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Article ; Online: New Insights Into Learning With Correntropy-Based Regression.

Feng, Yunlong

2020 Volume 33, Issue 1, Page(s) 157–173

Abstract: Stemming from information-theoretic learning, the correntropy criterion and its applications to machine learning tasks have been extensively studied and explored. Its application to regression problems leads to the robustness-enhanced regression paradigm: ...

Abstract	Stemming from information-theoretic learning, the correntropy criterion and its applications to machine learning tasks have been extensively studied and explored. Its application to regression problems leads to the robustness-enhanced regression paradigm: correntropy-based regression. Having drawn a great variety of successful real-world applications, its theoretical properties have also been investigated recently in a series of studies from a statistical learning viewpoint. The resulting big picture is that correntropy-based regression regresses toward the conditional mode function or the conditional mean function robustly under certain conditions. Continuing this trend and going further, in this study, we report some new insights into this problem. First, we show that under the additive noise regression model, such a regression paradigm can be deduced from minimum distance estimation, implying that the resulting estimator is essentially a minimum distance estimator and thus possesses robustness properties. Second, we show that the regression paradigm in fact provides a unified approach to regression problems in that it approaches the conditional mean, the conditional mode, and the conditional median functions under certain conditions. Third, we present some new results when it is used to learn the conditional mean function by developing its error bounds and exponential convergence rates under conditional (
MeSH term(s)	Algorithms ; Machine Learning ; Regression Analysis
Language	English
Publishing date	2020-10-20
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1025692-1
ISSN	1530-888X ; 0899-7667
ISSN (online)	1530-888X
ISSN	0899-7667
DOI	10.1162/neco_a_01334
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Rainfall during the night can trigger non-migratory take-off behavior of the white-backed planthopper, Sogatella furcifera.

Yang, Haibo / Feng, Yunlong / Zhu, Pinhong / Li, Dingxu / Hu, Gao

Insect science

2024

Abstract: Take-off behavior is crucial to the overall success of insect migration. Although most high-altitude migratory flights commence with mass take-offs around dusk and dawn, little is known about nighttime take-off behavior. The take-off behavior of ... ...

Abstract	Take-off behavior is crucial to the overall success of insect migration. Although most high-altitude migratory flights commence with mass take-offs around dusk and dawn, little is known about nighttime take-off behavior. The take-off behavior of migratory Sogatella furcifera was investigated in field cages from 2017 to 2019. The species showed a bimodal take-off pattern at dusk and dawn on rainless nights, with mass flight at dusk more intense than dawn flight. However, a higher frequency of take-offs during the nighttime was observed on rainy nights, resulting in the absence of dawn take-offs. Most migratory take-off individuals at dusk and dawn landed on the cage top or the walls above 150 cm, while non-migratory individuals that took off during the nighttime due to rainfall mainly landed on the cage walls below 150 cm. Furthermore, it has been observed that migratory take-off individuals possess stronger sustained flight capabilities and exhibit more immature ovaries compared with non-migratory take-offs. These findings advance our understanding of the take-off behavior of S. furcifera and thus provide a basis for the accurate prediction and management of the migratory dynamics of this pest.
Language	English
Publishing date	2024-02-27
Publishing country	Australia
Document type	Journal Article
ZDB-ID	2179775-4
ISSN	1744-7917 ; 1672-9609
ISSN (online)	1744-7917
ISSN	1672-9609
DOI	10.1111/1744-7917.13347
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Article ; Online: A Framework of Learning Through Empirical Gain Maximization.

Feng, Yunlong / Wu, Qiang

Neural computation

2021 Volume 33, Issue 6, Page(s) 1656–1697

Abstract: We develop in this letter a framework of empirical gain maximization (EGM) to address the robust regression problem where heavy-tailed noise or outliers may be present in the response variable. The idea of EGM is to approximate the density function of ... ...

Abstract	We develop in this letter a framework of empirical gain maximization (EGM) to address the robust regression problem where heavy-tailed noise or outliers may be present in the response variable. The idea of EGM is to approximate the density function of the noise distribution instead of approximating the truth function directly as usual. Unlike the classical maximum likelihood estimation that encourages equal importance of all observations and could be problematic in the presence of abnormal observations, EGM schemes can be interpreted from a minimum distance estimation viewpoint and allow the ignorance of those observations. Furthermore, we show that several well-known robust nonconvex regression paradigms, such as Tukey regression and truncated least square regression, can be reformulated into this new framework. We then develop a learning theory for EGM by means of which a unified analysis can be conducted for these well-established but not fully understood regression approaches. This new framework leads to a novel interpretation of existing bounded nonconvex loss functions. Within this new framework, the two seemingly irrelevant terminologies, the well-known Tukey's biweight loss for robust regression and the triweight kernel for nonparametric smoothing, are closely related. More precisely, we show that Tukey's biweight loss can be derived from the triweight kernel. Other frequently employed bounded nonconvex loss functions in machine learning, such as the truncated square loss, the Geman-McClure loss, and the exponential squared loss, can also be reformulated from certain smoothing kernels in statistics. In addition, the new framework enables us to devise new bounded nonconvex loss functions for robust learning.
Language	English
Publishing date	2021-09-08
Publishing country	United States
Document type	Journal Article
ZDB-ID	1025692-1
ISSN	1530-888X ; 0899-7667
ISSN (online)	1530-888X
ISSN	0899-7667
DOI	10.1162/neco_a_01384
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Fast Rates of Gaussian Empirical Gain Maximization With Heavy-Tailed Noise.

Huang, Shouyou / Feng, Yunlong / Wu, Qiang

IEEE transactions on neural networks and learning systems

2022 Volume 33, Issue 10, Page(s) 6038–6043

Abstract: In a regression setup, we study in this brief the performance of Gaussian empirical gain maximization (EGM), which includes a broad variety of well-established robust estimation approaches. In particular, we conduct a refined learning theory analysis for ...

Abstract	In a regression setup, we study in this brief the performance of Gaussian empirical gain maximization (EGM), which includes a broad variety of well-established robust estimation approaches. In particular, we conduct a refined learning theory analysis for Gaussian EGM, investigate its regression calibration properties, and develop improved convergence rates in the presence of heavy-tailed noise. To achieve these purposes, we first introduce a new weak moment condition that could accommodate the cases where the noise distribution may be heavy-tailed. Based on the moment condition, we then develop a novel comparison theorem that can be used to characterize the regression calibration properties of Gaussian EGM. It also plays an essential role in deriving improved convergence rates. Therefore, the present study broadens our theoretical understanding of Gaussian EGM.
Language	English
Publishing date	2022-10-05
Publishing country	United States
Document type	Journal Article
ISSN	2162-2388
ISSN (online)	2162-2388
DOI	10.1109/TNNLS.2022.3171171
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Article: Unmixing Biological Fluorescence Image Data with Sparse and Low-Rank Poisson Regression.

Wang, Ruogu / Lemus, Alex A / Henneberry, Colin M / Ying, Yiming / Feng, Yunlong / Valm, Alex M

bioRxiv : the preprint server for biology

2023

Abstract: Multispectral biological fluorescence microscopy has enabled the identification of multiple targets in complex samples. The accuracy in the unmixing result degrades (1) as the number of fluorophores used in any experiment increases and (2) as the signal- ... ...

Abstract	Multispectral biological fluorescence microscopy has enabled the identification of multiple targets in complex samples. The accuracy in the unmixing result degrades (1) as the number of fluorophores used in any experiment increases and (2) as the signal-to-noise ratio in the recorded images decreases. Further, the availability of prior knowledge regarding the expected spatial distributions of fluorophores in images of labeled cells provides an opportunity to improve the accuracy of fluorophore identification and abundance. We propose a regularized sparse and low-rank Poisson unmixing approach (SL-PRU) to deconvolve spectral images labeled with highly overlapping fluorophores which are recorded in low signal-to-noise regimes. Firstly, SL-PRU implements multi-penalty terms when pursuing sparseness and spatial correlation of the resulting abundances in small neighborhoods simultaneously. Secondly, SL-PRU makes use of Poisson regression for unmixing instead of least squares regression to better estimate photon abundance. Thirdly, we propose a method to tune the SL-PRU parameters involved in the unmixing procedure in the absence of knowledge of the ground truth abundance information in a recorded image. By validating on simulated and real-world images, we show that our proposed method leads to improved accuracy in unmixing fluorophores with highly overlapping spectra.
Language	English
Publishing date	2023-01-18
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.01.06.523044
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Article ; Online: Unmixing biological fluorescence image data with sparse and low-rank Poisson regression.

Wang, Ruogu / Lemus, Alex A / Henneberry, Colin M / Ying, Yiming / Feng, Yunlong / Valm, Alex M

Bioinformatics (Oxford, England)

2023 Volume 39, Issue 4

Abstract: Motivation: Multispectral biological fluorescence microscopy has enabled the identification of multiple targets in complex samples. The accuracy in the unmixing result degrades (i) as the number of fluorophores used in any experiment increases and (ii) ... ...

Abstract	Motivation: Multispectral biological fluorescence microscopy has enabled the identification of multiple targets in complex samples. The accuracy in the unmixing result degrades (i) as the number of fluorophores used in any experiment increases and (ii) as the signal-to-noise ratio in the recorded images decreases. Further, the availability of prior knowledge regarding the expected spatial distributions of fluorophores in images of labeled cells provides an opportunity to improve the accuracy of fluorophore identification and abundance. Results: We propose a regularized sparse and low-rank Poisson regression unmixing approach (SL-PRU) to deconvolve spectral images labeled with highly overlapping fluorophores which are recorded in low signal-to-noise regimes. First, SL-PRU implements multipenalty terms when pursuing sparseness and spatial correlation of the resulting abundances in small neighborhoods simultaneously. Second, SL-PRU makes use of Poisson regression for unmixing instead of least squares regression to better estimate photon abundance. Third, we propose a method to tune the SL-PRU parameters involved in the unmixing procedure in the absence of knowledge of the ground truth abundance information in a recorded image. By validating on simulated and real-world images, we show that our proposed method leads to improved accuracy in unmixing fluorophores with highly overlapping spectra. Availability and implementation: The source code used for this article was written in MATLAB and is available with the test data at https://github.com/WANGRUOGU/SL-PRU.
MeSH term(s)	Algorithms ; Software ; Microscopy, Fluorescence/methods ; Fluorescent Dyes
Chemical Substances	Fluorescent Dyes
Language	English
Publishing date	2023-03-22
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S. ; Research Support, Non-U.S. Gov't
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btad159
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Book ; Online: A Two-Stage Framework with Self-Supervised Distillation For Cross-Domain Text Classification

Feng, Yunlong / Li, Bohan / Qin, Libo / Xu, Xiao / Che, Wanxiang

2023

Abstract: Cross-domain text classification aims to adapt models to a target domain that lacks labeled data. It leverages or reuses rich labeled data from the different but related source domain(s) and unlabeled data from the target domain. To this end, previous ... ...

Abstract	Cross-domain text classification aims to adapt models to a target domain that lacks labeled data. It leverages or reuses rich labeled data from the different but related source domain(s) and unlabeled data from the target domain. To this end, previous work focuses on either extracting domain-invariant features or task-agnostic features, ignoring domain-aware features that may be present in the target domain and could be useful for the downstream task. In this paper, we propose a two-stage framework for cross-domain text classification. In the first stage, we finetune the model with mask language modeling (MLM) and labeled data from the source domain. In the second stage, we further fine-tune the model with self-supervised distillation (SSD) and unlabeled data from the target domain. We evaluate its performance on a public cross-domain text classification benchmark and the experiment results show that our method achieves new state-of-the-art results for both single-source domain adaptations (94.17% $\uparrow$1.03%) and multi-source domain adaptations (95.09% $\uparrow$1.34%).
Keywords	Computer Science - Computation and Language ; Computer Science - Artificial Intelligence
Subject code	400
Publishing date	2023-04-18
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: OpenSLU

Qin, Libo / Chen, Qiguang / Xu, Xiao / Feng, Yunlong / Che, Wanxiang

A Unified, Modularized, and Extensible Toolkit for Spoken Language Understanding

2023

Abstract: Spoken Language Understanding (SLU) is one of the core components of a task-oriented dialogue system, which aims to extract the semantic meaning of user queries (e.g., intents and slots). In this work, we introduce OpenSLU, an open-source toolkit to ... ...

Abstract	Spoken Language Understanding (SLU) is one of the core components of a task-oriented dialogue system, which aims to extract the semantic meaning of user queries (e.g., intents and slots). In this work, we introduce OpenSLU, an open-source toolkit to provide a unified, modularized, and extensible toolkit for spoken language understanding. Specifically, OpenSLU unifies 10 SLU models for both single-intent and multi-intent scenarios, which support both non-pretrained and pretrained models simultaneously. Additionally, OpenSLU is highly modularized and extensible by decomposing the model architecture, inference, and learning process into reusable modules, which allows researchers to quickly set up SLU experiments with highly flexible configurations. OpenSLU is implemented based on PyTorch, and released at \url{https://github.com/LightChen233/OpenSLU}. Comment: ACL2023 Demo Paper
Keywords	Computer Science - Computation and Language
Subject code	420
Publishing date	2023-05-17
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: A Statistical Learning Assessment of Huber Regression

Feng, Yunlong / Wu, Qiang

2020

Abstract: As one of the triumphs and milestones of robust statistics, Huber regression plays an important role in robust inference and estimation. It has also been finding a great variety of applications in machine learning. In a parametric setup, it has been ... ...

Abstract	As one of the triumphs and milestones of robust statistics, Huber regression plays an important role in robust inference and estimation. It has also been finding a great variety of applications in machine learning. In a parametric setup, it has been extensively studied. However, in the statistical learning context where a function is typically learned in a nonparametric way, there is still a lack of theoretical understanding of how Huber regression estimators learn the conditional mean function and why it works in the absence of light-tailed noise assumptions. To address these fundamental questions, we conduct an assessment of Huber regression from a statistical learning viewpoint. First, we show that the usual risk consistency property of Huber regression estimators, which is usually pursued in machine learning, cannot guarantee their learnability in mean regression. Second, we argue that Huber regression should be implemented in an adaptive way to perform mean regression, implying that one needs to tune the scale parameter in accordance with the sample size and the moment condition of the noise. Third, with an adaptive choice of the scale parameter, we demonstrate that Huber regression estimators can be asymptotic mean regression calibrated under $(1+\epsilon)$-moment conditions ($\epsilon>0$). Last but not least, under the same moment conditions, we establish almost sure convergence rates for Huber regression estimators. Note that the $(1+\epsilon)$-moment conditions accommodate the special case where the response variable possesses infinite variance and so the established convergence rates justify the robustness feature of Huber regression estimators. In the above senses, the present study provides a systematic statistical learning assessment of Huber regression estimators and justifies their merits in terms of robustness from a theoretical viewpoint.
Keywords	Mathematics - Statistics Theory ; Computer Science - Machine Learning ; Statistics - Machine Learning
Subject code	310 ; 519
Publishing date	2020-09-27
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: A Framework of Learning Through Empirical Gain Maximization

Feng, Yunlong / Wu, Qiang

2020

Abstract: We develop in this paper a framework of empirical gain maximization (EGM) to address the robust regression problem where heavy-tailed noise or outliers may present in the response variable. The idea of EGM is to approximate the density function of the ... ...

Abstract	We develop in this paper a framework of empirical gain maximization (EGM) to address the robust regression problem where heavy-tailed noise or outliers may present in the response variable. The idea of EGM is to approximate the density function of the noise distribution instead of approximating the truth function directly as usual. Unlike the classical maximum likelihood estimation that encourages equal importance of all observations and could be problematic in the presence of abnormal observations, EGM schemes can be interpreted from a minimum distance estimation viewpoint and allow the ignorance of those observations. Furthermore, it is shown that several well-known robust nonconvex regression paradigms, such as Tukey regression and truncated least square regression, can be reformulated into this new framework. We then develop a learning theory for EGM, by means of which a unified analysis can be conducted for these well-established but not fully-understood regression approaches. Resulting from the new framework, a novel interpretation of existing bounded nonconvex loss functions can be concluded. Within this new framework, the two seemingly irrelevant terminologies, the well-known Tukey's biweight loss for robust regression and the triweight kernel for nonparametric smoothing, are closely related. More precisely, it is shown that the Tukey's biweight loss can be derived from the triweight kernel. Similarly, other frequently employed bounded nonconvex loss functions in machine learning such as the truncated square loss, the Geman-McClure loss, and the exponential squared loss can also be reformulated from certain smoothing kernels in statistics. In addition, the new framework enables us to devise new bounded nonconvex loss functions for robust learning.
Keywords	Computer Science - Machine Learning ; Statistics - Machine Learning
Subject code	519
Publishing date	2020-09-29
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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