Unsupervised domain adaptation (UDA) seeks to bridge the domain gap between the target and source using unlabeled target data. Source-free UDA removes the requirement for labeled source data at the target to preserve data privacy and storage. However, work on source-free UDA assumes knowledge of domain gap distribution, and hence is limited to either target-aware or classification task. To overcome it, we propose TASFAR, a novel target-agnostic source-free domain adaptation approach for regression tasks. Using prediction confidence, TASFAR estimates a label density map as the target label distribution, which is then used to calibrate the source model on the target domain. We have conducted extensive experiments on four regression tasks with various domain gaps, namely, pedestrian dead reckoning for different users, image-based people counting in different scenes, housing-price prediction at different districts, and taxi-trip duration prediction from different departure points. TASFAR is shown to substantially outperform the state-of-the-art source-free UDA approaches by averagely reducing 22% errors for the four tasks and achieve notably comparable accuracy as source-based UDA without using source data.

Order-driven market simulation mimics the trader behaviors to generate order streams to support interactive studies of financial strategies. In market simulator, the multi-agent approach is commonly adopted due to its explainability. Existing multi-agent systems employ heuristic search to generate order streams, which is inefficient for large-scale simulation. Furthermore, the search-based behavior calibration often leads to inconsistent trader actions under the same general market condition, making the simulation results unstable and difficult to interpret. We propose CaliSim, the first search-free calibration approach multi-agent market simulator which achieves large-scale efficiency and behavior consistency. CaliSim uses meta-learning and devises a surrogate trading system with a consistency loss function for the reproducibility of order stream and trader behaviors. Extensive experiments in the market replay and case studies show that CaliSim achieves state-of-the-art in terms of order stream reproduction with consistent trader behavior and can capture patterns of real markets.