AMPLab Publications

• Comparing Human-Centric and Robot-Centric Sampling for Robot Deep Learning from Demonstrations
• ActiveClean: Interactive Data Cleaning For Statistical Modeling
• TSC-DL: Unsupervised Trajectory Segmentation of Multi-Modal Surgical Demonstrations with Deep Learning.
• Data Cleaning: Overview and Emerging Challenges
• ActiveClean: An Interactive Data Cleaning Framework For Modern Machine Learning (Demonstration Paper)
• PrivateClean: Data Cleaning and Differential Privacy
• SampleClean: Fast and Reliable Analytics on Dirty Data
• Transition State Clustering: Unsupervised Surgical Trajectory Segentation For Robot Learning
• Stale View Cleaning: Getting Fresh Answers from Stale Materialized Views
• Wisteria: Nurturing Scalable Data Cleaning Infrastructure
• Stale View Cleaning: Getting Fresh Answers from Stale Materialized Views
• Communication-Efficient Distributed Dual Coordinate Ascent
• A Partitioning Framework for Aggressive Data Skipping
• A Methodology for Learning, Analyzing, and Mitigating Social Influence Bias in Recommender Systems
• A Sample-and-Clean Framework for Fast and Accurate Query Processing on Dirty Data
• Fine-grained Partitioning for Aggressive Data Skipping