Waiming Tai

CoRR, 2024

Breaking the curse of dimensionality in structured density estimation.

[BibT_eX]

[DOI]

Robert A. Vandermeulen

CoRR, 2024

Agnostic Active Learning of Single Index Models with Linear Sample Complexity.

[BibT_eX]

[DOI]

Proceedings of the Thirty Seventh Annual Conference on Learning Theory, June 30, 2024

Optimal estimation of Gaussian (poly)trees.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

Inconsistency of Cross-Validation for Structure Learning in Gaussian Graphical Models.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

On Mergable Coresets for Polytope Distance.

[BibT_eX]

[DOI]

CoRR, 2023

Learning Mixtures of Gaussians with Censored Data.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2023

Tight Bounds on the Hardness of Learning Simple Nonparametric Mixtures.

[BibT_eX]

[DOI]

Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

2022

A super-polynomial lower bound for learning nonparametric mixtures.

[BibT_eX]

[DOI]

CoRR, 2022

Optimal Coreset for Gaussian Kernel Density Estimation.

[BibT_eX]

[DOI]

Proceedings of the 38th International Symposium on Computational Geometry, 2022

Optimal estimation of Gaussian DAG models.

[BibT_eX]

[DOI]

Ming Gao

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Geometry of Kernel Density Estimation.

[BibT_eX]

Waiming Tai

PhD thesis, 2021

Finding an Approximate Mode of a Kernel Density Estimate.

[BibT_eX]

[DOI]

Proceedings of the 29th Annual European Symposium on Algorithms, 2021

2020

Near-Optimal Coresets of Kernel Density Estimates.

[BibT_eX]

[DOI]

Discret. Comput. Geom., 2020

New Nearly-Optimal Coreset for Kernel Density Estimation.

[BibT_eX]

[DOI]

CoRR, 2020

The GaussianSketch for Almost Relative Error Kernel Distance.

[BibT_eX]

[DOI]

Proceedings of the Approximation, 2020

2019

Finding the Mode of a Kernel Density Estimate.

[BibT_eX]

[DOI]

CoRR, 2019

Learning In Practice: Reasoning About Quantization.

[BibT_eX]

[DOI]

CoRR, 2019

Approximate Guarantees for Dictionary Learning.

[BibT_eX]

[DOI]

Aditya Bhaskara

Proceedings of the Conference on Learning Theory, 2019

2018

Relative Error RKHS Embeddings for Gaussian Kernels.

[BibT_eX]

[DOI]

CoRR, 2018

Improved Coresets for Kernel Density Estimates.

[BibT_eX]

[DOI]

Proceedings of the Twenty-Ninth Annual ACM-SIAM Symposium on Discrete Algorithms, 2018

2014

Tracking the Frequency Moments at All Times.

[BibT_eX]

[DOI]

Zengfeng Huang