Insu Han

Nazreen Pallikkavaliyaveetil

CoRR, 2024

SubGen: Token Generation in Sublinear Time and Memory.

[BibT_eX]

[DOI]

CoRR, 2024

Cell2Sentence: Teaching Large Language Models the Language of Biology.

[BibT_eX]

[DOI]

Daniel LeVine

Syed Asad Rizvi

Sacha Lévy

Antonio Henrique de Oliveira Fonseca

Josue Ortega Caro

Amin Karbasi

Rahul Madhav Dhodapkar

David van Dijk

Proceedings of the Forty-first International Conference on Machine Learning, 2024

HyperAttention: Long-context Attention in Near-Linear Time.

[BibT_eX]

[DOI]

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Near Optimal Reconstruction of Spherical Harmonic Expansions.

[BibT_eX]

[DOI]

Amir Zandieh

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

KDEformer: Accelerating Transformers via Kernel Density Estimation.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2023

2022

Fast Neural Kernel Embeddings for General Activations.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Random Gegenbauer Features for Scalable Kernel Methods.

[BibT_eX]

[DOI]

Amir Zandieh

Proceedings of the International Conference on Machine Learning, 2022

Scalable MCMC Sampling for Nonsymmetric Determinantal Point Processes.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2022

Scalable Sampling for Nonsymmetric Determinantal Point Processes.

[BibT_eX]

[DOI]

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Random Features for the Neural Tangent Kernel.

[BibT_eX]

[DOI]

CoRR, 2021

Scaling Neural Tangent Kernels via Sketching and Random Features.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Scalable Learning and MAP Inference for Nonsymmetric Determinantal Point Processes.

[BibT_eX]

[DOI]

Victor-Emmanuel Brunel

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

Polynomial Tensor Sketch for Element-wise Function of Low-Rank Matrix.

[BibT_eX]

[DOI]

Proceedings of the 37th International Conference on Machine Learning, 2020

MAP Inference for Customized Determinantal Point Processes via Maximum Inner Product Search.

[BibT_eX]

[DOI]

Jennifer Gillenwater

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2018

Optimizing Spectral Sums using Randomized Chebyshev Expansions.

[BibT_eX]

[DOI]

CoRR, 2018

Stochastic Chebyshev Gradient Descent for Spectral Optimization.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2017

Approximating Spectral Sums of Large-Scale Matrices using Stochastic Chebyshev Approximations.

[BibT_eX]

[DOI]

SIAM J. Sci. Comput., 2017

Faster Greedy MAP Inference for Determinantal Point Processes.

[BibT_eX]

[DOI]

Proceedings of the 34th International Conference on Machine Learning, 2017

2016

Approximating the Spectral Sums of Large-scale Matrices using Chebyshev Approximations.

[BibT_eX]

[DOI]

CoRR, 2016

2015

Large-scale log-determinant computation through stochastic Chebyshev expansions.

[BibT_eX]

[DOI]

Dmitry Malioutov