Christopher Leckie

NeurIPS Conference 2025 Conference Paper

Fortifying Time Series: DTW-Certified Robust Anomaly Detection

• Shijie Liu
• Tansu Alpcan
• Christopher Leckie
• Sarah Erfani

Time-series anomaly detection is critical for ensuring safety in high-stakes applications, where robustness is a fundamental requirement rather than a mere performance metric. Addressing the vulnerability of these systems to adversarial manipulation is therefore essential. Existing defenses are largely heuristic or provide certified robustness only under $\ell_p$-norm constraints, which are incompatible with time-series data. In particular, $\ell_p$-norm fails to capture the intrinsic temporal structure in time series, causing small temporal distortions to significantly alter the $\ell_p$-norm measures. Instead, the similarity metric Dynamic Time Warping (DTW) is more suitable and widely adopted in the time-series domain, as DTW accounts for temporal alignment and remains robust to temporal variations. To date, however, there has been no certifiable robustness result in this metric that provides guarantees. In this work, we introduce the first DTW-certified robust defense in time-series anomaly detection by adapting the randomized smoothing paradigm. We develop this certificate by bridging the $\ell_p$-norm to DTW distance through a lower-bound transformation. Extensive experiments across various datasets and models validate the effectiveness and practicality of our theoretical approach. Results demonstrate significantly improved performance, e. g. , up to 18. 7\% in F1-score under DTW-based adversarial attacks compared to traditional certified models.

AAMAS Conference 2025 Conference Paper

Local Anomaly Detection with Partial Observation in Multi-agent Systems as a Data Matching Game

• Zixin Ye
• Tansu Alpcan
• Christopher Leckie

Local anomaly detection in a multi-agent system is a pervasive but challenging problem. The challenge entails how agents with heterogeneous objectives and partial data collection train local anomaly detectors for heterogeneous domain-specific tasks. This paper proposes a distributed training method to address this question. Our approach involves a game-theoretic framework to address agents’ heterogeneous objectives and a transformer-based model to handle partial data observation. Our game, conditionally proven as a potential game, guides agents under the same local objectives into a data-sharing group for local training. Compared to other topperforming SOTAs, our evaluation outcomes empirically reflect the efficiency and robustness of our method in multi-agent scenarios.

ICLR Conference 2025 Conference Paper

Open-Set Graph Anomaly Detection via Normal Structure Regularisation

• Qizhou Wang 0001
• Guansong Pang
• Mahsa Salehi
• Xiaokun Xia
• Christopher Leckie

This paper considers an important Graph Anomaly Detection (GAD) task, namely open-set GAD, which aims to train a detection model using a small number of normal and anomaly nodes (referred to as *seen anomalies*) to detect both seen anomalies and *unseen anomalies* (*i.e*., anomalies that cannot be illustrated the training anomalies). Those labelled training data provide crucial prior knowledge about abnormalities for GAD models, enabling substantially reduced detection errors. However, current supervised GAD methods tend to over-emphasise fitting the seen anomalies, leading to many errors of detecting the unseen anomalies as normal nodes. Further, existing open-set AD models were introduced to handle Euclidean data, failing to effectively capture discriminative features from graph structure and node attributes for GAD. In this work, we propose a novel open-set GAD approach, namely $\underline{n}ormal$ $\underline{s}tructure$ $\underline{reg}ularisation$ (**NSReg**), to achieve generalised detection ability to unseen anomalies, while maintaining its effectiveness on detecting seen anomalies. The key idea in NSReg is to introduce a regularisation term that enforces the learning of compact, semantically-rich representations of normal nodes based on their structural relations to other nodes. When being optimised with supervised anomaly detection losses, the regularisation term helps incorporate strong normality into the modelling, and thus, it effectively avoids over-fitting the seen anomalies and learns a better normality decision boundary, largely reducing the false negatives of detecting unseen anomalies as normal. Extensive empirical results on seven real-world datasets show that NSReg significantly outperforms state-of-the-art competing methods by at least 14% AUC-ROC on the unseen anomaly classes and by 10% AUC-ROC on all anomaly classes. Code and datasets are available at https://github.com/mala-lab/NSReg.

ECAI Conference 2024 Conference Paper

Be Persistent: Towards a Unified Solution for Mitigating Shortcuts in Deep Learning

• Hadi M. Dolatabadi
• Sarah Monazam Erfani
• Christopher Leckie

Deep neural networks (DNNs) are vulnerable to shortcut learning: rather than learning the intended task, they tend to draw inconclusive relationships between their inputs and outputs. Shortcut learning is ubiquitous among many failure cases of neural networks, and traces of this phenomenon can be seen in their generalizability issues, domain shift, adversarial vulnerability, and even bias towards majority groups. In this paper, we argue that this commonality in the cause of various DNN issues creates a significant opportunity that should be leveraged to find a unified solution for shortcut learning. To this end, we outline the recent advances in topological data analysis (TDA), and persistent homology (PH) in particular, to sketch a unified roadmap for detecting shortcuts in deep learning. We demonstrate our arguments by investigating the topological features of computational graphs in DNNs using two cases of unlearnable examples and bias in decision-making as our test studies. Our analysis of these two failure cases of DNNs reveals that finding a unified solution for shortcut learning in DNNs is not out of reach, and TDA can play a significant role in forming such a framework.

ECAI Conference 2024 Conference Paper

DeCoRTAD: Diffusion Based Conditional Representation Learning for Online Trajectory Anomaly Detection

• Chen Wang 0131
• Sarah Monazam Erfani
• Tansu Alpcan
• Christopher Leckie

Online trajectory anomaly detection has become a critical task in many real-world applications. However, most existing works assume anomalies are significantly different from normal patterns or require knowing the destinations in advance. In this work, we focus on the problem of detecting anomalous subtrajectories in an online manner without knowing their destinations. This task presents a significant challenge as anomalous subtrajectories may largely overlap with normal trajectories, and we only have limited information on an ongoing trajectory during online detection. To overcome the limitations of current methods, we propose a novel diffusion-based conditional representation learning for online trajectory anomaly detection (DeCoRTAD), that aims to detect anomalies at the representation level, thereby improving computational efficiency. Our framework integrates a diffusion model with two encoders: one for capturing current information and another for encoding historical context. By conditioning the current encoder on the history encoder, we leverage past information as prior knowledge to achieve a more meaningful and compact latent space representation. Our method excels in capturing the normal representation in highly diverse trajectory data, therefore achieving great performance in online detection of fine-grained anomalies. Our experiments show that DeCoRTAD can achieve outstanding online anomaly detection performance in F1 score with an average improvement of 9. 31%, and a maximum improvement of 22% over comparable baselines.

AAMAS Conference 2024 Conference Paper

Detecting Anomalous Agent Decision Sequences Based on Offline Imitation Learning

• Chen Wang
• Sarah Erfani
• Tansu Alpcan
• Christopher Leckie

Anomaly detection in decision-making sequences is a challenging problem due to the complexity of normality representation learning, the sequential nature of the task and the difficulty of realworld implementation. In this work, we propose extracting two behaviour features: action optimality and sequential association to detect anomalous behaviour. Our offline imitation learning model is an adaptation of behavioural cloning with a transformer policy network, where we modify the training process to learn a Q function and a state value function from normal trajectories.

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.

AAAI Conference 2022 Conference Paper

A Divide and Conquer Algorithm for Predict+Optimize with Non-convex Problems

• Ali Ugur Guler
• Emir Demirović
• Jeffrey Chan
• James Bailey
• Christopher Leckie
• Peter J. Stuckey

The predict+optimize problem combines machine learning and combinatorial optimization by predicting the problem coefficients first and then using these coefficients to solve the optimization problem. While this problem can be solved in two separate stages, recent research shows end to end models can achieve better results. This requires differentiating through a discrete combinatorial function. Models that use differentiable surrogates are prone to approximation errors, while existing exact models are limited to dynamic programming, or they do not generalize well with scarce data. In this work we propose a novel divide and conquer algorithm based on transition points to reason over exact optimization problems and predict the coefficients using the optimization loss. Moreover, our model is not limited to dynamic programming problems. We also introduce a greedy version, which achieves similar results with less computation. In comparison with other predict+optimize frameworks, we show our method outperforms existing exact frameworks and can reason over hard combinatorial problems better than surrogate methods.

JMLR Journal 2022 Journal Article

MurTree: Optimal Decision Trees via Dynamic Programming and Search

• Emir Demirović
• Anna Lukina
• Emmanuel Hebrard
• Jeffrey Chan
• James Bailey
• Christopher Leckie
• Kotagiri Ramamohanarao
• Peter J. Stuckey

Decision tree learning is a widely used approach in machine learning, favoured in applications that require concise and interpretable models. Heuristic methods are traditionally used to quickly produce models with reasonably high accuracy. A commonly criticised point, however, is that the resulting trees may not necessarily be the best representation of the data in terms of accuracy and size. In recent years, this motivated the development of optimal classification tree algorithms that globally optimise the decision tree in contrast to heuristic methods that perform a sequence of locally optimal decisions. We follow this line of work and provide a novel algorithm for learning optimal classification trees based on dynamic programming and search. Our algorithm supports constraints on the depth of the tree and number of nodes. The success of our approach is attributed to a series of specialised techniques that exploit properties unique to classification trees. Whereas algorithms for optimal classification trees have traditionally been plagued by high runtimes and limited scalability, we show in a detailed experimental study that our approach uses only a fraction of the time required by the state-of-the-art and can handle datasets with tens of thousands of instances, providing several orders of magnitude improvements and notably contributing towards the practical use of optimal decision trees. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2022. ( edit, beta )

AAAI Conference 2022 Conference Paper

Noise-Robust Learning from Multiple Unsupervised Sources of Inferred Labels

• Amila Silva
• Ling Luo
• Shanika Karunasekera
• Christopher Leckie

Deep Neural Networks (DNNs) generally require large-scale datasets for training. Since manually obtaining clean labels for large datasets is extremely expensive, unsupervised models based on domain-specific heuristics can be used to efficiently infer the labels for such datasets. However, the labels from such inferred sources are typically noisy, which could easily mislead and lessen the generalizability of DNNs. Most approaches proposed in the literature to address this problem assume the label noise depends only on the true class of an instance (i. e. , class-conditional noise). However, this assumption is not realistic for the inferred labels as they are typically inferred based on the features of the instances. The few recent attempts to model such instance-dependent (i. e. , feature-dependent) noise require auxiliary information about the label noise (e. g. , noise rates or clean samples). This work proposes a theoretically motivated framework to correct label noise in the presence of multiple labels inferred from unsupervised models. The framework consists of two modules: (1) MULTI-IDNC, a novel approach to correct label noise that is instance-dependent yet not class-conditional; (2) MULTI- CCNC, which extends an existing class-conditional noiserobust approach to yield improved class-conditional noise correction using multiple noisy label sources. We conduct experiments using nine real-world datasets for three different classification tasks (images, text and graph nodes). Our results show that our approach achieves notable improvements (e. g. , 6. 4% in accuracy) against state-of-the-art baselines while dealing with both instance-dependent and classconditional noise in inferred label sources.

IJCAI Conference 2021 Conference Paper

Closing the BIG-LID: An Effective Local Intrinsic Dimensionality Defense for Nonlinear Regression Poisoning

• Sandamal Weerasinghe
• Tamas Abraham
• Tansu Alpcan
• Sarah M. Erfani
• Christopher Leckie
• Benjamin I. P. Rubinstein

Nonlinear regression, although widely used in engineering, financial and security applications for automated decision making, is known to be vulnerable to training data poisoning. Targeted poisoning attacks may cause learning algorithms to fit decision functions with poor predictive performance. This paper presents a new analysis of local intrinsic dimensionality (LID) of nonlinear regression under such poisoning attacks within a Stackelberg game, leading to a practical defense. After adapting a gradient-based attack on linear regression that significantly impairs prediction capabilities to nonlinear settings, we consider a multi-step unsupervised black-box defense. The first step identifies samples that have the greatest influence on the learner's validation error; we then use the theory of local intrinsic dimensionality, which reveals the degree of being an outlier of data samples, to iteratively identify poisoned samples via a generative probabilistic model, and suppress their influence on the prediction function. Empirical validation demonstrates superior performance compared to a range of recent defenses.

AAAI Conference 2021 Conference Paper

Embracing Domain Differences in Fake News: Cross-domain Fake News Detection using Multi-modal Data

• Amila Silva
• Ling Luo
• Shanika Karunasekera
• Christopher Leckie

With the rapid evolution of social media, fake news has become a significant social problem, which cannot be addressed in a timely manner using manual investigation. This has motivated numerous studies on automating fake news detection. Most studies explore supervised training models with different modalities (e. g. , text, images, and propagation networks) of news records to identify fake news. However, the performance of such techniques generally drops if news records are coming from different domains (e. g. , politics, entertainment), especially for domains that are unseen or rarely-seen during training. As motivation, we empirically show that news records from different domains have significantly different word usage and propagation patterns. Furthermore, due to the sheer volume of unlabelled news records, it is challenging to select news records for manual labelling so that the domain-coverage of the labelled dataset is maximized. Hence, this work: (1) proposes a novel framework that jointly preserves domain-specific and cross-domain knowledge in news records to detect fake news from different domains; and (2) introduces an unsupervised technique to select a set of unlabelled informative news records for manual labelling, which can be ultimately used to train a fake news detection model that performs well for many domains while minimizing the labelling cost. Our experiments show that the integration of the proposed fake news model and the selective annotation approach achieves state-of-the-art performance for cross-domain news datasets, while yielding notable improvements for rarely-appearing domains in news datasets.

NeurIPS Conference 2020 Conference Paper

AdvFlow: Inconspicuous Black-box Adversarial Attacks using Normalizing Flows

• Hadi Mohaghegh Dolatabadi
• Sarah Erfani
• Christopher Leckie

Deep learning classifiers are susceptible to well-crafted, imperceptible variations of their inputs, known as adversarial attacks. In this regard, the study of powerful attack models sheds light on the sources of vulnerability in these classifiers, hopefully leading to more robust ones. In this paper, we introduce AdvFlow: a novel black-box adversarial attack method on image classifiers that exploits the power of normalizing flows to model the density of adversarial examples around a given target image. We see that the proposed method generates adversaries that closely follow the clean data distribution, a property which makes their detection less likely. Also, our experimental results show competitive performance of the proposed approach with some of the existing attack methods on defended classifiers.

AAAI Conference 2020 Conference Paper

Dynamic Programming for Predict+Optimise

• Emir Demirovi?
• Peter J. Stuckey
• Tias Guns
• James Bailey
• Christopher Leckie
• Kotagiri Ramamohanarao
• Jeffrey Chan

We study the predict+optimise problem, where machine learning and combinatorial optimisation must interact to achieve a common goal. These problems are important when optimisation needs to be performed on input parameters that are not fully observed but must instead be estimated using machine learning. We provide a novel learning technique for predict+optimise to directly reason about the underlying combinatorial optimisation problem, offering a meaningful integration of machine learning and optimisation. This is done by representing the combinatorial problem as a piecewise linear function parameterised by the coefficients of the learning model and then iteratively performing coordinate descent on the learning coefficients. Our approach is applicable to linear learning functions and any optimisation problem solvable by dynamic programming. We illustrate the effectiveness of our approach on benchmarks from the literature.

IJCAI Conference 2019 Conference Paper

Predict+Optimise with Ranking Objectives: Exhaustively Learning Linear Functions

• Emir Demirovic
• Peter J. Stuckey
• James Bailey
• Jeffrey Chan
• Christopher Leckie
• Kotagiri Ramamohanarao
• Tias Guns

We study the predict+optimise problem, where machine learning and combinatorial optimisation must interact to achieve a common goal. These problems are important when optimisation needs to be performed on input parameters that are not fully observed but must instead be estimated using machine learning. Our contributions are two-fold: 1) we provide theoretical insight into the properties and computational complexity of predict+optimise problems in general, and 2) develop a novel framework that, in contrast to related work, guarantees to compute the optimal parameters for a linear learning function given any ranking optimisation problem. We illustrate the applicability of our framework for the particular case of the unit-weighted knapsack predict+optimise problem and evaluate on benchmarks from the literature.

AAAI Conference 2017 Conference Paper

From Shared Subspaces to Shared Landmarks: A Robust Multi-Source Classification Approach

• Sarah Erfani
• Mahsa Baktashmotlagh
• Masud Moshtaghi
• Vinh Nguyen
• Christopher Leckie
• James Bailey
• Kotagiri Ramamohanarao

Training machine leaning algorithms on augmented data from different related sources is a challenging task. This problem arises in several applications, such as the Internet of Things (IoT), where data may be collected from devices with different settings. The learned model on such datasets can generalize poorly due to distribution bias. In this paper we consider the problem of classifying unseen datasets, given several labeled training samples drawn from similar distributions. We exploit the intrinsic structure of samples in a latent subspace and identify landmarks, a subset of training instances from different sources that should be similar. Incorporating subspace learning and landmark selection enhances generalization by alleviating the impact of noise and outliers, as well as improving efficiency by reducing the size of the data. However, since addressing the two issues simultaneously results in an intractable problem, we relax the objective function by leveraging the theory of nonlinear projection and solve a tractable convex optimisation. Through comprehensive analysis, we show that our proposed approach outperforms stateof-the-art results on several benchmark datasets, while keeping the computational complexity low.

IJCAI Conference 2016 Conference Paper

Robust Domain Generalisation by Enforcing Distribution Invariance

• Sarah M. Erfani
• Mahsa Baktashmotlagh
• Masud Moshtaghi
• Vinh Nguyen
• Christopher Leckie
• James Bailey
• Kotagiri Ramamohanarao

Many conventional statistical machine learning algorithms generalise poorly if distribution bias exists in the datasets. For example, distribution bias arises in the context of domain generalization, where knowledge acquired from multiple source domains need to be used in a previously unseen target domains. We propose Elliptical Summary Randomisation (ESRand), an efficient domain generalisation approach that comprises of a randomized kernel and elliptical data summarisation. ESRand learns a domain interdependent projection to a latent subspace that minimises the existing biases to the data while maintaining the functional relationship between domains. In the latent subspace, ellipsoidal summaries replace the samples to enhance the generalisation by further removing biasand noise in the data. Moreover, the summarization enables large-scale data processing by significantly reducing the size of the data. Through comprehensive analysis, we show that our subspace-based approach outperforms state-of-the-art results on several activity recognition benchmark datasets, while keeping the computational complexity significantly low.

ICAPS Conference 2016 Conference Paper

Towards Next Generation Touring: Personalized Group Tours

• Kwan Hui Lim 0001
• Jeffrey Chan
• Christopher Leckie
• Shanika Karunasekera

Recommending and planning tour itineraries are challenging and time-consuming for tourists, hence they may seek tour operators for help. Traditionally tour operators have offered standard tour packages of popular locations, but these packages may not cater to tourist's interests. In addition, tourists may want to travel in a group, e. g. , extended family, and want an operator to help them. We introduce the novel problem of group tour recommendation (GroupTourRec), which involves many challenges: forming tour groups whose members have similar interests; recommending Points-of-Interests (POI) that form the tour itinerary and cater for the group's interests; and assigning guides to lead these tours. For each challenge, we propose solutions involving: clustering for tourist groupings; optimizing a variant of the Orienteering problem for POI recommendations; and integer programming for tour guide assignments. Using a Flickr dataset of seven cities, we compare our proposed approaches against various baselines and observe significant improvements in terms of interest similarity, total/maximum/minimum tour interests and total tour guide expertise.

IJCAI Conference 2015 Conference Paper

Personalized Tour Recommendation Based on User Interests and Points of Interest Visit Durations

• Kwan Hui Lim
• Jeffrey Chan
• Christopher Leckie
• Shanika Karunasekera

Tour recommendation and itinerary planning are challenging tasks for tourists, due to their need to select Points of Interest (POI) to visit in unfamiliar cities, and to select POIs that align with their interest preferences and trip constraints. We propose an algorithm called PERSTOUR for recommending personalized tours using POI popularity and user interest preferences, which are automatically derived from real-life travel sequences based on geotagged photos. Our tour recommendation problem is modelled using a formulation of the Orienteering problem, and considers user trip constraints such as time limits and the need to start and end at specific POIs. In our work, we also reflect levels of user interest based on visit durations, and demonstrate how POI visit duration can be personalized using this time-based user interest. Using a Flickr dataset of four cities, our experiments show the effectiveness of PERSTOUR against various baselines, in terms of tour popularity, interest, recall, precision and F1-score. In particular, our results show the merits of using time-based user interest and personalized POI visit durations, compared to the current practice of using frequency-based user interest and average visit durations.

ICML Conference 2002 Conference Paper

Learning to Share Distributed Probabilistic Beliefs

• Christopher Leckie
• Kotagiri Ramamohanarao

IJCAI Conference 1999 Conference Paper

An Evaluation of Criteria for Measuring the Quality of Clusters

• Bhavani Raskutti
• Christopher Leckie

An important problem in clustering is how to decide what is the best set of clusters for a given data set, in terms of both the number of clusters and the membership of those clusters. In this paper we develop four criteria for measuring the quality of different sets of clusters. These criteria are designed so that different criteria prefer cluster sets that generalise at different levels of granularity. We evaluate the suitability of these criteria for non-hierarchical clustering of the results returned by a search engine. We also compare the number of clusters chosen by these criteria with the number of clusters chosen by a group of human subjects. Our results demonstrate that our criteria match the variability exhibited by human subjects, indicating there is no single perfect criterion. Instead, it is necessary to select the correct criterion to match a human subject's generalisation needs.

AIJ Journal 1998 Journal Article

Inductive learning of search control rules for planning

• Christopher Leckie
• Ingrid Zukerman