Large datasets with noisy labels and high dimensions have become increasingly prevalent in industry. These datasets often contain errors or inconsistencies in the assigned labels and introduce a vast number of predictive variables. Such issues frequently arise in real-world scenarios due to uncertainties or human errors during data collection and annotation processes. The presence of noisy labels and high dimensions can significantly impair the generalization ability and accuracy of trained models. To address the above issues, we introduce a simple-structured penalized γ-divergence model and a novel meta-gradient correction algorithm and establish the foundations of these two modules based on rigorous theoretical proofs. Finally, comprehensive experiments are conducted to validate their effectiveness in detecting noisy labels and mitigating the curse of dimensionality and suggest that our proposed model and algorithm can achieve promising outcomes. Moreover, we open-source our codes and distinctive datasets on GitHub (refer to https://github.com/DebtVC2022/Robust_Learning_with_MGC).

Efficient stock status analysis and forecasting are important for stock market participants to be able to improve returns and reduce associated risks. However, stock market data are replete with noise and randomness, rendering the task of attaining precise price predictions arduous. Moreover, the lagging phenomenon of price prediction makes it hard for the corresponding trading strategy to capture the turning points, resulting in lower investment returns. To address this issue, we propose a framework for Important Trading Point (ITP) prediction based on Return-Adaptive Piecewise Linear Representation (RA-PLR) and a Batch Attention Multi-Scale Convolution Recurrent Neural Network (Batch-MCRNN) with the starting point of improving stock investment returns. Firstly, a novel RA-PLR method is adopted to detect historical ITPs in the stock market. Then, we apply the Batch-MCRNN model to integrate the information of the data across space, time, and sample dimensions for predicting future ITPs. Finally, we design a trading strategy that combines the Relative Strength Index (RSI) and the Double Check (DC) method to match ITP predictions. We conducted a comprehensive and systematic comparison with several state-of-the-art benchmark models on real-world datasets regarding prediction accuracy, risk, return, and other indicators. Our proposed method significantly outperformed the comparative methods on all indicators and has significant reference value for stock investment.

Low efficiency in telephone banking services reduces customer satisfaction. Therefore, some recent studies have concentrated on applying voice conversion models to improve telephone banking services. However, building such a model raises three huge challenges, as practical telephone banking services require natural and high-quality conversations. These challenges include the lack of parallel speech data, difficulty in generating natural speech, and difficulty in modeling long speech. To tackle such challenges, we propose a novel unsupervised prosody transfer for improving customer satisfaction in telephone conversations relying on grounded theoretical foundations. Our model consists of a solo-encoding disentanglement module and a forge module. (i) The disentanglement module uses three unique constraints to effectively reduce manual feature engineering and training costs and decompose extremely long speech without parallel data. (ii) The forge module hammers at converting the source prosody to the target one and guarantees correct fine-grained alignments, thereby generating natural speech. Finally, extensive experiments are conducted on large-scale telephone recordings from XWbank in China and suggest that our model can achieve promising outcomes. Moreover, we open-source our codes and unique datasets on GitHub.