Insu Han

According to our database, Insu Han authored at least 19 papers between 2015 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

SubGen: Token Generation in Sublinear Time and Memory.

Amir Zandieh, Insu Han, Vahab Mirrokni, Amin Karbasi

CoRR, 2024

2023

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

Insu Han, Rajesh Jayaram, Amin Karbasi, Vahab Mirrokni, David P. Woodruff, Amir Zandieh

CoRR, 2023

Near Optimal Reconstruction of Spherical Harmonic Expansions.

Amir Zandieh, Insu Han, Haim Avron

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.

Amir Zandieh, Insu Han, Majid Daliri, Amin Karbasi

Proceedings of the International Conference on Machine Learning, 2023

2022

Fast Neural Kernel Embeddings for General Activations.

Insu Han, Amir Zandieh, Jaehoon Lee, Roman Novak, Lechao Xiao, Amin Karbasi

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.

Insu Han, Amir Zandieh, Haim Avron

Proceedings of the International Conference on Machine Learning, 2022

Scalable MCMC Sampling for Nonsymmetric Determinantal Point Processes.

Insu Han, Mike Gartrell, Elvis Dohmatob, Amin Karbasi

Proceedings of the International Conference on Machine Learning, 2022

Scalable Sampling for Nonsymmetric Determinantal Point Processes.

Insu Han, Mike Gartrell, Jennifer Gillenwater, Elvis Dohmatob, Amin Karbasi

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Random Features for the Neural Tangent Kernel.

Insu Han, Haim Avron, Neta Shoham, Chaewon Kim, Jinwoo Shin

CoRR, 2021

Scaling Neural Tangent Kernels via Sketching and Random Features.

Amir Zandieh, Insu Han, Haim Avron, Neta Shoham, Chaewon Kim, Jinwoo Shin

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.

Mike Gartrell, Insu Han, Elvis Dohmatob, Jennifer Gillenwater, 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.

Insu Han, Haim Avron, Jinwoo Shin

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

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

Insu Han, Jennifer Gillenwater

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

2018

Optimizing Spectral Sums using Randomized Chebyshev Expansions.

Insu Han, Haim Avron, Jinwoo Shin

CoRR, 2018

Stochastic Chebyshev Gradient Descent for Spectral Optimization.

Insu Han, Haim Avron, Jinwoo Shin

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.

Insu Han, Dmitry Malioutov, Haim Avron, Jinwoo Shin

SIAM J. Sci. Comput., 2017

Faster Greedy MAP Inference for Determinantal Point Processes.

Insu Han, Prabhanjan Kambadur, KyoungSoo Park, Jinwoo Shin

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

2016

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

Insu Han, Dmitry Malioutov, Haim Avron, Jinwoo Shin

CoRR, 2016

2015

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

Insu Han, Dmitry Malioutov, Jinwoo Shin

Proceedings of the 32nd International Conference on Machine Learning, 2015