{"total":19,"rows":[{"id":"2303.10158","title":"Data-centric Artificial Intelligence: A Survey","created":"2023-03-17","cited_by_count":203,"area":null},{"id":"2106.06135","title":"DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning","created":"2021-06-11","cited_by_count":66,"area":null},{"id":"2206.13033","title":"Normalized/Clipped SGD with Perturbation for Differentially Private Non-Convex Optimization","created":"2022-06-27","cited_by_count":36,"area":null},{"id":"2101.12240","title":"Differential Privacy Meets Federated Learning under Communication Constraints","created":"2021-01-28","cited_by_count":16,"area":null},{"id":"2409.09727","title":"From Challenges and Pitfalls to Recommendations and Opportunities: Implementing Federated Learning in Healthcare","created":"2024-09-15","cited_by_count":16,"area":null},{"id":"2205.09494","title":"Differentially private Riemannian optimization","created":"2022-05-19","cited_by_count":12,"area":null},{"id":"2401.00583","title":"Improving the Privacy and Practicality of Objective Perturbation for Differentially Private Linear Learners","created":"2023-12-31","cited_by_count":12,"area":null},{"id":"2405.13763","title":"Banded Square Root Matrix Factorization for Differentially Private Model Training","created":"2024-05-22","cited_by_count":12,"area":null},{"id":"2105.08179","title":"Learning Disentangled Representations for Time Series","created":"2021-05-17","cited_by_count":8,"area":null},{"id":"2306.08173","title":"Safeguarding Data in Multimodal AI: A Differentially Private Approach to CLIP Training","created":"2023-06-13","cited_by_count":7,"area":null},{"id":"2111.12064","title":"Semantic-Aware Collaborative Deep Reinforcement Learning Over Wireless Cellular Networks","created":"2021-11-23","cited_by_count":6,"area":null},{"id":"2101.07413","title":"Dynamic Privacy Budget Allocation Improves Data Efficiency of Differentially Private Gradient Descent","created":"2021-01-19","cited_by_count":5,"area":null},{"id":"2402.04375","title":"Bounding the Excess Risk for Linear Models Trained on Marginal-Preserving, Differentially-Private, Synthetic Data","created":"2024-02-06","cited_by_count":3,"area":null},{"id":"2105.11611","title":"KnowSR: Knowledge Sharing among Homogeneous Agents in Multi-agent Reinforcement Learning","created":"2021-05-25","cited_by_count":2,"area":null},{"id":"2412.07477","title":"Progressive-Resolution Policy Distillation: Leveraging Coarse-Resolution Simulations for Time-Efficient Fine-Resolution Policy Learning","created":"2024-12-10","cited_by_count":2,"area":null},{"id":"2302.14329","title":"Towards Personalized Preprocessing Pipeline Search","created":"2023-02-28","cited_by_count":1,"area":null},{"id":"2411.03059","title":"Enhancing DP-SGD through Non-monotonous Adaptive Scaling Gradient Weight","created":"2024-11-05","cited_by_count":1,"area":null},{"id":"2211.02003","title":"Private Blind Model Averaging - Distributed, Non-interactive, and Convergent","created":"2022-11-03","cited_by_count":0,"area":null},{"id":"2408.01445","title":"MiranDa: Mimicking the Learning Processes of Human Doctors to Achieve Causal Inference for Medication Recommendation","created":"2024-07-23","cited_by_count":0,"area":null}],"next_cursor":null}