56 citations · 223 across the 16 of their papers we have counts for
17 papers
CARD: Channel Aligned Robust Blend Transformer for Time Series Forecasting
Wang Xue, Tian Zhou, Qingsong Wen +3
Recent studies have demonstrated the great power of Transformer models for time series forecasting. One of the key elements that lead to the transformer's success is the channel-in…
Studying the Impact of Data Disclosure Mechanism in Recommender Systems via Simulation
Ziqian Chen, Fei Sun, Yifan Tang +3
Recently, privacy issues in web services that rely on users' personal data have raised great attention. Unlike existing privacy-preserving technologies such as federated learning a…
OpenBox: A Generalized Black-box Optimization Service
Yang Li, Yu Shen, Wentao Zhang +9
Black-box optimization (BBO) has a broad range of applications, including automatic machine learning, engineering, physics, and experimental design. However, it remains a challenge…
CausCF: Causal Collaborative Filtering for RecommendationEffect Estimation
Xu Xie, Zhaoyang Liu, Shiwen Wu +6
To improve user experience and profits of corporations, modern industrial recommender systems usually aim to select the items that are most likely to be interacted with (e.g., clic…
Explore User Neighborhood for Real-time E-commerce Recommendation
Xu Xie, Fei Sun, Xiaoyong Yang +4
Recommender systems play a vital role in modern online services, such as Amazon and Taobao. Traditional personalized methods, which focus on user-item (UI) relations, have been wid…
Efficient Automatic CASH via Rising Bandits
Yang Li, Jiawei Jiang, Jinyang Gao +3
The Combined Algorithm Selection and Hyperparameter optimization (CASH) is one of the most fundamental problems in Automatic Machine Learning (AutoML). The existing Bayesian optimi…
MFES-HB: Efficient Hyperband with Multi-Fidelity Quality Measurements
Yang Li, Yu Shen, Jiawei Jiang +3
Hyperparameter optimization (HPO) is a fundamental problem in automatic machine learning (AutoML). However, due to the expensive evaluation cost of models (e.g., training deep lear…
Contrastive Learning for Sequential Recommendation
Xu Xie, Fei Sun, Zhaoyang Liu +4
Sequential recommendation methods play a crucial role in modern recommender systems because of their ability to capture a user's dynamic interest from her/his historical interactio…
Modeling Personalized Item Frequency Information for Next-basket Recommendation
Haoji Hu, Xiangnan He, Jinyang Gao +1
Next-basket recommendation (NBR) is prevalent in e-commerce and retail industry. In this scenario, a user purchases a set of items (a basket) at a time. NBR performs sequential mod…
Privileged Features Distillation at Taobao Recommendations
Chen Xu, Quan Li, Junfeng Ge +7
Features play an important role in the prediction tasks of e-commerce recommendations. To guarantee the consistency of off-line training and on-line serving, we usually utilize the…
Effective and Efficient Dropout for Deep Convolutional Neural Networks
Shaofeng Cai, Yao Shu, Gang Chen +3
Convolutional Neural networks (CNNs) based applications have become ubiquitous, where proper regularization is greatly needed. To prevent large neural network models from overfitti…
Model Slicing for Supporting Complex Analytics with Elastic Inference Cost and Resource Constraints
Shaofeng Cai, Gang Chen, Beng Chin Ooi +1
Deep learning models have been used to support analytics beyond simple aggregation, where deeper and wider models have been shown to yield great results. These models consume a hug…
Medical Concept Embedding with Time-Aware Attention
Xiangrui Cai, Jinyang Gao, Kee Yuan Ngiam +3
Embeddings of medical concepts such as medication, procedure and diagnosis codes in Electronic Medical Records (EMRs) are central to healthcare analytics. Previous work on medical…
PANDA: Facilitating Usable AI Development
Jinyang Gao, Wei Wang, Meihui Zhang +7
Recent advances in artificial intelligence (AI) and machine learning have created a general perception that AI could be used to solve complex problems, and in some situations over-…
Rafiki: Machine Learning as an Analytics Service System
Wei Wang, Sheng Wang, Jinyang Gao +4
Big data analytics is gaining massive momentum in the last few years. Applying machine learning models to big data has become an implicit requirement or an expectation for most ana…
Deep Learning At Scale and At Ease
Wei Wang, Gang Chen, Haibo Chen +5
Recently, deep learning techniques have enjoyed success in various multimedia applications, such as image classification and multi-modal data analysis. Large deep learning models a…
Active Sampler: Light-weight Accelerator for Complex Data Analytics at Scale
Jinyang Gao, H. V. Jagadish, Beng Chin Ooi
Recent years have witnessed amazing outcomes from "Big Models" trained by "Big Data". Most popular algorithms for model training are iterative. Due to the surging volumes of data,…