Xiang Li

Silent Data Corruption (SDC) poses a critical threat to large-scale LLM training. Existing offline tests and online detection methods provide solutions for large-scale systems, yet they suffer from high overhead or low detection accuracy in LLM training. This paper presents AEGIS, an online SDC d...

Sentiment classification is a crucial task in natural language processing (NLP). To mitigate the spurious correlation, the causal word identification method estimates the impact of treatment words on sentence sentiment and removes those with low treatment effects. However, previous works regard t...

The post-click Conversion Rate (CVR) prediction plays a pivotal role in industrial recommender systems, directly serving as a decisive factor for ranking strategies and revenue optimization. While Entire Space Multi-Task Models (e.g., ESMM) have become the dominant framework by effectively addres...

In marketing, optimizing subsidy allocation to maximize overall profits is of substantial economic importance. Prior research has employed treatment effect estimation techniques to identify subsidy-sensitive items and design corresponding allocation strategies. However, more accurate treatment ef...

Post-click conversion rate (CVR) prediction is a central task in recommender systems, yet selection bias creates a severe distributional gap between the clicked training samples and the entire inference space. To address selection bias, propensity-based methods such as inverse propensity scoring...

User activity modeling serves as an effective approach to reflect Daily Active Users (DAU) levels by forecasting users' prospective behavioral engagement. In industrial practice, particularly in user active days prediction tasks, the data demonstrates extreme distributional imbalance. Specificall...