no code implementations • 11 Dec 2023 • Alex Kulesza, Ananda Theertha Suresh, Yuyan Wang
We propose a new algorithm and show that it is min-max optimal, achieving the best possible constant in the leading term of the mean squared error for all $\epsilon$, and that this constant is the same as the optimal algorithm under the swap model.
no code implementations • 2 Jun 2023 • Pan Li, Yuyan Wang, Ed H. Chi, Minmin Chen
Hierarchical structure, on the other hand, exists in a user's novelty-seeking intent, which is manifested as a static and intrinsic user preference for seeking novelty along with a dynamic session-based propensity.
Hierarchical Reinforcement Learning Recommendation Systems +1
no code implementations • 2 Jun 2023 • Pan Li, Yuyan Wang, Ed H. Chi, Minmin Chen
For the aspect-based recommendation component, the extracted aspects are concatenated with the usual user and item features used by the recommendation model.
no code implementations • 19 Mar 2023 • Hongmeng Liu, Jiapeng Zhao, Yixuan Huo, Yuyan Wang, Chun Liao, Liyan Shen, Shiyao Cui, Jinqiao Shi
Traditional user representation methods mainly rely on modeling the text information of posts and cannot capture the temporal content and the forum interaction of posts.
no code implementations • 17 Nov 2022 • Bo Chang, Alexandros Karatzoglou, Yuyan Wang, Can Xu, Ed H. Chi, Minmin Chen
We demonstrate the effectiveness of the latent user intent modeling via offline analyses as well as live experiments on a large-scale industrial recommendation platform.
no code implementations • 2 Apr 2022 • Jianling Wang, Ya Le, Bo Chang, Yuyan Wang, Ed H. Chi, Minmin Chen
Users who come to recommendation platforms are heterogeneous in activity levels.
no code implementations • NeurIPS 2021 • Silvio Lattanzi, Benjamin Moseley, Sergei Vassilvitskii, Yuyan Wang, Rudy Zhou
In correlation clustering we are given a set of points along with recommendations whether each pair of points should be placed in the same cluster or into separate clusters.
no code implementations • 4 Jun 2021 • Yuyan Wang, Xuezhi Wang, Alex Beutel, Flavien Prost, Jilin Chen, Ed H. Chi
This presents a multi-dimensional Pareto frontier on (1) the trade-off between group fairness and accuracy with respect to each task, as well as (2) the trade-offs across multiple tasks.
1 code implementation • 30 Aug 2020 • Benjamin Moseley, Yuyan Wang
The paper builds a theoretical connection between this objective and the bisecting k-means algorithm.
no code implementations • 17 Aug 2020 • Zhe Chen, Yuyan Wang, Dong Lin, Derek Zhiyuan Cheng, Lichan Hong, Ed H. Chi, Claire Cui
Despite deep neural network (DNN)'s impressive prediction performance in various domains, it is well known now that a set of DNN models trained with the same model specification and the same data can produce very different prediction results.
no code implementations • 13 Aug 2020 • Yuyan Wang, Zhe Zhao, Bo Dai, Christopher Fifty, Dong Lin, Lichan Hong, Ed H. Chi
A delicate balance between multi-task generalization and multi-objective optimization is therefore needed for finding a better trade-off between efficiency and generalization.
no code implementations • 1 Aug 2020 • Benjamin Moseley, Kirk Pruhs, Alireza Samadian, Yuyan Wang
Few relational algorithms are known and this paper offers techniques for designing relational algorithms as well as characterizing their limitations.
no code implementations • NeurIPS 2020 • Sara Ahmadian, Alessandro Epasto, Marina Knittel, Ravi Kumar, Mohammad Mahdian, Benjamin Moseley, Philip Pham, Sergei Vassilvitskii, Yuyan Wang
As machine learning has become more prevalent, researchers have begun to recognize the necessity of ensuring machine learning systems are fair.