456 citations · 611 across the 7 of their papers we have counts for
8 papers
MARCO: Click-Intent Decomposition for Calibrated Ads Conversion Prediction
Shiwen Shen, Xiru Huang, Liang Luo +32
Not all clicks are equal. Industrial ads ranking decouples conversion probability into click-through rate (CTR) and post-click conversion rate (CVR), yet treats every click as the…
Towards the Better Ranking Consistency: A Multi-task Learning Framework for Early Stage Ads Ranking
Xuewei Wang, Qiang Jin, Shengyu Huang +10
Dividing ads ranking system into retrieval, early, and final stages is a common practice in large scale ads recommendation to balance the efficiency and accuracy. The early stage r…
Refusion: Enabling Large-Size Realistic Image Restoration with Latent-Space Diffusion Models
Ziwei Luo, Fredrik K. Gustafsson, Zheng Zhao +2
This work aims to improve the applicability of diffusion models in realistic image restoration. Specifically, we enhance the diffusion model in several aspects such as network arch…
Learnability and Complexity of Quantum Samples
Murphy Yuezhen Niu, Andrew M. Dai, Li Li +5
Given a quantum circuit, a quantum computer can sample the output distribution exponentially faster in the number of bits than classical computers. A similar exponential separation…
Image Augmentations for GAN Training
Zhengli Zhao, Zizhao Zhang, Ting Chen +2
Data augmentations have been widely studied to improve the accuracy and robustness of classifiers. However, the potential of image augmentation in improving GAN models for image sy…
Improved Consistency Regularization for GANs
Zhengli Zhao, Sameer Singh, Honglak Lee +3
Recent work has increased the performance of Generative Adversarial Networks (GANs) by enforcing a consistency cost on the discriminator. We improve on this technique in several wa…
Improving Differentially Private Models with Active Learning
Zhengli Zhao, Nicolas Papernot, Sameer Singh +2
Broad adoption of machine learning techniques has increased privacy concerns for models trained on sensitive data such as medical records. Existing techniques for training differen…
Generating Natural Adversarial Examples
Zhengli Zhao, Dheeru Dua, Sameer Singh
Due to their complex nature, it is hard to characterize the ways in which machine learning models can misbehave or be exploited when deployed. Recent work on adversarial examples,…