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National University of Singapore

Academic institutionasia · sg
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Research library4,583linked papers
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Selected work

Representative Papers

Budget Pacing in Repeated Auctions: Regret and Efficiency without Convergence

May 18, 2022Information Technology Convergence and Services

This paper investigates the impact of dynamic bidding pacing algorithms on group liquid welfare and individual dynamic regret in repeated ad auctions under budget constraints. To overcome the limitation of prior work—reliance on convergence assumptions about algorithmic dynamics—we propose a novel theoretical framework that makes no such assumptions. First, we establish that liquid welfare is guaranteed to be at least 50% of the optimal expected value, irrespective of convergence. Second, we derive an upper bound on dynamic regret tailored to time-varying budgets. Third, we design a gradient-based linear pacing algorithm within the core auction framework, integrating monotonic return-on-spend modeling and dynamic regret analysis to ensure broad applicability across first-price, second-price, and generalized second-price auctions. Empirical validation on Bing Ads data confirms the theoretical guarantees.

36 citations1 influentialRead paper

Intuitive Surgical SurgToolLoc Challenge Results: 2022-2023

May 11, 2023

To address the challenge of real-time, robust surgical instrument localization in minimally invasive robotic-assisted surgery (RAS) video streams, this work introduces SurgToolLoc—the first large-scale, multi-view, multi-scenario benchmark dataset with pixel-level mask annotations. We further propose a novel evaluation protocol emphasizing both cross-center generalizability and real-time inference (≥30 FPS). Methodologically, we integrate instance segmentation and keypoint detection with temporal modeling (ConvLSTM/Transformer), domain adaptation, and weakly supervised learning. Our best-performing model achieves 92.4% mAP@0.5 on the test set while maintaining an inference speed of 36 FPS—substantially outperforming conventional template matching and early CNN-based approaches. The solution has undergone rigorous preclinical validation across multiple surgical scenarios. By providing a reproducible, scalable, end-to-end framework for visual instrument localization in RAS, this work establishes a new standard for benchmarking and advancing vision-based surgical navigation systems.

17 citationsRead paper

Dynamic Multimodal Fusion via Meta-Learning Towards Micro-Video Recommendation

Aug 30, 2023ACM Trans. Inf. Syst.

To address the limitation of static multimodal fusion in middle-school micro-video recommendation—its inability to capture inter-video modality relationship discrepancies—this paper proposes MetaMMF, a meta-learning-based dynamic multimodal fusion framework. Methodologically, MetaMMF treats multimodal fusion for each video as an individual meta-task and employs meta-learning to generate video-specific fusion functions; it further adopts CP tensor decomposition to enhance parameter efficiency and training stability. While implicitly incorporating graph neural network principles (e.g., akin to MMGCN), MetaMMF avoids explicit graph construction. Extensive experiments on three benchmark datasets demonstrate that MetaMMF consistently outperforms state-of-the-art models—including MMGCN, LATTICE, and InvRL—achieving superior recommendation accuracy and computational efficiency. The source code is publicly released, empirically validating the dual advantages of dynamic fusion in both performance and efficiency.

15 citationsRead paper

Adversarial Multi-Agent Evaluation of Large Language Models through Iterative Debates

Oct 07, 2024arXiv.org

Existing LLM evaluation methods suffer from inconsistency, bias, and opaque automated metrics. To address these issues, we propose an interpretable, adversarial multi-agent evaluation framework: multiple LLM agents assume “advocate” roles and engage in structured debates under a judge-jury mechanism, enabling dynamic assessment through iterative argumentation and adjudication. Our key contributions include: (1) introducing the first evaluation paradigm wherein LLMs serve as *debate-capable advocates*; (2) designing a theory-driven probabilistic error attenuation model to quantify and mitigate evaluation bias; and (3) integrating role-based prompting, formal debate protocols, and self-supervised feedback. Experiments demonstrate that our multi-advocate architecture significantly reduces evaluation error, enhances robustness, and improves cross-task consistency—establishing a new benchmark for trustworthy LLM evaluation.

11 citationsRead paper
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