Chao Chen

Optimization of the overload detection algorithm for virtual machine consolidation

2016

With the increasing demand of computing resources, a lot of large-scale data centers have been established around the world. Data centers provide efficient and convenient computing services while consume enormous energy at the same time. …

ERMMA: Expected Risk Minimization for Matrix Approximation-based Recommender Systems

2017 · Proceedings of the AAAI Conference on Artificial Intelligence

Matrix approximation (MA) is one of the most popular techniques in today's recommender systems. In most MA-based recommender systems, the problem of risk minimization should be defined, and how to achieve minimum expected risk in …

Research on Kano model based on online comment data mining

2018

The opinion mining and the sentiment analysis of the network comment are the key points of the text analysis. By excavating the comment information of the online products, the real demand of the customers can …

Multi-photon quantum boson sampling on a semiconductor (Conference Presentation)

2018

Boson sampling is considered as a strong candidate to demonstrate the “quantum advantage / supremacy” over classical computers. However, previous proof-of-principle experiments suffered from small photon number and low sampling rates owing to the inefficiencies …

Collaborative Filtering with Noisy Ratings

2019 · Society for Industrial and Applied Mathematics eBooks

User ratings on items are noisy in real-world recommender systems, which raises challenges to matrix approximation (MA)-based collaborative filtering (CF) algorithms — the learned models will be easily biased to the noisy training data and …

A Multimodal Transformer: Fusing Clinical Notes with Structured EHR Data for Interpretable In-Hospital Mortality Prediction

2022 · PubMed

Deep-learning-based clinical decision support using structured electronic health records (EHR) has been an active research area for predicting risks of mortality and diseases. Meanwhile, large amounts of narrative clinical notes provide complementary information, but are …

FoPro: Few-Shot Guided Robust Webly-Supervised Prototypical Learning

2022 · arXiv (Cornell University)

Recently, webly supervised learning (WSL) has been studied to leverage numerous and accessible data from the Internet. Most existing methods focus on learning noise-robust models from web images while neglecting the performance drop caused by …

Provable Robust Saliency-based Explanations

2022 · arXiv (Cornell University)

To foster trust in machine learning models, explanations must be faithful and stable for consistent insights. Existing relevant works rely on the $\ell_p$ distance for stability assessment, which diverges from human perception. Besides, existing adversarial …

Topology-Guided Multi-Class Cell Context Generation for Digital Pathology

2023 · arXiv (Cornell University)

In digital pathology, the spatial context of cells is important for cell classification, cancer diagnosis and prognosis. To model such complex cell context, however, is challenging. Cells form different mixtures, lineages, clusters and holes. To …

A New Safe-Level Enabled Borderline-SMOTE for Condition Recognition of Imbalanced Dataset

2023 · IEEE Transactions on Instrumentation and Measurement

Machine learning-based classification strategy has been successfully applied in actual industrial monitoring but it is often hindered when the data set is imbalanced. Technically, the misclassification phenomenon, as a serious performance degradation of generalisation ability, …

Reduced-Complexity Erasure Decoding of Low-Rate Reed–Solomon Codes Based on LCH-FFT

2023

This paper presents a new erasure decoding algorithm for low-rate Reed–Solomon codes (rate ≤ 0.5) based on a recently proposed FFT known as LCH-FFT. The algorithm requires O(n log k) finite field operations, where n …

LAMEE: A Light All-MLP Framework for TimeSeries Prediction Empowering Recommendations

2023 · Research Square

Abstract Exogenous variables, unrelated to the recommendation system itself, can significantly enhance its performance. Therefore, integrating these time-evolving exogenous variables into a time series and conducting time series predictions can maximize the potential of recommendation …

FusionSF: Fuse Heterogeneous Modalities in a Vector Quantized Framework for Robust Solar Power Forecasting

2024 · arXiv (Cornell University)

Accurate solar power forecasting is crucial to integrate photovoltaic plants into the electric grid, schedule and secure the power grid safety. This problem becomes more demanding for those newly installed solar plants which lack sufficient …

Robust Conformal Prediction under Distribution Shift via Physics-Informed Structural Causal Model

2024 · arXiv (Cornell University)

Uncertainty is critical to reliable decision-making with machine learning. Conformal prediction (CP) handles uncertainty by predicting a set on a test input, hoping the set to cover the true label with at least $(1-α)$ confidence. …