8 citations · 13 across the 7 of their papers we have counts for
7 papers
Bending the Scaling Law Curve in Large-Scale Recommendation Systems
Qin Ding, Kevin Course, Linjian Ma +19
Learning from user interaction history through sequential models has become a cornerstone of large-scale recommender systems. Recent advances in large language models have revealed…
A Unified Knowledge-Distillation and Semi-Supervised Learning Framework to Improve Industrial Ads Delivery Systems
Hamid Eghbalzadeh, Yang Wang, Rui Li +9
Industrial ads ranking systems conventionally rely on labeled impression data, which leads to challenges such as overfitting, slower incremental gain from model scaling, and biases…
Syndicated Bandits: A Framework for Auto Tuning Hyper-parameters in Contextual Bandit Algorithms
Qin Ding, Yue Kang, Yi-Wei Liu +3
The stochastic contextual bandit problem, which models the trade-off between exploration and exploitation, has many real applications, including recommender systems, online adverti…
Robust Stochastic Linear Contextual Bandits Under Adversarial Attacks
Qin Ding, Cho-Jui Hsieh, James Sharpnack
Stochastic linear contextual bandit algorithms have substantial applications in practice, such as recommender systems, online advertising, clinical trials, etc. Recent works show t…
An Efficient Algorithm For Generalized Linear Bandit: Online Stochastic Gradient Descent and Thompson Sampling
Qin Ding, Cho-Jui Hsieh, James Sharpnack
We consider the contextual bandit problem, where a player sequentially makes decisions based on past observations to maximize the cumulative reward. Although many algorithms have b…
Multiscale Non-stationary Stochastic Bandits
Qin Ding, Cho-Jui Hsieh, James Sharpnack
Classic contextual bandit algorithms for linear models, such as LinUCB, assume that the reward distribution for an arm is modeled by a stationary linear regression. When the linear…
Bivariate Binomial Moments and Bonferroni-type Inequalities
Qin Ding, Eugene Seneta
We obtain bivariate forms of Gumbel's, Fréchet's and Chung's linear inequalities for in terms of the bivariate binomial moments , $1\le i\le k, 1\l…