\\mathbb{R}^N$","created":"2022-06-07","cited_by_count":1,"area":null},{"id":"2302.06165","title":"Sparse Dimensionality Reduction Revisited","created":"2023-02-13","cited_by_count":1,"area":null},{"id":"2308.13960","title":"Sparse Models for Machine Learning","created":"2023-08-26","cited_by_count":1,"area":null},{"id":"2412.00696","title":"Intermediate Outputs Are More Sensitive Than You Think","created":"2024-12-01","cited_by_count":1,"area":null},{"id":"2412.07242","title":"Optimization Can Learn Johnson Lindenstrauss Embeddings","created":"2024-12-10","cited_by_count":1,"area":null},{"id":"2605.03346","title":"Provable Accuracy Collapse in Embedding-Based Representations under Dimensionality Mismatch","created":"2026-05-05","cited_by_count":1,"area":null},{"id":"2606.09653","title":"A Unifying Framework for Concept-Based Representational Similarity","created":"2026-06-08","cited_by_count":1,"area":null},{"id":"2107.06626","title":"Optimality of the Johnson-Lindenstrauss Dimensionality Reduction for Practical Measures","created":"2021-07-14","cited_by_count":0,"area":null},{"id":"2202.08299","title":"The learning phases in NN: From Fitting the Majority to Fitting a Few","created":"2022-02-16","cited_by_count":0,"area":null},{"id":"2209.01006","title":"Johnson-Lindenstrauss embeddings for noisy vectors -- taking advantage of the noise","created":"2022-09-01","cited_by_count":0,"area":null},{"id":"2210.04807","title":"Efficient NTK using Dimensionality Reduction","created":"2022-10-10","cited_by_count":0,"area":null},{"id":"2307.11371","title":"Random Separating Hyperplane Theorem and Learning Polytopes","created":"2023-07-21","cited_by_count":0,"area":null},{"id":"2310.19068","title":"Sketching Algorithms for Sparse Dictionary Learning: PTAS and Turnstile Streaming","created":"2023-10-29","cited_by_count":0,"area":null},{"id":"2311.05075","title":"Mental Health Diagnosis in the Digital Age: Harnessing Sentiment Analysis on Social Media Platforms upon Ultra-Sparse Feature Content","created":"2023-11-09","cited_by_count":0,"area":null},{"id":"2502.19865","title":"Beyond Worst-Case Dimensionality Reduction for Sparse Vectors","created":"2025-02-27","cited_by_count":0,"area":null},{"id":"2504.16362","title":"Almost Right: Making First-Layer Kernels Nearly Orthogonal Improves Model Generalization","created":"2025-04-23","cited_by_count":0,"area":null},{"id":"2505.24737","title":"Adapting to Linear Separable Subsets with Large-Margin in Differentially Private Learning","created":"2025-05-30","cited_by_count":0,"area":null},{"id":"2509.09485","title":"Balancing Utility and Privacy: Dynamically Private SGD with Random Projection","created":"2025-09-11","cited_by_count":0,"area":null},{"id":"2509.22553","title":"Linear Causal Representation Learning by Topological Ordering, Pruning, and Disentanglement","created":"2025-09-26","cited_by_count":0,"area":null},{"id":"2607.11938","title":"Mathematics of Data Science","created":"2026-07-11","cited_by_count":0,"area":null},{"id":"2608.26552","title":"Hadamard Flattening and Gaussian Pooling Sketch for Least Squares with Coordinate-wise Guarantee","created":"2026-08-27","cited_by_count":0,"area":null},{"id":"2609.02155","title":"Exact Limits of Random Projections for Preserving Geometry: Distance Recovery, Nearest-Neighbor Rankings, and Covariance Shape in Gaussian Models","created":"2026-09-02","cited_by_count":0,"area":null}],"next_cursor":null}