About Me
I am a postdoctoral research fellow in the Department of Electrical Engineering at Yale University, working with Prof. Amin Karbasi. Previously, I obtained my Ph.D. degree in the School of Electrical Engineering at Korea Advanced Institute of Science and Technology (KAIST), where I am advised by Jinwoo Shin. I recieved an M.S. in Electrical engineering and a B.S. in Electrical Engineering and Mathematics (minored) from KAIST. My research interests focus on approximate algorithm design and analysis for large-scale machine learning and its applications. In 2019, I was fortunate to be a recipient of Microsoft Research Asia Fellowship 2019.
Here is my CV (Last update: Sep 2022).
Contact
Email : insu.han at yale.edu
Publications
Fast Neural Kernel Embeddings for General Activations
[paper][code, also implemented in Neural Tangents library]
Insu Han, Amin Zandieh, Jaehoon Lee, Roman Novak, Lechao Xiao, Amin Karbasi
To be appeared in Advances in Neural Information Processing Systems (NeurIPS) 2022
Near Optimal Reconstruction of Spherical Harmonic Expansions
[paper]
Amir Zandieh, Insu Han, Haim Avron
Under review
Scalable MCMC Sampling for Nonsymmetric Determinantal Point Processes
[paper][code]
Insu Han, Mike Gartrell, Elvis Dohmatob, Amin Karbasi
International Conference on Machine Learning (ICML) 2022, Long Presentation (118/5630=2%)
Random Gegenbauer Features for Scalable Kernel Methods
[paper]
Insu Han, Amir Zandieh, Haim Avron
International Conference on Machine Learning (ICML) 2022, Long Presentation (118/5630=2%)
Scalable Sampling for Nonsymmetric Determinantal Point Processes
[paper][code]
Insu Han, Mike Gartrell, Jennifer Gillenwater, Elvis Dohmatob, Amin Karbasi
International Conference on Learning Representations (ICLR) 2022, Spotlight Presentation
Scaling Neural Tangent Kernels via Sketching and Random Features
[paper][code]
Insu Han, Amir Zandieh, Haim Avron, Neta Shoham, Chaewon Kim, Jinwoo Shin
Advances in Neural Information Processing Systems (NeurIPS) 2021
Scalable Learning and MAP Inference for Nonsymmetric Determinantal Point Processes
[paper][code]
Mike Gartrell, Insu Han, Elvis Dohmatob, Jennifer Gillenwater and Victor-Emmanuel Brunel
International Conference on Learning Representations (ICLR) 2021, Oral Presentation (58/2997=1.8%)
Polynomial Tensor Sketch for Element-wise Function of Low-Rank Matrix
[paper][code]
Insu Han, Haim Avron and Jinwoo Shin
International Conference on Machine Learning (ICML) 2020
MAP Inference for Customized Determinantal Point Processes via Maximum Inner Product Search
[paper][code]
Insu Han and Jennifer Gillenwater
International Conference on Artificial Intelligence and Statistics (AISTATS) 2020
Stochastic Chebyshev Gradient Descent for Spectral Optimization
[paper][poster][video]
Insu Han, Haim Avron and Jinwoo Shin
Neural Information Processing Systems (NeurIPS) 2018, Spotlight Presentation (168/4856=3.5%)
Faster Greedy MAP Inference for Determinantal Point Processes
[paper][code][video]
Insu Han, Prabhanjan Kambadur, Kyoungsoo Park and Jinwoo Shin
International Conference on Machine Learning (ICML) 2017
Approximating Spectral Sums of Large-scale Matrices using Stochastic Chebyshev Approximations
[paper]
Insu Han, Dmitry Malioutov, Haim Avron and Jinwoo Shin
SIAM Journal on Scientific Computing (SISC) 2017
Large-scale Log-determinant Computation through Stochastic Chebyshev Expansions
[paper][code][video]
Insu Han, Dmitry Malioutov, and Jinwoo Shin
International Conference on Machine Learning (ICML) 2015
Research experiences
- [Summer 2019] Research intern at Google New York City with Jennifer Gillenwater
- [Spring 2018] Visting student at Tel Aviv University with Haim Avron