32 citations · 79 across the 17 of their papers we have counts for
17 papers
Mamba Drafters for Speculative Decoding
Daewon Choi, Seunghyuk Oh, Saket Dingliwal +9
Speculative decoding has emerged as a promising approach to accelerating large language model (LLM) generation using a fast drafter while maintaining alignment with the target mode…
QJL: 1-Bit Quantized JL Transform for KV Cache Quantization with Zero Overhead
Amir Zandieh, Majid Daliri, Insu Han
Serving LLMs requires substantial memory due to the storage requirements of Key-Value (KV) embeddings in the KV cache, which grows with sequence length. An effective approach to co…
HyperAttention: Long-context Attention in Near-Linear Time
Insu Han, Rajesh Jayaram, Amin Karbasi +3
We present an approximate attention mechanism named HyperAttention to address the computational challenges posed by the growing complexity of long contexts used in Large Language M…
KDEformer: Accelerating Transformers via Kernel Density Estimation
Amir Zandieh, Insu Han, Majid Daliri +1
Dot-product attention mechanism plays a crucial role in modern deep architectures (e.g., Transformer) for sequence modeling, however, naïve exact computation of this model incurs q…
Fast Neural Kernel Embeddings for General Activations
Insu Han, Amir Zandieh, Jaehoon Lee +3
Infinite width limit has shed light on generalization and optimization aspects of deep learning by establishing connections between neural networks and kernel methods. Despite thei…
Scalable MCMC Sampling for Nonsymmetric Determinantal Point Processes
Insu Han, Mike Gartrell, Elvis Dohmatob +1
A determinantal point process (DPP) is an elegant model that assigns a probability to every subset of a collection of items. While conventionally a DPP is parameterized by a sy…
Near Optimal Reconstruction of Spherical Harmonic Expansions
Amir Zandieh, Insu Han, Haim Avron
We propose an algorithm for robust recovery of the spherical harmonic expansion of functions defined on the d-dimensional unit sphere using a near-optimal number…
Random Gegenbauer Features for Scalable Kernel Methods
Insu Han, Amir Zandieh, Haim Avron
We propose efficient random features for approximating a new and rich class of kernel functions that we refer to as Generalized Zonal Kernels (GZK). Our proposed GZK family, genera…
Scalable Sampling for Nonsymmetric Determinantal Point Processes
Insu Han, Mike Gartrell, Jennifer Gillenwater +2
A determinantal point process (DPP) on a collection of items is a model, parameterized by a symmetric kernel matrix, that assigns a probability to every subset of those items.…
Scaling Neural Tangent Kernels via Sketching and Random Features
Amir Zandieh, Insu Han, Haim Avron +3
The Neural Tangent Kernel (NTK) characterizes the behavior of infinitely-wide neural networks trained under least squares loss by gradient descent. Recent works also report that NT…
Random Features for the Neural Tangent Kernel
Insu Han, Haim Avron, Neta Shoham +2
The Neural Tangent Kernel (NTK) has discovered connections between deep neural networks and kernel methods with insights of optimization and generalization. Motivated by this, rece…
Scalable Learning and MAP Inference for Nonsymmetric Determinantal Point Processes
Mike Gartrell, Insu Han, Elvis Dohmatob +2
Determinantal point processes (DPPs) have attracted significant attention in machine learning for their ability to model subsets drawn from a large item collection. Recent work sho…
Polynomial Tensor Sketch for Element-wise Function of Low-Rank Matrix
Insu Han, Haim Avron, Jinwoo Shin
This paper studies how to sketch element-wise functions of low-rank matrices. Formally, given low-rank matrix A = [Aij] and scalar non-linear function f, we aim for finding an appr…
Stochastic Chebyshev Gradient Descent for Spectral Optimization
Insu Han, Haim Avron, Jinwoo Shin
A large class of machine learning techniques requires the solution of optimization problems involving spectral functions of parametric matrices, e.g. log-determinant and nuclear no…
Faster Greedy MAP Inference for Determinantal Point Processes
Insu Han, Prabhanjan Kambadur, Kyoungsoo Park +1
Determinantal point processes (DPPs) are popular probabilistic models that arise in many machine learning tasks, where distributions of diverse sets are characterized by matrix det…
Approximating the Spectral Sums of Large-scale Matrices using Chebyshev Approximations
Insu Han, Dmitry Malioutov, Haim Avron +1
Computation of the trace of a matrix function plays an important role in many scientific computing applications, including applications in machine learning, computational physics (…
Large-scale Log-determinant Computation through Stochastic Chebyshev Expansions
Insu Han, Dmitry Malioutov, Jinwoo Shin
Logarithms of determinants of large positive definite matrices appear ubiquitously in machine learning applications including Gaussian graphical and Gaussian process models, partit…