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Insu Han
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Journal Articles
- 2017
- [j1]Insu Han, Dmitry Malioutov, Haim Avron
, Jinwoo Shin:
Approximating Spectral Sums of Large-Scale Matrices using Stochastic Chebyshev Approximations. SIAM J. Sci. Comput. 39(4) (2017)
Conference and Workshop Papers
- 2024
- [c14]Insu Han, Rajesh Jayaram, Amin Karbasi, Vahab Mirrokni, David P. Woodruff, Amir Zandieh:
HyperAttention: Long-context Attention in Near-Linear Time. ICLR 2024 - 2023
- [c13]Amir Zandieh, Insu Han, Majid Daliri, Amin Karbasi:
KDEformer: Accelerating Transformers via Kernel Density Estimation. ICML 2023: 40605-40623 - [c12]Amir Zandieh, Insu Han, Haim Avron:
Near Optimal Reconstruction of Spherical Harmonic Expansions. NeurIPS 2023 - 2022
- [c11]Insu Han, Mike Gartrell, Jennifer Gillenwater, Elvis Dohmatob, Amin Karbasi:
Scalable Sampling for Nonsymmetric Determinantal Point Processes. ICLR 2022 - [c10]Insu Han, Mike Gartrell, Elvis Dohmatob, Amin Karbasi:
Scalable MCMC Sampling for Nonsymmetric Determinantal Point Processes. ICML 2022: 8213-8229 - [c9]Insu Han, Amir Zandieh
, Haim Avron:
Random Gegenbauer Features for Scalable Kernel Methods. ICML 2022: 8330-8358 - [c8]Insu Han, Amir Zandieh, Jaehoon Lee, Roman Novak, Lechao Xiao, Amin Karbasi:
Fast Neural Kernel Embeddings for General Activations. NeurIPS 2022 - 2021
- [c7]Mike Gartrell, Insu Han, Elvis Dohmatob, Jennifer Gillenwater, Victor-Emmanuel Brunel:
Scalable Learning and MAP Inference for Nonsymmetric Determinantal Point Processes. ICLR 2021 - [c6]Amir Zandieh, Insu Han, Haim Avron, Neta Shoham, Chaewon Kim, Jinwoo Shin:
Scaling Neural Tangent Kernels via Sketching and Random Features. NeurIPS 2021: 1062-1073 - 2020
- [c5]Insu Han, Jennifer Gillenwater:
MAP Inference for Customized Determinantal Point Processes via Maximum Inner Product Search. AISTATS 2020: 2797-2807 - [c4]Insu Han, Haim Avron, Jinwoo Shin:
Polynomial Tensor Sketch for Element-wise Function of Low-Rank Matrix. ICML 2020: 3984-3993 - 2018
- [c3]Insu Han, Haim Avron, Jinwoo Shin:
Stochastic Chebyshev Gradient Descent for Spectral Optimization. NeurIPS 2018: 7397-7407 - 2017
- [c2]Insu Han, Prabhanjan Kambadur, KyoungSoo Park, Jinwoo Shin:
Faster Greedy MAP Inference for Determinantal Point Processes. ICML 2017: 1384-1393 - 2015
- [c1]Insu Han, Dmitry Malioutov, Jinwoo Shin:
Large-scale log-determinant computation through stochastic Chebyshev expansions. ICML 2015: 908-917
Informal and Other Publications
- 2024
- [i17]Amir Zandieh, Insu Han, Vahab Mirrokni, Amin Karbasi:
SubGen: Token Generation in Sublinear Time and Memory. CoRR abs/2402.06082 (2024) - [i16]Amir Zandieh, Majid Daliri, Insu Han:
QJL: 1-Bit Quantized JL Transform for KV Cache Quantization with Zero Overhead. CoRR abs/2406.03482 (2024) - 2023
- [i15]Amir Zandieh, Insu Han, Majid Daliri, Amin Karbasi:
KDEformer: Accelerating Transformers via Kernel Density Estimation. CoRR abs/2302.02451 (2023) - [i14]Insu Han, Rajesh Jayaram, Amin Karbasi, Vahab Mirrokni, David P. Woodruff, Amir Zandieh:
HyperAttention: Long-context Attention in Near-Linear Time. CoRR abs/2310.05869 (2023) - 2022
- [i13]Insu Han, Mike Gartrell, Jennifer Gillenwater, Elvis Dohmatob, Amin Karbasi:
Scalable Sampling for Nonsymmetric Determinantal Point Processes. CoRR abs/2201.08417 (2022) - [i12]Insu Han, Amir Zandieh, Haim Avron:
Random Gegenbauer Features for Scalable Kernel Methods. CoRR abs/2202.03474 (2022) - [i11]Amir Zandieh, Insu Han, Haim Avron:
Near Optimal Reconstruction of Spherical Harmonic Expansions. CoRR abs/2202.12995 (2022) - [i10]Insu Han, Mike Gartrell, Elvis Dohmatob, Amin Karbasi:
Scalable MCMC Sampling for Nonsymmetric Determinantal Point Processes. CoRR abs/2207.00486 (2022) - [i9]Insu Han, Amir Zandieh, Jaehoon Lee, Roman Novak, Lechao Xiao, Amin Karbasi:
Fast Neural Kernel Embeddings for General Activations. CoRR abs/2209.04121 (2022) - 2021
- [i8]Insu Han, Haim Avron, Neta Shoham, Chaewon Kim, Jinwoo Shin:
Random Features for the Neural Tangent Kernel. CoRR abs/2104.01351 (2021) - [i7]Amir Zandieh, Insu Han, Haim Avron, Neta Shoham, Chaewon Kim, Jinwoo Shin:
Scaling Neural Tangent Kernels via Sketching and Random Features. CoRR abs/2106.07880 (2021) - 2020
- [i6]Mike Gartrell, Insu Han, Elvis Dohmatob, Jennifer Gillenwater, Victor-Emmanuel Brunel:
Scalable Learning and MAP Inference for Nonsymmetric Determinantal Point Processes. CoRR abs/2006.09862 (2020) - 2019
- [i5]Insu Han, Haim Avron, Jinwoo Shin:
Polynomial Tensor Sketch for Element-wise Function of Low-Rank Matrix. CoRR abs/1905.11616 (2019) - 2018
- [i4]Insu Han, Haim Avron, Jinwoo Shin:
Optimizing Spectral Sums using Randomized Chebyshev Expansions. CoRR abs/1802.06355 (2018) - 2017
- [i3]Insu Han, Prabhanjan Kambadur, KyoungSoo Park, Jinwoo Shin:
Faster Greedy MAP Inference for Determinantal Point Processes. CoRR abs/1703.03389 (2017) - 2016
- [i2]Insu Han, Dmitry Malioutov, Haim Avron, Jinwoo Shin:
Approximating the Spectral Sums of Large-scale Matrices using Chebyshev Approximations. CoRR abs/1606.00942 (2016) - 2015
- [i1]Insu Han, Dmitry Malioutov, Jinwoo Shin:
Large-scale Log-determinant Computation through Stochastic Chebyshev Expansions. CoRR abs/1503.06394 (2015)
Coauthor Index
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