134 citations · 445 across the 29 of their papers we have counts for
9 papers · 1 filter
Online Matching: A Real-time Bandit System for Large-scale Recommendations
Xinyang Yi, Shao-Chuan Wang, Ruining He +6
The last decade has witnessed many successes of deep learning-based models for industry-scale recommender systems. These models are typically trained offline in a batch manner. Whi…
Improving Training Stability for Multitask Ranking Models in Recommender Systems
Jiaxi Tang, Yoel Drori, Daryl Chang +6
Recommender systems play an important role in many content platforms. While most recommendation research is dedicated to designing better models to improve user experience, we foun…
Improving Multi-Task Generalization via Regularizing Spurious Correlation
Ziniu Hu, Zhe Zhao, Xinyang Yi +4
Multi-Task Learning (MTL) is a powerful learning paradigm to improve generalization performance via knowledge sharing. However, existing studies find that MTL could sometimes hurt…
Learning to Embed Categorical Features without Embedding Tables for Recommendation
Wang-Cheng Kang, Derek Zhiyuan Cheng, Tiansheng Yao +4
Embedding learning of categorical features (e.g. user/item IDs) is at the core of various recommendation models including matrix factorization and neural collaborative filtering. T…
Self-supervised Learning for Large-scale Item Recommendations
Tiansheng Yao, Xinyang Yi, Derek Zhiyuan Cheng +8
Large scale recommender models find most relevant items from huge catalogs, and they play a critical role in modern search and recommendation systems. To model the input space with…
Learning-to-Rank with Partitioned Preference: Fast Estimation for the Plackett-Luce Model
Jiaqi Ma, Xinyang Yi, Weijing Tang +4
We investigate the Plackett-Luce (PL) model based listwise learning-to-rank (LTR) on data with partitioned preference, where a set of items are sliced into ordered and disjoint par…
More Supervision, Less Computation: Statistical-Computational Tradeoffs in Weakly Supervised Learning
Xinyang Yi, Zhaoran Wang, Zhuoran Yang +2
We consider the weakly supervised binary classification problem where the labels are randomly flipped with probability . Although there exist numerous algorithms for this pro…
Solving a Mixture of Many Random Linear Equations by Tensor Decomposition and Alternating Minimization
Xinyang Yi, Constantine Caramanis, Sujay Sanghavi
We consider the problem of solving mixed random linear equations with components. This is the noiseless setting of mixed linear regression. The goal is to estimate multiple lin…
Regularized EM Algorithms: A Unified Framework and Statistical Guarantees
Xinyang Yi, Constantine Caramanis
Latent variable models are a fundamental modeling tool in machine learning applications, but they present significant computational and analytical challenges. The popular EM algori…