3 papers
cs.LG2026
Entropy-Constrained Adaptive Stochastic Quantization
Ran Ben Basat, Yaniv Ben-Itzhak, Michael Mitzenmacher +1
Adaptive stochastic quantization (ASQ) is a recently introduced quantization approach that optimizes the Mean Squared Error (MSE) for a given input while preserving unbiasedness. I…
cs.LG2026
A Note on TurboQuant and the Earlier DRIVE/EDEN Line of Work
Ran Ben-Basat, Yaniv Ben-Itzhak, Gal Mendelson +3
This note clarifies the relationship between the recent TurboQuant work and the earlier DRIVE (NeurIPS 2021) and EDEN (ICML 2022) schemes. DRIVE is a 1-bit quantizer that EDEN exte…
cs.LG2025
Optimal and Near-Optimal Adaptive Vector Quantization
Ran Ben-Basat, Yaniv Ben-Itzhak, Michael Mitzenmacher +1
Quantization is a fundamental optimization for many machine-learning use cases, including compressing gradients, model weights and activations, and datasets. The most accurate form…