Author

Ashish J. Khisti

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#machine learning Jun 2026

Multi-Bitwidth Quantization for LLMs Using Additive Codebooks

Drop-by-Drop is proposed, a novel multi-bitwidth post-training quantization framework enabling inference-time precision control over LLM weights from a single quantized model, and it is proved that Gaussian sources are successively refinable under a weighted mean squared error distortion motivated by LLM loss functions...

Liza Babaoglu, Shuangyi Chen, Ashish J. Khisti · arXiv.org · 1 citation

#machine learning Preprint Sep 2026

Scalable Discrete-to-Continuous Channel Simulation for Compression and Privacy

This work introduces a scheme for both exact and approximate simulation of discrete-to-continuous channels which conversely uses a fixed number of random samples, and therefore has a runtime independent of the channel and the input.

Joseph Rowan, Buu Phan, Ashish J. Khisti · 0 citations