Amir Zandieh

According to our database, Amir Zandieh authored at least 26 papers between 2014 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2025

TurboQuant: Online Vector Quantization with Near-optimal Distortion Rate.

Amir Zandieh, Majid Daliri, Majid Hadian, Vahab Mirrokni

CoRR, April, 2025

BalanceKV: KV Cache Compression through Discrepancy Theory.

Insu Han, Michael Kapralov, Ekaterina Kochetkova, Kshiteej Sheth, Amir Zandieh

CoRR, February, 2025

PolarQuant: Quantizing KV Caches with Polar Transformation.

Insu Han, Praneeth Kacham, Amin Karbasi, Vahab Mirrokni, Amir Zandieh

CoRR, February, 2025

QJL: 1-Bit Quantized JL Transform for KV Cache Quantization with Zero Overhead.

Amir Zandieh, Majid Daliri, Insu Han

Proceedings of the AAAI-25, Sponsored by the Association for the Advancement of Artificial Intelligence, February 25, 2025

2024

SubGen: Token Generation in Sublinear Time and Memory.

Amir Zandieh, Insu Han, Vahab Mirrokni, Amin Karbasi

CoRR, 2024

HyperAttention: Long-context Attention in Near-Linear Time.

Insu Han, Rajesh Jayaram, Amin Karbasi, Vahab Mirrokni, David P. Woodruff, Amir Zandieh

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Traversing the FFT Computation Tree for Dimension-Independent Sparse Fourier Transforms.

Karl Bringmann, Michael Kapralov, Mikhail Makarov, Vasileios Nakos, Amir Yagudin, Amir Zandieh

Proceedings of the 2023 ACM-SIAM Symposium on Discrete Algorithms, 2023

Near Optimal Reconstruction of Spherical Harmonic Expansions.

Amir Zandieh, Insu Han, Haim Avron

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

KDEformer: Accelerating Transformers via Kernel Density Estimation.

Amir Zandieh, Insu Han, Majid Daliri, Amin Karbasi

Proceedings of the International Conference on Machine Learning, 2023

2022

Fast Neural Kernel Embeddings for General Activations.

Insu Han, Amir Zandieh, Jaehoon Lee, Roman Novak, Lechao Xiao, Amin Karbasi

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Leverage Score Sampling for Tensor Product Matrices in Input Sparsity Time.

David P. Woodruff, Amir Zandieh

Proceedings of the International Conference on Machine Learning, 2022

Random Gegenbauer Features for Scalable Kernel Methods.

Insu Han, Amir Zandieh, Haim Avron

Proceedings of the International Conference on Machine Learning, 2022

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

Learning with Neural Tangent Kernels in Near Input Sparsity Time.

Amir Zandieh

CoRR, 2021

Scaling Neural Tangent Kernels via Sketching and Random Features.

Amir Zandieh, Insu Han, Haim Avron, Neta Shoham, Chaewon Kim, Jinwoo Shin

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Fourier Sampling in Signal Processing and Numerical Linear Algebra.

Amir Zandieh

PhD thesis, 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

Proceedings of the 2020 ACM-SIAM Symposium on Discrete Algorithms, 2020

Near Input Sparsity Time Kernel Embeddings via Adaptive Sampling.

David P. Woodruff, Amir Zandieh

Proceedings of the 37th International Conference on Machine Learning, 2020

Scaling up Kernel Ridge Regression via Locality Sensitive Hashing.

Amir Zandieh, Navid Nouri, Ameya Velingker, Michael Kapralov, Ilya P. Razenshteyn

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

A universal sampling method for reconstructing signals with simple Fourier transforms.

Haim Avron, Michael Kapralov, Cameron Musco, Christopher Musco, Ameya Velingker, Amir Zandieh

Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing, 2019

Dimension-independent Sparse Fourier Transform.

Michael Kapralov, Ameya Velingker, Amir Zandieh

Proceedings of the Thirtieth Annual ACM-SIAM Symposium on Discrete Algorithms, 2019

Efficiently Learning Fourier Sparse Set Functions.

Andisheh Amrollahi, Amir Zandieh, Michael Kapralov, Andreas Krause

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018

Beyond 1/2-Approximation for Submodular Maximization on Massive Data Streams.

Ashkan Norouzi-Fard, Jakub Tarnawski, Slobodan Mitrovic, Amir Zandieh, Aidasadat Mousavifar, Ola Svensson

Proceedings of the 35th International Conference on Machine Learning, 2018

2017

An adaptive sublinear-time block sparse fourier transform.

Volkan Cevher, Michael Kapralov, Jonathan Scarlett, Amir Zandieh

Proceedings of the 49th Annual ACM SIGACT Symposium on Theory of Computing, 2017

Random Fourier Features for Kernel Ridge Regression: Approximation Bounds and Statistical Guarantees.

Haim Avron, Michael Kapralov, Cameron Musco, Christopher Musco, Ameya Velingker, Amir Zandieh

Proceedings of the 34th International Conference on Machine Learning, 2017

2014

Reconstruction of Sub-Nyquist Random Sampling for Sparse and Multi-Band Signals.

Amir Zandieh, Alireza Zareian, Masoumeh Azghani, Farokh Marvasti

CoRR, 2014