:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Aliakbarpour, Maryam, Bairaktari, Konstantina, Brown, Gavin, Smith, Adam, Srebro, Nathan, Ullman, Jonathan
Format:	Preprint
Published:	2023
Subjects:	Machine Learning Data Structures and Algorithms
Online Access:	https://arxiv.org/abs/2312.13978
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Sample-efficient Multiclass Calibration under $\ell_{p}$ Error
by: Bairaktari, Konstantina, et al.
Published: (2025)

Privacy in Metalearning and Multitask Learning: Modeling and Separations
by: Aliakbarpour, Maryam, et al.
Published: (2024)

Optimal Prediction-Augmented Algorithms for Testing Independence of Distributions
by: Aliakbarpour, Maryam, et al.
Published: (2026)

Optimal Algorithms for Augmented Testing of Discrete Distributions
by: Aliakbarpour, Maryam, et al.
Published: (2024)

On the Complexity of Learning Sparse Functions with Statistical and Gradient Queries
by: Joshi, Nirmit, et al.
Published: (2024)

How fast can you find a good hypothesis?
by: Aamand, Anders, et al.
Published: (2025)

On the Structure of Replicable Hypothesis Testers
by: Aamand, Anders, et al.
Published: (2025)

Better Private Distribution Testing by Leveraging Unverified Auxiliary Data
by: Aliakbarpour, Maryam, et al.
Published: (2025)

High-Probability Bounds For Heterogeneous Local Differential Privacy
by: Aliakbarpour, Maryam, et al.
Published: (2025)

Nearly-Linear Time Private Hypothesis Selection with the Optimal Approximation Factor
by: Aliakbarpour, Maryam, et al.
Published: (2025)

Smooth Lower Bounds for Differentially Private Algorithms via Padding-and-Permuting Fingerprinting Codes
by: Peter, Naty, et al.
Published: (2023)

A Bias-Accuracy-Privacy Trilemma for Statistical Estimation
by: Kamath, Gautam, et al.
Published: (2023)

Private Mean Estimation with Person-Level Differential Privacy
by: Agarwal, Sushant, et al.
Published: (2024)

Learning Small Decision Trees with Few Outliers: A Parameterized Perspective
by: Gahlawat, Harmender, et al.
Published: (2025)

Sample-Adaptivity Tradeoff in On-Demand Sampling
by: Haghtalab, Nika, et al.
Published: (2025)

CoinPress: Practical Private Mean and Covariance Estimation
by: Biswas, Sourav, et al.
Published: (2020)

Inner Product Aware Quantization: Provably Fast, Accurate, and Adaptive Algorithms
by: White, Nathan, et al.
Published: (2026)

Online Combinatorial Allocations and Auctions with Few Samples
by: Dütting, Paul, et al.
Published: (2024)

Online Matrix Factorization, Online Private Query Release, and Online Discrepancy Minimization
by: Nikolov, Aleksandar, et al.
Published: (2026)

Fit Like You Sample: Sample-Efficient Generalized Score Matching from Fast Mixing Diffusions
by: Qin, Yilong, et al.
Published: (2023)

Thompson Sampling Itself is Differentially Private
by: Ou, Tingting, et al.
Published: (2024)

Efficient Sample-optimal Learning of Gaussian Tree Models via Sample-optimal Testing of Gaussian Mutual Information
by: Gayen, Sutanu, et al.
Published: (2024)

Sharper Bounds for $\ell_p$ Sensitivity Sampling
by: Woodruff, David P., et al.
Published: (2023)

Finite Sample Bounds for Learning with Score Matching
by: Smedira, Devin, et al.
Published: (2026)

Optimal Dimension-Free Sampling for Regularized Classification
by: Alishahi, Meysam, et al.
Published: (2026)

Distribution Learning Meets Graph Structure Sampling
by: Bhattacharyya, Arnab, et al.
Published: (2024)

Learning Juntas under Markov Random Fields
by: Chandrasekaran, Gautam, et al.
Published: (2025)

Structure-Aware Spectral Sparsification via Uniform Edge Sampling
by: He, Kaiwen, et al.
Published: (2025)

Ridge Leverage Score Sampling for $\ell_p$ Subspace Approximation
by: Woodruff, David P., et al.
Published: (2024)

Taming Imperfect Process Verifiers: A Sampling Perspective on Backtracking
by: Rohatgi, Dhruv, et al.
Published: (2025)

A Provably Accurate Randomized Sampling Algorithm for Logistic Regression
by: Chowdhury, Agniva, et al.
Published: (2024)

Sample-Efficient Optimization over Generative Priors via Coarse Learnability
by: Awasthi, Pranjal, et al.
Published: (2025)

Online Conversion with Switching Costs: Robust and Learning-Augmented Algorithms
by: Lechowicz, Adam, et al.
Published: (2023)

Chasing Convex Functions with Long-term Constraints
by: Lechowicz, Adam, et al.
Published: (2024)

Connectivity Oracles for Predictable Vertex Failures
by: Hu, Bingbing, et al.
Published: (2023)

Signal-Aware Workload Shifting Algorithms with Uncertainty-Quantified Predictors
by: Johnson, Ezra, et al.
Published: (2025)

A Single-Sample Polylogarithmic Regret Bound for Nonstationary Online Linear Programming
by: Xu, Haoran, et al.
Published: (2026)

Sample-and-Search: An Effective Algorithm for Learning-Augmented k-Median Clustering in High dimensions
by: Cheng, Kangke, et al.
Published: (2026)

Data-Efficient Learning via Clustering-Based Sensitivity Sampling: Foundation Models and Beyond
by: Axiotis, Kyriakos, et al.
Published: (2024)

A New Rejection Sampling Approach to $k$-$\mathtt{means}$++ With Improved Trade-Offs
by: Shah, Poojan, et al.
Published: (2025)