3 papers
stat.ML2026
StatQAT: Statistical Quantizer Optimization for Deep Networks
Mehmet Aktukmak, Daniel Huang, Ke Ding
Quantization is essential for reducing the computational cost and memory usage of deep neural networks, enabling efficient inference on low-precision hardware. Despite the growing…
cs.LG2026
Scaling Gaussian Process Regression with Full Derivative Observations
Daniel Huang
We present a scalable Gaussian Process (GP) method called DSoftKI that can fit and predict full derivative observations. It extends SoftKI, a method that approximates a kernel via…
stat.ML2025
High-Dimensional Gaussian Process Regression with Soft Kernel Interpolation
Chris Camaño, Daniel Huang
We introduce Soft Kernel Interpolation (SoftKI), a method that combines aspects of Structured Kernel Interpolation (SKI) and variational inducing point methods, to achieve scalable…