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Weiwei Cheng

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12 papers
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12

JBHI Journal 2026 Journal Article

NeuroCLIP: A Multimodal Contrastive Learning Method for rTMS-treated Methamphetamine Addiction Analysis

  • Chengkai Wang
  • Di Wu
  • Yunsheng Liao
  • Wenyao Zheng
  • Ziyi Zeng
  • Xurong Gao
  • Hemmings Wu
  • Zhoule Zhu

Methamphetamine dependence poses a significant global health challenge, yet its assessment and the evaluation of treatments like repetitive transcranial magnetic stimulation (rTMS) frequently depend on subjective self-reports, which may introduce uncertainties. While objective neuroimaging modalities such as electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) offer alternatives, their individual limitations and the reliance on conventional, often hand-crafted, feature extraction can compromise the reliability of derived biomarkers. To overcome these limitations, we propose NeuroCLIP, a novel deep learning framework integrating simultaneously recorded EEG and fNIRS data through a progressive learning strategy. This approach offers a robust and trustworthy data-driven biomarker for methamphetamine addiction. Validation experiments show that NeuroCLIP significantly improves discriminative capabilities among the methamphetamine-dependent individuals and healthy controls compared to models using either EEG or only fNIRS alone. Furthermore, the proposed framework facilitates objective, brain-based evaluation of rTMS treatment efficacy, demonstrating measurable shifts in neural patterns towards healthy control profiles after treatment. Critically, we establish the trustworthiness of the multimodal data-driven biomarker by showing its strong correlation with psychometrically validated craving scores. These findings suggest that biomarker derived from EEG-fNIRS data via NeuroCLIP offers enhanced robustness and reliability over single-modality approaches, providing a valuable tool for addiction neuroscience research and potentially improving clinical assessments.

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JMLR Journal 2014 Journal Article

On the Bayes-Optimality of F-Measure Maximizers

  • Willem Waegeman
  • Krzysztof Dembczyński
  • Arkadiusz Jachnik
  • Weiwei Cheng
  • Eyke Hüllermeier

The F-measure, which has originally been introduced in information retrieval, is nowadays routinely used as a performance metric for problems such as binary classification, multi-label classification, and structured output prediction. Optimizing this measure is a statistically and computationally challenging problem, since no closed-form solution exists. Adopting a decision-theoretic perspective, this article provides a formal and experimental analysis of different approaches for maximizing the F-measure. We start with a Bayes-risk analysis of related loss functions, such as Hamming loss and subset zero-one loss, showing that optimizing such losses as a surrogate of the F-measure leads to a high worst-case regret. Subsequently, we perform a similar type of analysis for F-measure maximizing algorithms, showing that such algorithms are approximate, while relying on additional assumptions regarding the statistical distribution of the binary response variables. Furthermore, we present a new algorithm which is not only computationally efficient but also Bayes-optimal, regardless of the underlying distribution. To this end, the algorithm requires only a quadratic (with respect to the number of binary responses) number of parameters of the joint distribution. We illustrate the practical performance of all analyzed methods by means of experiments with multi-label classification problems. [abs] [ pdf ][ bib ] &copy JMLR 2014. ( edit, beta )

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IJCAI Conference 2013 Conference Paper

Preference-Based CBR: General Ideas and Basic Principles

  • Eyke Hüllermeier
  • Weiwei Cheng

Building on recent research on preference handling in artificial intelligence and related fields, our goal is to develop a coherent and generic methodological framework for case-based reasoning (CBR) on the basis of formal concepts and methods for knowledge representation and reasoning with preferences. A preference-based approach to CBR appears to be appealing for several reasons, notably because case-based experiences naturally lend themselves to representations in terms of preference or order relations. Moreover, the flexibility and expressiveness of a preference-based formalism well accommodate the uncertain and approximate nature of case-based problem solving. In this paper, we outline the basic ideas of preferencebased CBR and sketch a formal framework for realizing these ideas.

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ICML Conference 2013 Conference Paper

Top-k Selection based on Adaptive Sampling of Noisy Preferences

  • Róbert Busa-Fekete
  • Balázs Szörényi
  • Weiwei Cheng
  • Paul Weng
  • Eyke Hüllermeier

We consider the problem of reliably selecting an optimal subset of fixed size from a given set of choice alternatives, based on noisy information about the quality of these alternatives. Problems of similar kind have been tackled by means of adaptive sampling schemes called racing algorithms. However, in contrast to existing approaches, we do not assume that each alternative is characterized by a real-valued random variable, and that samples are taken from the corresponding distributions. Instead, we only assume that alternatives can be compared in terms of pairwise preferences. We propose and formally analyze a general preference-based racing algorithm that we instantiate with three specific ranking procedures and corresponding sampling schemes. Experiments with real and synthetic data are presented to show the efficiency of our approach.

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NeurIPS Conference 2012 Conference Paper

Label Ranking with Partial Abstention based on Thresholded Probabilistic Models

  • Weiwei Cheng
  • Eyke Hüllermeier
  • Willem Waegeman
  • Volkmar Welker

Several machine learning methods allow for abstaining from uncertain predictions. While being common for settings like conventional classification, abstention has been studied much less in learning to rank. We address abstention for the label ranking setting, allowing the learner to declare certain pairs of labels as being incomparable and, thus, to predict partial instead of total orders. In our method, such predictions are produced via thresholding the probabilities of pairwise preferences between labels, as induced by a predicted probability distribution on the set of all rankings. We formally analyze this approach for the Mallows and the Plackett-Luce model, showing that it produces proper partial orders as predictions and characterizing the expressiveness of the induced class of partial orders. These theoretical results are complemented by experiments demonstrating the practical usefulness of the approach.

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NeurIPS Conference 2011 Conference Paper

An Exact Algorithm for F-Measure Maximization

  • Krzysztof Dembczynski
  • Willem Waegeman
  • Weiwei Cheng
  • Eyke Hüllermeier

The F-measure, originally introduced in information retrieval, is nowadays routinely used as a performance metric for problems such as binary classification, multi-label classification, and structured output prediction. Optimizing this measure remains a statistically and computationally challenging problem, since no closed-form maximizer exists. Current algorithms are approximate and typically rely on additional assumptions regarding the statistical distribution of the binary response variables. In this paper, we present an algorithm which is not only computationally efficient but also exact, regardless of the underlying distribution. The algorithm requires only a quadratic number of parameters of the joint distribution (with respect to the number of binary responses). We illustrate its practical performance by means of experimental results for multi-label classification.

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