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
KDEformer: Accelerating Transformers via Kernel Density Estimation
[paper]
Amir Zandieh, Insu Han, Majid Daliri, Amin Karbasi
Under review
Fast Neural Kernel Embeddings for General Activations
[paper][code, also implemented in Neural Tangents library]
Insu Han, Amir Zandieh, Jaehoon Lee, Roman Novak, Lechao Xiao, Amin Karbasi
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