Publications (24)
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…
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…
KnapSpec: Self-Speculative Decoding via Adaptive Layer Selection as a Knapsack Problem
Seongjin Cha, Gyuwan Kim, Dongsu Han +2
Self-speculative decoding (SSD) accelerates LLM inference by skipping layers to create an efficient draft model, yet existing methods often rely on static heuristics that ignore th…
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…
PolarQuant: Quantizing KV Caches with Polar Transformation
Insu Han, Praneeth Kacham, Amin Karbasi +2
Large language models (LLMs) require significant memory to store Key-Value (KV) embeddings in their KV cache, especially when handling long-range contexts. Quantization of these KV…
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…