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Insu Han
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2020 – today
- 2024
- [c15]Insu Han, Rajesh Jayaram, Amin Karbasi, Vahab Mirrokni, David P. Woodruff, Amir Zandieh
:
HyperAttention: Long-context Attention in Near-Linear Time. ICLR 2024 - [c14]Daniel LeVine, Syed Asad Rizvi, Sacha Lévy, Nazreen Pallikkavaliyaveetil, David Zhang, Xingyu Chen, Sina Ghadermarzi, Ruiming Wu, Zihe Zheng, Ivan Vrkic, Anna Zhong, Daphne Raskin, Insu Han, Antonio Henrique de Oliveira Fonseca, Josue Ortega Caro, Amin Karbasi, Rahul Madhav Dhodapkar, David van Dijk:
Cell2Sentence: Teaching Large Language Models the Language of Biology. ICML 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
- [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 - [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
- [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
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 - [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
- [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 - [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
- [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 - [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)
2010 – 2019
- 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
- [c3]Insu Han, Haim Avron, Jinwoo Shin:
Stochastic Chebyshev Gradient Descent for Spectral Optimization. NeurIPS 2018: 7397-7407 - [i4]Insu Han, Haim Avron, Jinwoo Shin:
Optimizing Spectral Sums using Randomized Chebyshev Expansions. CoRR abs/1802.06355 (2018) - 2017
- [j1]Insu Han, Dmitry Malioutov, Haim Avron
Approximating Spectral Sums of Large-Scale Matrices using Stochastic Chebyshev Approximations. SIAM J. Sci. Comput. 39(4) (2017) - [c2]Insu Han, Prabhanjan Kambadur, KyoungSoo Park, Jinwoo Shin:
Faster Greedy MAP Inference for Determinantal Point Processes. ICML 2017: 1384-1393 - [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
- [c1]Insu Han, Dmitry Malioutov, Jinwoo Shin:
Large-scale log-determinant computation through stochastic Chebyshev expansions. ICML 2015: 908-917 - [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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