Yield optimization is one of the central challenges in submicrometer integrated circuit manufacture. Classic yield optimization methods rely on importance sampling (IS) to provide efficient and robust yield estimation for each individual design. Despite its success, such an approach is still computationally expensive due to the large number of calculations for many different designs. To resolve this challenge, we propose conditional importance sampling (CIS) that can approximate the optimal proposal distribution for any given design by leveraging the power of the modern deep-learning-based sampling method, conditional normalizing flow. More importantly, CIS generalizes well to unseen design and thus can deliver effective yield optimization with a small number of expensive simulations. To conduct yield optimization efficiently with consideration of creditable uncertainty, we propose a novel Important Sampling Bayesian optimization (ISBO) using a deep-warped gradient-boosting regression (GBR). The proposed method is extensively evaluated against five state-of-the-art baselines; the results show that the proposed method delivers superior performance: a speedup of $1.10 \times-l0.46\times$ ($4.45 \times$ on average) with even higher yield designs, an improvement of $1.1 \times-10 \times$ ($4.44 \times$ on average) in consideration of the Optimality-Cost Ratio, and most importantly, excellent robustness and consistency in all our extensive experiments on analog and SRAM circuits.