Reconstruction of Sub-Nyquist Random Sampling for Sparse and Multi-Band Signals. - Amir Zandieh; Alireza Zareian; Masoumeh Azghani; Farokh Marvasti (CoRR, 2014)

Random Fourier Features for Kernel Ridge Regression: Approximation Bounds and Statistical Guarantees. - Haim Avron; Michael Kapralov; Cameron Musco; Christopher Musco; Ameya Velingker; Amir Zandieh (ICML, 2017)

An adaptive sublinear-time block sparse fourier transform. - Volkan Cevher; Michael Kapralov; Jonathan Scarlett; Amir Zandieh (STOC, 2017)

Beyond 1/2-Approximation for Submodular Maximization on Massive Data Streams. - Ashkan Norouzi-Fard; Jakub Tarnawski; Slobodan Mitrovic; Amir Zandieh; Aidasadat Mousavifar; Ola Svensson (ICML, 2018)

Efficiently Learning Fourier Sparse Set Functions. - Andisheh Amrollahi; Amir Zandieh; Michael Kapralov; Andreas Krause 001 (NeurIPS, 2019)

Dimension-independent Sparse Fourier Transform. - Michael Kapralov; Ameya Velingker; Amir Zandieh (SODA, 2019)

A universal sampling method for reconstructing signals with simple Fourier transforms. - Haim Avron; Michael Kapralov; Cameron Musco; Christopher Musco; Ameya Velingker; Amir Zandieh (STOC, 2019)

Scaling up Kernel Ridge Regression via Locality Sensitive Hashing. - Amir Zandieh; Navid Nouri; Ameya Velingker; Michael Kapralov; Ilya P. Razenshteyn (AISTATS, 2020)

Near Input Sparsity Time Kernel Embeddings via Adaptive Sampling. - David P. Woodruff; Amir Zandieh (ICML, 2020)

Oblivious Sketching of High-Degree Polynomial Kernels. - Thomas D. Ahle; Michael Kapralov; Jakob Bæk Tejs Knudsen; Rasmus Pagh; Ameya Velingker; David P. Woodruff; Amir Zandieh (SODA, 2020)

Fourier Sampling in Signal Processing and Numerical Linear Algebra. - Amir Zandieh (2020)

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

Learning with Neural Tangent Kernels in Near Input Sparsity Time. - Amir Zandieh (CoRR, 2021)

Sparse Fourier Transform by traversing Cooley-Tukey FFT computation graphs. - Karl Bringmann; Michael Kapralov; Mikhail Makarov; Vasileios Nakos; Amir Yagudin; Amir Zandieh (CoRR, 2021)

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

Leverage Score Sampling for Tensor Product Matrices in Input Sparsity Time. - David P. Woodruff; Amir Zandieh (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)

Traversing the FFT Computation Tree for Dimension-Independent Sparse Fourier Transforms. - Karl Bringmann; Michael Kapralov; Mikhail Makarov; Vasileios Nakos; Amir Yagudin; Amir Zandieh (SODA, 2023)

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

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)

TurboQuant: Online Vector Quantization with Near-optimal Distortion Rate. - Amir Zandieh; Majid Daliri; Majid Hadian; Vahab Mirrokni (CoRR, 2025)