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University of Melbourne

Academic institutionaustralasia · au
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Research library1,096linked papers
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Selected work

Representative Papers

On the Identification of Temporally Causal Representation with Instantaneous Dependence

May 24, 2024arXiv.org

Existing time-series causal representation learning methods typically neglect instantaneous causal relationships, while emerging approaches accommodating such dependencies rely on latent-variable interventions or grouped observational data—conditions rarely satisfied in practice. To address this, we propose IDOL, the first framework enabling unique identification of latent causal processes with instantaneous dependencies without requiring interventions or data grouping. Theoretically, IDOL introduces sparse influence constraints—unifying delayed and instantaneous causal modeling—and temporal context variability, establishing strong identifiability guarantees. Methodologically, it integrates temporal variational inference with gradient-driven sparse regularization to jointly estimate latent variables and the causal graph. Experiments demonstrate that IDOL achieves exact structural recovery on synthetic benchmarks and significantly improves long-horizon prediction accuracy and causal interpretability across multiple human motion forecasting datasets.

11 citationsRead paper

Sports-QA: A Large-Scale Video Question Answering Benchmark for Complex and Professional Sports

Jan 03, 2024arXiv.org

Existing VideoQA datasets lack fine-grained modeling of professional sports actions, hindering effective reasoning for descriptive, temporal, causal, and counterfactual questions. To address this, we introduce Sports-QA—the first video question answering benchmark tailored to professional sports scenarios—covering multiple sports disciplines and four categories of complex reasoning tasks. Methodologically, we propose the Auto-Focus Transformer (AFT), which employs an attention-driven dynamic focusing mechanism to adaptively model multi-scale temporal information and integrates joint video–language representation learning. Extensive experiments demonstrate that AFT achieves state-of-the-art performance on Sports-QA, substantially outperforming general-purpose VideoQA models. This work constitutes the first systematic validation of an architecture explicitly designed for fine-grained sports action understanding and dynamic logical reasoning, establishing a new foundation for domain-specific VideoQA research.

10 citations2 influentialRead paper

Inclusion-of-Thoughts: Mitigating Preference Instability via Purifying the Decision Space

Mar 15, 2026

This work addresses the susceptibility of large language models to distractors in multiple-choice questions, which often leads to unstable preferences and answer oscillation. To mitigate this issue, the authors propose the Inclusion-of-Thoughts (IoT) method, which employs a self-filtering mechanism to assess the plausibility of candidate options and reformulates the question by explicitly removing distractors. This approach purifies the decision space, reduces cognitive load, and steers the model toward meaningful comparisons and chain-of-thought reasoning. IoT is the first method to explicitly alleviate preference instability caused by distractors, achieving substantial gains in reasoning performance across arithmetic, commonsense, and educational benchmarks while incurring minimal computational overhead. Additionally, it enhances the transparency and interpretability of model decisions.

3 citationsRead paper

A class of skew-multivariate distributions for spatial data

Jan 27, 2026

Existing spatial data models struggle to simultaneously capture complex extremal features such as tail dependence, asymptotic independence, and tail asymmetry. This work proposes a novel approach by introducing multivariate Pareto mixture distributions into a spatial copula framework, yielding a flexible model capable of jointly modeling both bulk and tail behaviors. The resulting formulation provides a unified treatment of the three aforementioned extremal dependence structures while preserving permutation asymmetry. Based on copula theory and maximum likelihood estimation, the method is validated through finite-sample simulations that demonstrate its computational feasibility and favorable parameter estimation performance. Empirical analysis of temperature data successfully reveals intricate tail structures, underscoring the model’s theoretical rigor and practical utility.

1 citationsRead paper
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