Large-scale log-determinant computation through stochastic Chebyshev expansions. - Insu Han; Dmitry Malioutov; Jinwoo Shin (ICML, 2015)

Approximating the Spectral Sums of Large-scale Matrices using Chebyshev Approximations. - Insu Han; Dmitry Malioutov; Haim Avron; Jinwoo Shin (CoRR, 2016)

Faster Greedy MAP Inference for Determinantal Point Processes. - Insu Han; Prabhanjan Kambadur; KyoungSoo Park; Jinwoo Shin (ICML, 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)

Stochastic Chebyshev Gradient Descent for Spectral Optimization. - Insu Han; Haim Avron; Jinwoo Shin (NeurIPS, 2018)

Optimizing Spectral Sums using Randomized Chebyshev Expansions. - Insu Han; Haim Avron; Jinwoo Shin (CoRR, 2018)

MAP Inference for Customized Determinantal Point Processes via Maximum Inner Product Search. - Insu Han; Jennifer Gillenwater (AISTATS, 2020)

Polynomial Tensor Sketch for Element-wise Function of Low-Rank Matrix. - Insu Han; Haim Avron; Jinwoo Shin (ICML, 2020)

Scalable Learning and MAP Inference for Nonsymmetric Determinantal Point Processes. - Mike Gartrell; Insu Han; Elvis Dohmatob; Jennifer Gillenwater; Victor-Emmanuel Brunel (ICLR, 2021)

Scaling Neural Tangent Kernels via Sketching and Random Features. - Amir Zandieh; Insu Han; Haim Avron; Neta Shoham; Chaewon Kim; Jinwoo Shin (NeurIPS, 2021)

Random Features for the Neural Tangent Kernel. - Insu Han; Haim Avron; Neta Shoham; Chaewon Kim; Jinwoo Shin (CoRR, 2021)

Scalable Sampling for Nonsymmetric Determinantal Point Processes. - Insu Han; Mike Gartrell; Jennifer Gillenwater; Elvis Dohmatob; Amin Karbasi (ICLR, 2022)

Scalable MCMC Sampling for Nonsymmetric Determinantal Point Processes. - Insu Han; Mike Gartrell; Elvis Dohmatob; Amin Karbasi (ICML, 2022)

Random Gegenbauer Features for Scalable Kernel Methods. - Insu Han; Amir Zandieh; Haim Avron (ICML, 2022)

Fast Neural Kernel Embeddings for General Activations. - Insu Han; Amir Zandieh; Jaehoon Lee 001; Roman Novak; Lechao Xiao; Amin Karbasi (NeurIPS, 2022)

KDEformer: Accelerating Transformers via Kernel Density Estimation. - Amir Zandieh; Insu Han; Majid Daliri; Amin Karbasi (ICML, 2023)

Near Optimal Reconstruction of Spherical Harmonic Expansions. - Amir Zandieh; Insu Han; Haim Avron (NeurIPS, 2023)

HyperAttention: Long-context Attention in Near-Linear Time. - Insu Han; Rajesh Jayaram; Amin Karbasi; Vahab Mirrokni; David P. Woodruff; Amir Zandieh (ICLR, 2024)

Cell2Sentence: Teaching Large Language Models the Language of Biology. - 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; et al. (ICML, 2024)

SubGen: Token Generation in Sublinear Time and Memory. - Amir Zandieh; Insu Han; Vahab Mirrokni; Amin Karbasi (CoRR, 2024)

QJL: 1-Bit Quantized JL Transform for KV Cache Quantization with Zero Overhead. - Amir Zandieh; Majid Daliri; Insu Han (AAAI, 2025)

A Dual Power Mode Q/V-Band SiGe HBT Cascode Power Amplifier With a Novel Reconfigurable Four-Way Wilkinson Power Combiner Balun. - Insu Han; Hanjung Lee; Seong-Mo Moon; Inchan Ju (IEEE Trans. Circuits Syst. II Express Briefs, 2025)

PolarQuant: Quantizing KV Caches with Polar Transformation. - Insu Han; Praneeth Kacham; Amin Karbasi; Vahab Mirrokni; Amir Zandieh (CoRR, 2025)

BalanceKV: KV Cache Compression through Discrepancy Theory. - Insu Han; Michael Kapralov; Ekaterina Kochetkova; Kshiteej Sheth; Amir Zandieh (CoRR, 2025)

Design of a Compact, Highly Efficient, and High-Power Q-/V-Band SiGe HBT Cascode Power Amplifier With a Four-Way Wilkinson Power Combiner Balun. - Hanjung Lee; Insu Han; Inchan Ju (IEEE J. Solid State Circuits, 2025)

Mamba Drafters for Speculative Decoding. - Daewon Choi; Seunghyuk Oh; Saket Dingliwal; Jihoon Tack; Kyuyoung Kim; Woomin Song; Seojin Kim; Insu Han; Jinwoo Shin; Aram Galstyan; Shubham Katiyar; Sravan Babu Bodapati (CoRR, 2025)