| Sampling-based Estimation of the Number of Distinct Values in Distributed Environment | 0 | 0.34 | 2022 |
| Graph Neural Networks with Node-wise Architecture | 0 | 0.34 | 2022 |
| Towards Universal Sequence Representation Learning for Recommender Systems | 0 | 0.34 | 2022 |
| Alleviating Spurious Correlations in Knowledge-aware Recommendations through Counterfactual Generator | 0 | 0.34 | 2022 |
| Federated Matrix Factorization with Privacy Guarantee. | 0 | 0.34 | 2022 |
| FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Package for Federated Graph Learning | 0 | 0.34 | 2022 |
| Efficient Approximate Range Aggregation over Large-scale Spatial Data Federation (Extended Abstract) | 0 | 0.34 | 2022 |
| Explainable Neural Rule Learning | 0 | 0.34 | 2022 |
| One Size Does Not Fit All: A Bandit-Based Sampler Combination Framework with Theoretical Guarantees | 0 | 0.34 | 2022 |
| KoMen: Domain Knowledge Guided Interaction Recommendation for Emerging Scenarios | 1 | 0.36 | 2022 |
| Toward Personalized Answer Generation in E-Commerce via Multi-perspective Preference Modeling | 0 | 0.34 | 2022 |
| Learned Query Optimizer: At the Forefront of AI-Driven Databases | 0 | 0.34 | 2022 |
| Strengthening Order Preserving Encryption with Differential Privacy | 0 | 0.34 | 2022 |
| Finding Meta Winning Ticket to Train Your MAML | 0 | 0.34 | 2022 |
| VolcanoML: Speeding up End-to-End AutoML via Scalable Search Space Decomposition. | 0 | 0.34 | 2021 |
| Unified Conversational Recommendation Policy Learning via Graph-based Reinforcement Learning | 1 | 0.35 | 2021 |
| Learning to be a Statistician: Learned Estimator for Number of Distinct Values | 0 | 0.34 | 2021 |
| FlashP: an analytical pipeline for real-time forecasting of time-series relational data | 0 | 0.34 | 2021 |
| AutoML: From Methodology to Application | 0 | 0.34 | 2021 |
| CGM: an enhanced mechanism for streaming data collection with local differential privacy | 0 | 0.34 | 2021 |
| CGM: An Enhanced Mechanism for Streaming Data Collectionwith Local Differential Privacy. | 0 | 0.34 | 2021 |
| AutoML: A Perspective where Industry Meets Academy | 2 | 0.65 | 2021 |
| FlashP: An Analytical Pipeline for Real-time Forecasting of Time-Series Relational Data. | 0 | 0.34 | 2021 |
| Efficient Reductions and a Fast Algorithm of Maximum Weighted Independent Set | 0 | 0.34 | 2021 |
| Catch a Blowfish Alive: A Demonstration of Policy-Aware Differential Privacy for Interactive Data Exploration. | 0 | 0.34 | 2021 |
| Two-Sided Online Micro-Task Assignment in Spatial Crowdsourcing | 3 | 0.38 | 2021 |
| Debiasing Learning based Cross-domain Recommendation | 1 | 0.38 | 2021 |
| Federated Matrix Factorization with Privacy Guarantee | 0 | 0.34 | 2021 |
| Catch a blowfish alive: a demonstration of policy-aware differential privacy for interactive data exploration | 0 | 0.34 | 2021 |
| CausCF: Causal Collaborative Filtering for Recommendation Effect Estimation | 0 | 0.34 | 2021 |
| VolcanoML: speeding up end-to-end AutoML via scalable search space decomposition | 3 | 0.40 | 2021 |
| Collecting and Analyzing Data Jointly from Multiple Services under Local Differential Privacy. | 0 | 0.34 | 2020 |
| Sequential Recommendation with Self-Attentive Multi-Adversarial Network | 6 | 0.40 | 2020 |
| π-Hub: Large-scale video learning, storage, and retrieval on heterogeneous hardware platforms | 0 | 0.34 | 2020 |
| Simple and Deep Graph Convolutional Networks | 0 | 0.34 | 2020 |
| Automated Relational Meta-learning | 0 | 0.34 | 2020 |
| An Adaptive Embedding Framework for Heterogeneous Information Networks | 0 | 0.34 | 2020 |
| Learning to Mutate with Hypergradient Guided Population | 0 | 0.34 | 2020 |
| Intent Preference Decoupling for User Representation on Online Recommender System | 0 | 0.34 | 2020 |
| Linear and Range Counting under Metric-based Local Differential Privacy | 0 | 0.34 | 2020 |
| Continuous Integration of Machine Learning Models with ease.ml/ci: Towards a Rigorous Yet Practical Treatment. | 2 | 0.38 | 2019 |
| DPSAaS: Multi-Dimensional Data Sharing and Analytics as Services under Local Differential Privacy. | 0 | 0.34 | 2019 |
| DPSAaS: Multi-Dimensional Data Sharing and Analytics as Services under Local Differential Privacy. | 0 | 0.34 | 2019 |
| Answering Multi-Dimensional Analytical Queries under Local Differential Privacy | 5 | 0.42 | 2019 |
| Columnstore and B+ tree - Are Hybrid Physical Designs Important? | 0 | 0.34 | 2018 |
| Efficient Estimation of Inclusion Coefficient using HyperLogLog Sketches. | 0 | 0.34 | 2018 |
| Efficient Identification of Approximate Best Configuration of Training in Large Datasets. | 0 | 0.34 | 2018 |
| An Algorithmic Framework For Differentially Private Data Analysis on Trusted Processors. | 1 | 0.35 | 2018 |
| An Efficient Two-Layer Mechanism for Privacy-Preserving Truth Discovery. | 6 | 0.41 | 2018 |
| Comparing Population Means under Local Differential Privacy: with Significance and Power. | 1 | 0.36 | 2018 |
