Long-tail learning primarily focuses on mitigating the label distribution shift between long-tailed training data and uniformly distributed test data. However, in real-world applications, we often encounter a more intricate challenge where the test label distribution is agnostic. To address this problem, we first theoretically establish the substantial potential for reducing the generalization error if we can precisely estimate the test label distribution. Motivated by the theoretical insight, we introduce a simple yet effective solution called label shift correction (LSC). LSC estimates the test label distribution within the proposed framework of generalized black box shift estimation, and adjusts the predictions from a pre-trained model to align with the test distribution. Theoretical analyses confirm that accurate estimation of test label distribution can effectively reduce the generalization error. Extensive experimental results demonstrate that our method significantly outperforms previous state-of-the-art approaches, especially when confronted with non-uniform test label distribution. Notably, the proposed method is general and complements existing long-tail learning approaches, consistently improving their performance. The source code is available at https://github.com/Stomach-ache/label-shift-correction

Recently on-demand food delivery service has become very popular in China. More than 30 million orders are placed by eaters of Meituan-Dianping everyday. Delicacies are delivered to eaters in 30 minutes on average. To fully leverage the ability of our couriers and restaurants, delivery scope is proposed as an infrastructure product for on-demand food delivery area. A delivery scope based retrieval system is designed and built on our platform. In order to draw suitable delivery scopes for millions of restaurant partners, we propose a pioneering delivery scope generation framework. In our framework, a single delivery scope generation algorithm is proposed by using spatial computational techniques and data mining techniques. Moreover, a scope scoring algorithm and decision algorithm are proposed by utilizing machine learning models and combinatorial optimization techniques. Specifically, we propose a novel delivery scope sample generation method and use the scope related features to estimate order numbers and average delivery time in a period of time for each delivery scope. Then we formalize the candidate scopes selection process as a binary integer programming problem. Both branch&bound algorithm and a heuristic search algorithm are integrated in our system. Results of online experiments show that scopes generated by our new algorithm significantly outperform manual generated ones. Our algorithm brings more orders without hurt of users' experience. After deployed online, our system has saved thousands of hours for operation staff, and it is considered to be one of the most useful operation tools to balance demand of eaters and supply of restaurants and couriers.

Blind channel identification has been a popular research subject in recent years. We introduce the concept of knowledge-based blind channel identification. By relying on known information such as the pulse shaping filter and the antialiasing filter responses, the performance of channel identification and equalization can be significantly enhanced in digital communication systems. We present two simple methods: one in time-domain and one in frequency domain. Our simulation results demonstrate the performance of these two approaches.