4 papers
Q-Strata: Hierarchical Bit Allocation for Mixed-Precision Quantization of Mixture-of-Experts LLMs
Deokjae Lee, Sihun Chu, Hyun Oh Song
Mixed-precision quantization (MPQ) assigns a different bitwidth to each linear layer of a large language model (LLM) to minimize the quantization-induced quality loss under a fixed…
Q-Palette: Fractional-Bit Quantizers Toward Optimal Bit Allocation for Efficient LLM Deployment
Deokjae Lee, Hyun Oh Song
We study weight-only post-training quantization (PTQ), which quantizes the weights of a large language model (LLM) without retraining, using little or no calibration data. Weight-o…
Large-Scale Targeted Cause Discovery via Learning from Simulated Data
Jang-Hyun Kim, Claudia Skok Gibbs, Sangdoo Yun +2
We propose a novel machine learning approach for inferring causal variables of a target variable from observations. Our focus is on directly inferring a set of causal factors witho…
Training Greedy Policy for Proposal Batch Selection in Expensive Multi-Objective Combinatorial Optimization
Deokjae Lee, Hyun Oh Song, Kyunghyun Cho
Active learning is increasingly adopted for expensive multi-objective combinatorial optimization problems, but it involves a challenging subset selection problem, optimizing the ba…