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University of Queensland

Academic institutionaustralasia · au
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Research library654linked papers
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Selected work

Representative Papers

Continuous Input Embedding Size Search For Recommender Systems

Apr 07, 2023Annual International ACM SIGIR Conference on Research and Development in Information Retrieval

To address memory inefficiency caused by fixed high-dimensional embeddings in recommender systems, this paper proposes a memory-constrained continuous embedding dimension optimization framework. Unlike conventional approaches that employ uniform high-dimensional embeddings or existing reinforcement learning (RL)-based methods limited to discrete dimension selection, our work introduces the first continuous-space embedding dimension search paradigm. We design a stochastic walk-driven exploration strategy to efficiently navigate the continuous dimension space, enabling joint optimization of recommendation accuracy and memory efficiency. The method is model-agnostic and plug-and-play. Extensive experiments on two real-world datasets and three state-of-the-art recommendation models demonstrate that our approach achieves superior performance across multiple memory budgets, consistently outperforming discrete-search baselines and establishing new state-of-the-art results.

21 citations1 influentialRead paper

ConjNorm: Tractable Density Estimation for Out-of-Distribution Detection

Feb 27, 2024International Conference on Learning Representations

Existing post-hoc out-of-distribution (OOD) detection methods rely on logits, distance metrics, or strong distributional assumptions, limiting their ability to accurately model true data density. To address this, we propose a unified density modeling framework based on Bregman divergences, reformulating density estimation as a differentiable optimization problem for the optimal norm coefficient $ p $. We first uncover a novel paradigm for exponential-family density modeling under conjugate constraints, leading to ConjNorm—a method that achieves unbiased, analytically differentiable density estimation without restrictive distributional assumptions. ConjNorm integrates Bregman divergence theory, exponential-family modeling, and Monte Carlo importance sampling. On CIFAR-100 and ImageNet-1K, it reduces false positive rate at 95% true positive rate (FPR95) by 13.25% and 28.19%, respectively, over prior state-of-the-art methods, significantly improving both OOD detection accuracy and robustness.

18 citations1 influentialRead paper

MVGS: Multi-view-regulated Gaussian Splatting for Novel View Synthesis

Oct 02, 2024arXiv.org

To address overfitting in 3D Gaussian Splatting (3DGS) under single-view supervision—leading to artifacts in novel-view synthesis and inaccurate geometric reconstruction—this paper proposes a multi-view collaborative optimization framework. Our method introduces three key innovations: (1) a novel multi-view regularization paradigm that jointly enforces consistency across multiple views; (2) an intrinsic-cross-guided coarse-to-fine training strategy integrating multi-scale geometric and appearance priors; and (3) ray-intersection-driven cross-view densification coupled with view-difference-aware adaptive densification. While preserving real-time rendering performance, our approach significantly improves both novel-view image fidelity and 3D geometric accuracy. Extensive experiments demonstrate strong generalization across diverse scenes and mainstream 3DGS variants, outperforming existing single-view methods in both qualitative and quantitative evaluations.

15 citations1 influentialRead paper

Budgeted Embedding Table For Recommender Systems

Oct 23, 2023Web Search and Data Mining

To address memory overflow and poor scalability caused by fixed-size embeddings in recommender systems, this paper proposes a budget-controllable, table-level embedding optimization method. Unlike existing instance-wise search approaches or manually tuned lightweight embedding schemes, our method introduces a novel table-level action modeling and set representation learning framework, incorporating an action suitability predictor to jointly optimize embedding dimensions across all feature tables—globally, efficiently, and strictly under user-specified memory constraints. By integrating set neural networks with memory-aware architecture search, the approach eliminates the need for heuristic trade-off coefficients. Evaluated on two real-world datasets with three mainstream recommendation models, our method achieves state-of-the-art performance, significantly improving both recommendation accuracy and memory efficiency while rigorously satisfying multiple predefined memory budgets.

10 citationsRead paper

Scalable Dynamic Embedding Size Search for Streaming Recommendation

Jul 22, 2024International Conference on Information and Knowledge Management

To address the unbounded embedding storage overhead caused by continuously growing users and items in streaming recommendation, this paper proposes Scalable Lightweight Embeddings (SCALL), which adaptively allocates embedding dimensions per entity under strict memory budget constraints. Its core contributions are threefold: (1) the first probabilistic distribution-based embedding size sampling mechanism, enabling precise memory control; (2) a reinforcement learning search paradigm with fixed-length state representations, supporting dynamic size generation for unseen entities; and (3) mean-pooling-based state modeling coupled with dynamic memory-aware optimization. Experiments on two public streaming recommendation datasets demonstrate that SCALL reduces storage overhead by up to 62% while improving recommendation accuracy—Recall@10 increases by 3.1–5.7%. To our knowledge, SCALL is the first method to jointly optimize memory controllability and model performance in streaming recommendation.

9 citations1 influentialRead paper
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