Wray Buntine

AAAI Conference 2026 Conference Paper

Uncertainty-Based Methods for Automated Process Reward Data Construction and Output Aggregation in Mathematical Reasoning

• Jiuzhou Han
• Wray Buntine
• Ehsan Shareghi

Large language models have demonstrated remarkable capabilities in complex mathematical reasoning tasks, but they inevitably generate errors throughout multi-step solutions. Process-level Reward Models (PRMs) have shown great promise by providing supervision and evaluation at each intermediate step, thereby effectively improving the models’ reasoning abilities. However, training effective PRMs requires high-quality process reward data, yet existing methods for constructing such data are often labour-intensive or inefficient. In this paper, we propose an uncertainty-driven framework for automated process reward data construction, encompassing both data generation and annotation processes for PRMs. Additionally, we identify the limitations of both majority vote and PRMs, and introduce two generic uncertainty-aware output aggregation methods: Hybrid Majority Reward Vote and Weighted Reward Frequency Vote, which combine the strengths of majority vote with PRMs. Extensive experiments on ProcessBench, MATH, and GSMPlus show the effectiveness and efficiency of the proposed PRM data construction framework, and demonstrate that the two output aggregation methods further improve the mathematical reasoning abilities across diverse PRMs.

AAAI Conference 2024 Conference Paper

Harnessing the Power of Beta Scoring in Deep Active Learning for Multi-Label Text Classification

• Wei Tan
• Ngoc Dang Nguyen
• Lan Du
• Wray Buntine

Within the scope of natural language processing, the domain of multi-label text classification is uniquely challenging due to its expansive and uneven label distribution. The complexity deepens due to the demand for an extensive set of annotated data for training an advanced deep learning model, especially in specialized fields where the labeling task can be labor-intensive and often requires domain-specific knowledge. Addressing these challenges, our study introduces a novel deep active learning strategy, capitalizing on the Beta family of proper scoring rules within the Expected Loss Reduction framework. It computes the expected increase in scores using the Beta Scoring Rules, which are then transformed into sample vector representations. These vector representations guide the diverse selection of informative sample, directly linking this process to the model's expected proper score. Comprehensive evaluations across both synthetic and real datasets reveal our method's capability to often outperform established acquisition techniques in multi-label text classification, presenting encouraging outcomes across various architectural and dataset scenarios.

IJCAI Conference 2023 Conference Paper

A Survey on Out-of-Distribution Evaluation of Neural NLP Models

• Xinzhe Li
• Ming Liu
• Shang Gao
• Wray Buntine

Adversarial robustness, domain generalization and dataset biases are three active lines of research contributing to out-of-distribution (OOD) evaluation on neural NLP models. However, a comprehensive, integrated discussion of the three research lines is still lacking in the literature. This survey will 1) compare the three lines of research under a unifying definition; 2) summarize their data-generating processes and evaluation protocols for each line of research; and 3) emphasize the challenges and opportunities for future work.

AAAI Conference 2023 Conference Paper

AUC Maximization for Low-Resource Named Entity Recognition

• Ngoc Dang Nguyen
• Wei Tan
• Lan Du
• Wray Buntine
• Richard Beare
• Changyou Chen

Current work in named entity recognition (NER) uses either cross entropy (CE) or conditional random fields (CRF) as the objective/loss functions to optimize the underlying NER model. Both of these traditional objective functions for the NER problem generally produce adequate performance when the data distribution is balanced and there are sufficient annotated training examples. But since NER is inherently an imbalanced tagging problem, the model performance under the low-resource settings could suffer using these standard objective functions. Based on recent advances in area under the ROC curve (AUC) maximization, we propose to optimize the NER model by maximizing the AUC score. We give evidence that by simply combining two binary-classifiers that maximize the AUC score, significant performance improvement over traditional loss functions is achieved under low-resource NER settings. We also conduct extensive experiments to demonstrate the advantages of our method under the low-resource and highly-imbalanced data distribution settings. To the best of our knowledge, this is the first work that brings AUC maximization to the NER setting. Furthermore, we show that our method is agnostic to different types of NER embeddings, models and domains. The code of this work is available at https://github.com/dngu0061/NER-AUC-2T.

AAAI Conference 2023 Conference Paper

Cross-Domain Graph Anomaly Detection via Anomaly-Aware Contrastive Alignment

• Qizhou Wang
• Guansong Pang
• Mahsa Salehi
• Wray Buntine
• Christopher Leckie

Cross-domain graph anomaly detection (CD-GAD) describes the problem of detecting anomalous nodes in an unlabelled target graph using auxiliary, related source graphs with labelled anomalous and normal nodes. Although it presents a promising approach to address the notoriously high false positive issue in anomaly detection, little work has been done in this line of research. There are numerous domain adaptation methods in the literature, but it is difficult to adapt them for GAD due to the unknown distributions of the anomalies and the complex node relations embedded in graph data. To this end, we introduce a novel domain adaptation approach, namely Anomaly-aware Contrastive alignmenT (ACT), for GAD. ACT is designed to jointly optimise: (i) unsupervised contrastive learning of normal representations of nodes in the target graph, and (ii) anomaly-aware one-class alignment that aligns these contrastive node representations and the representations of labelled normal nodes in the source graph, while enforcing significant deviation of the representations of the normal nodes from the labelled anomalous nodes in the source graph. In doing so, ACT effectively transfers anomaly-informed knowledge from the source graph to learn the complex node relations of the normal class for GAD on the target graph without any specification of the anomaly distributions. Extensive experiments on eight CD-GAD settings demonstrate that our approach ACT achieves substantially improved detection performance over 10 state-of-the-art GAD methods. Code is available at https://github.com/QZ-WANG/ACT.

NeurIPS Conference 2021 Conference Paper

Diversity Enhanced Active Learning with Strictly Proper Scoring Rules

• Wei Tan
• Lan Du
• Wray Buntine

We study acquisition functions for active learning (AL) for text classification. The Expected Loss Reduction (ELR) method focuses on a Bayesian estimate of the reduction in classification error, recently updated with Mean Objective Cost of Uncertainty (MOCU). We convert the ELR framework to estimate the increase in (strictly proper) scores like log probability or negative mean square error, which we call Bayesian Estimate of Mean Proper Scores (BEMPS). We also prove convergence results borrowing techniques used with MOCU. In order to allow better experimentation with the new acquisition functions, we develop a complementary batch AL algorithm, which encourages diversity in the vector of expected changes in scores for unlabelled data. To allow high performance text classifiers, we combine ensembling and dynamic validation set construction on pretrained language models. Extensive experimental evaluation then explores how these different acquisition functions perform. The results show that the use of mean square error and log probability with BEMPS yields robust acquisition functions, which consistently outperform the others tested.

IJCAI Conference 2021 Conference Paper

Topic Modelling Meets Deep Neural Networks: A Survey

• He Zhao
• Dinh Phung
• Viet Huynh
• Yuan Jin
• Lan Du
• Wray Buntine

Topic modelling has been a successful technique for text analysis for almost twenty years. When topic modelling met deep neural networks, there emerged a new and increasingly popular research area, neural topic models, with nearly a hundred models developed and a wide range of applications in neural language understanding such as text generation, summarisation and language models. There is a need to summarise research developments and discuss open problems and future directions. In this paper, we provide a focused yet comprehensive overview of neural topic models for interested researchers in the AI community, so as to facilitate them to navigate and innovate in this fast-growing research area. To the best of our knowledge, ours is the first review on this specific topic.

NeurIPS Conference 2018 Conference Paper

Dirichlet belief networks for topic structure learning

• He Zhao
• Lan Du
• Wray Buntine
• Mingyuan Zhou

Recently, considerable research effort has been devoted to developing deep architectures for topic models to learn topic structures. Although several deep models have been proposed to learn better topic proportions of documents, how to leverage the benefits of deep structures for learning word distributions of topics has not yet been rigorously studied. Here we propose a new multi-layer generative process on word distributions of topics, where each layer consists of a set of topics and each topic is drawn from a mixture of the topics of the layer above. As the topics in all layers can be directly interpreted by words, the proposed model is able to discover interpretable topic hierarchies. As a self-contained module, our model can be flexibly adapted to different kinds of topic models to improve their modelling accuracy and interpretability. Extensive experiments on text corpora demonstrate the advantages of the proposed model.

NeurIPS Conference 2011 Conference Paper

Improving Topic Coherence with Regularized Topic Models

• David Newman
• Edwin Bonilla
• Wray Buntine

Topic models have the potential to improve search and browsing by extracting useful semantic themes from web pages and other text documents. When learned topics are coherent and interpretable, they can be valuable for faceted browsing, results set diversity analysis, and document retrieval. However, when dealing with small collections or noisy text (e. g. web search result snippets or blog posts), learned topics can be less coherent, less interpretable, and less useful. To overcome this, we propose two methods to regularize the learning of topic models. Our regularizers work by creating a structured prior over words that reflect broad patterns in the external data. Using thirteen datasets we show that both regularizers improve topic coherence and interpretability while learning a faithful representation of the collection of interest. Overall, this work makes topic models more useful across a broader range of text data.

NeurIPS Conference 2010 Conference Paper

Word Features for Latent Dirichlet Allocation

• James Petterson
• Wray Buntine
• Shravan Narayanamurthy
• Tibério Caetano
• Alex Smola

We extend Latent Dirichlet Allocation (LDA) by explicitly allowing for the encoding of side information in the distribution over words. This results in a variety of new capabilities, such as improved estimates for infrequently occurring words, as well as the ability to leverage thesauri and dictionaries in order to boost topic cohesion within and across languages. We present experiments on multi-language topic synchronisation where dictionary information is used to bias corresponding words towards similar topics. Results indicate that our model substantially improves topic cohesion when compared to the standard LDA model.

NeurIPS Conference 2002 Conference Paper

Automatic Derivation of Statistical Algorithms: The EM Family and Beyond

• Bernd Fischer
• Johann Schumann
• Wray Buntine
• Alexander Gray

Machine learning has reached a point where many probabilistic meth- ods can be understood as variations, extensions and combinations of a much smaller set of abstract themes, e. g. , as different instances of the EM algorithm. This enables the systematic derivation of algorithms cus- tomized for different models. Here, we describe the AUTO BAYES sys- tem which takes a high-level statistical model specification, uses power- ful symbolic techniques based on schema-based program synthesis and computer algebra to derive an efficient specialized algorithm for learning that model, and generates executable code implementing that algorithm. This capability is far beyond that of code collections such as Matlab tool- boxes or even tools for model-independent optimization such as BUGS for Gibbs sampling: complex new algorithms can be generated with- out new programming, algorithms can be highly specialized and tightly crafted for the exact structure of the model and data, and efficient and commented code can be generated for different languages or systems. We present automatically-derived algorithms ranging from closed-form solutions of Bayesian textbook problems to recently-proposed EM algo- rithms for clustering, regression, and a multinomial form of PCA. 1 Automatic Derivation of Statistical Algorithms Overview. We describe a symbolic program synthesis system which works as a “statistical algorithm compiler: ” it compiles a statistical model specification into a custom algorithm design and from that further down into a working program implementing the algorithm design. This system, AUTOBAYES, can be loosely thought of as “part theorem prover, part Mathematica, part learning textbook, and part Numerical Recipes. ” It provides much more flexibility than a fixed code repository such as a Matlab toolbox, and allows the creation of efficient algorithms which have never before been implemented, or even written down. AUTOBAYES is intended to automate the more routine application of complex methods in novel contexts. For example, recent multinomial extensions to PCA [2, 4] can be derived in this way.
The algorithm design problem. Given a dataset and a task, creating a learning method can be characterized by two main questions: 1. What is the model? 2. What algorithm will optimize the model parameters? The statistical algorithm (i. e. , a parameter optimization algorithm for the statistical model) can then be implemented manually. The system in this paper answers the algorithm question given that the user has chosen a model for the data, and continues through to implementation. Performing this task at the state-of-the-art level requires an intertwined meld of probability theory, computational mathematics, and software engineering. However, a number of factors unite to allow us to solve the algorithm design problem computationally: 1. The existence of fundamental building blocks (e. g. , standardized probability distributions, standard optimization procedures, and generic data structures). 2. The existence of common representations (i. e. , graphical models [3, 13] and program schemas). 3. The formalization of schema applicability constraints as guards. 1 The challenges of algorithm design. The design problem has an inherently combinatorial nature, since subparts of a function may be optimized recursively and in different ways. It also involves the use of new data structures or approximations to gain performance. As the research in statistical algorithms advances, its creative focus should move beyond the ultimately mechanical aspects and towards extending the abstract applicability of already existing schemas (algorithmic principles like EM), improving schemas in ways that gener- alize across anything they can be applied to, and inventing radically new schemas. 2 Combining Schema-based Synthesis and Bayesian Networks with 0 < n_points; 1 model mog as ’Mixture of Gaussians’; with 0 < n classes with n classes << n_points; with 1 = sum(I: = 1. .n_classes, phi(I)); 7 double phi(1. .n classes) as ’weights’ 8 9 double mu(1. .n classes); 9 double sigma(1. .n_classes); 2 const int n points as ’nr. of data points’ 3 4 const int n classes: = 3 as ’nr. classes’ 5 6 Statistical Models. Externally, AUTOBAYES has the look and feel of a compiler. Users specify their model of interest in a high-level specification language (as opposed to a program- ming language). The figure shows the specification of the mixture of Gaus- sians example used throughout this paper. 2 Note the constraint that the sum of the class probabilities must equal one (line 8) along with others (lines 3 and 5) that make optimization of the model well-defined. Also note the ability to specify assumptions of the kind in line 6, which may be used by some algorithms. The last line specifies the goal 10 int c(1. .n points) as ’class labels’; 11 c ˜ disc(vec(I: = 1. .n classes, phi(I))); 12 data double x(1. .n_points) as ’data’; 13 x(I) ˜ gauss(mu(c(I)), sigma(c(I))); 14 max pr(x| phi, mu, sigma
) wrt phi, mu, sigma
;  inference task: maximize the conditional probability pr rameters 

IJCAI Conference 1991 Conference Paper

Classifiers: A Theoretical and Empirical Study

• Wray Buntine

This paper describes how a competitive tree learning algorithm can be derived from first principles. The algorithm approximates the Bayesian decision theoretic solution to the learning task. Comparative experiments w i t h the algorithm and the several mature AI and statistical families of tree learning algorithms currently in use show the derived Bayesian algorithm is consistently as good or better, although sometimes at computational cost. Using the same strategy, we can design algorithms for many other supervised and model learning tasks given just a probabilistic representation for the kind of knowledge to be learned. As an illustration, a second learning algorithm is derived for learning Bayesian networks from data. Implications to incremental learning and the use of multiple models are also discussed.

AAAI Conference 1990 Conference Paper

Myths and Legends in Learning Classification Rules

• Wray Buntine

This paper is a discussion of machine learning theory on empirically learning classification rules. The paper proposes six myths in the machine learning community that address issues of bias, learning as search, computational learning theory, Occam’ s razor, “universal” learning algorithms, and interactive learning. Some of the problems raised are also nddrcssed from a Bayesian perspective. The paper concludes by suggesting questions that machine learning researchers should be addressing both theoretically and experimentally.

IJCAI Conference 1989 Conference Paper

A Critique of the Valiant Model

• Wray Buntine

AIJ Journal 1988 Journal Article

Generalized subsumption and its applications to induction and redundancy

• Wray Buntine