Machine learning models often suffer from performance degradation when dealing with class distributions that differ from the training distribution, a scenario commonly referred to as label shift. Addressing this challenge, this paper introduces Match, a novel adjustment approach that maximizes the likelihood of predicted probabilities under class prevalence constraints. Unlike existing methods such as retraining with instance re-weighting and the Bayes update rule, Match ensures that the adjusted class distribution aligns precisely with the prevalence estimates from quantifiers. By formulating the adjustment process as a binary integer linear optimization problem, Match benefits from efficient mixed-integer solvers. Extensive experiments demonstrate that Match outperforms the state-of-the-art in classifier adjustment with statistical significance, particularly in handling scenarios with imbalanced distributions.

An assumption often made in supervised learning is that the training and testing sets have the same label distribution. However, in real-life scenarios, this assumption rarely holds. For example, medical diagnosis result distributions change over time and across locations; fraud detection models must adapt as patterns of fraudulent activity shift; the category distribution of social media posts changes based on trending topics and user demographics. In the task of label shift estimation, the goal is to estimate the changing label distribution pt(y) in the testing set, assuming the likelihood p(x|y) does not change, implying no concept drift. In this paper, we propose a new approach for post-hoc label shift estimation, unlike previous methods that perform moment matching with confusion matrix estimated from a validation set or maximize the likelihood of the new data with an expectation-maximization algorithm. We aim to incrementally update the prior on each sample, adjusting each posterior for more accurate label shift estimation. The proposed method is based on intuitive assumptions on classifiers that are generally true for modern probabilistic classifiers. The proposed method relies on a weaker notion of calibration compared to other methods. As a post-hoc approach for label shift estimation, the proposed method is versatile and can be applied to any black-box probabilistic classifier. Experiments on CIFAR-10 and MNIST show that the proposed method consistently outperforms the current state-of-the-art maximum likelihood-based methods under different calibrations and varying intensities of label shift.

Deep neural networks often produce miscalibrated probability estimates, leading to overconfident predictions. A common approach for calibration is fitting a post-hoc calibration map on unseen validation data that transforms predicted probabilities. A key desirable property of the calibration map is instance-wise monotonicity (i.e., preserving the ranking of probability outputs). However, most existing post-hoc calibration methods do not guarantee monotonicity. Previous monotonic approaches either use an under-parameterized calibration map with limited expressive ability or rely on black-box neural networks, which lack interpretability and robustness. In this paper, we propose a family of novel monotonic post-hoc calibration methods, which employs a constrained calibration map parameterized linearly with respect to the number of classes. Our proposed approach ensures expressiveness, robustness, and interpretability while preserving the relative ordering of the probability output by formulating the proposed calibration map as a constrained optimization problem. Our proposed methods achieve state-of-the-art performance across datasets with different deep neural network models, outperforming existing calibration methods while being data and computation-efficient. Our code is available at https://github.com/YunruiZhang/Calibration-by-Constrained-Transformation

Three-dimensional electromagnetic field drive technology, as a cutting-edge remote wireless control method, is extensively utilized in the biomedical diagnosis and treatment of micro-robots. This paper presents the design of a three-dimensional electromagnetic drive system for micro-robots, leveraging a gradient magnetic field to achieve comprehensive automatic control in three axes. Firstly, we refine the iron core’s end structure to produce an uniform gradient magnetic field throughout the three-dimensional space. Following that, the parameters at the end of the iron core are fine-tuned to meet the specifications for magnetic field gradient, magnetic flux density, and effective workspace. Then a three-dimensional electromagnetic drive system with strong magnetic field gradient is established, achieving a remarkable maximum gradient of 1.70 T/m at the center of the workspace. Compared with other systems, the gradient is significantly enhanced. Subsequently, we carry out a three-dimensional drive experiment for a micro-robot, confirming the system’s driving efficacy. To enable precise path following for micro-robots within a three-dimensional space, we have formulated a control strategy rooted in micro-robot dynamics. The controller stability is guaranteed through the Lyapunov theory. Ultimately, a three-dimensional path following experiment is executed on the developed electromagnetic drive system. The experiment confirms the capability of our designed system which can achieve the three-dimensional closed-loop motion for the micro-robot.