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University of Illinois Urbana-Champaign

Academic institutionnorthamerica · us
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Research library3,137linked papers
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Selected work

Representative Papers

A Comprehensive Survey on Vector Database: Storage and Retrieval Technique, Challenge

Oct 18, 2023arXiv.org

Managing and retrieving high-dimensional vector data poses significant challenges, particularly as traditional databases fail to meet performance requirements and the need for tight integration with large language models (LLMs) intensifies. Method: This paper systematically surveys four major approximate nearest neighbor search (ANNS) paradigms—hashing, tree-based indexing, graph-based methods (e.g., HNSW), and quantization (PQ/SQ)—and integrates hybrid optimization strategies. Contribution/Results: It introduces, for the first time, a “Four-Dimensional Methodology” framework tailored for industrial deployment of vector databases, analyzing trade-offs among accuracy, latency, memory footprint, and scalability. The work constructs a structured knowledge graph covering 200+ ANNS algorithms and proposes a novel paradigm for deep synergy between vector databases and LLMs. Collectively, these contributions provide both theoretical foundations and practical guidelines for system selection, architectural design, and development of AI-native database systems.

62 citations3 influentialRead paper

A practical approach for computing the diameter of a point set

Jun 01, 2001SCG '01

This paper addresses the efficient computation of the diameter of a point set in high-dimensional space. To overcome the practical inefficiency of traditional algorithms, we propose the first input-sensitive approximation algorithm for this problem. Our method integrates geometric pruning, iterative distance thresholding, and extremal direction sampling, coupled with exact verification and early termination mechanisms. It requires no preprocessing or parameter tuning, offering both theoretical guarantees and empirical robustness. Evaluated on extensive benchmark datasets, our algorithm significantly outperforms state-of-the-art methods in runtime while maintaining near-perfect (≈100%) accuracy. The key innovation lies in introducing input sensitivity to diameter computation—achieving ultra-fast performance on typical instances while retaining worst-case theoretical bounds. This balance makes it the empirically optimal diameter computation scheme at the time of publication.

48 citations2 influentialRead paper

LidarDM: Generative LiDAR Simulation in a Generated World

Apr 03, 2024IEEE International Conference on Robotics and Automation

Existing methods for generating realistic, layout-aware, physically plausible, and temporally coherent 4D LiDAR video in autonomous driving simulation remain inadequate. Method: This paper introduces the first generative 4D LiDAR world model tailored for driving scenes. It employs an integrated framework that leverages latent diffusion models for 3D scene generation, jointly models dynamic agent motion, and captures spatiotemporal (4D) point cloud sequences; LiDAR video is then synthesized via differentiable sensor rendering. Contribution/Results: Our approach pioneers driving-semantic-guided 4D LiDAR generation, uniquely ensuring layout consistency, physical interpretability, and temporal coherence. Quantitative and qualitative evaluations demonstrate significant improvements over prior art in realism, temporal continuity, and structural fidelity. The generated LiDAR sequences effectively support downstream perception model training and evaluation.

39 citations5 influentialRead paper

Preconditioned Gradient Descent for Over-Parameterized Nonconvex Matrix Factorization

Apr 13, 2025Neural Information Processing Systems

In overparameterized nonconvex matrix factorization—where the specified rank $r$ exceeds the true rank $r^*$—gradient descent suffers sublinear convergence, severely limiting efficiency. This paper proposes PrecGD, a lightweight preconditioned gradient descent method that restores linear convergence without requiring prior knowledge of $r^*$. Key contributions include: (i) the first theoretical demonstration that $ell_2$ regularization, within a specific damping range, effectively mitigates ill-conditioning of the factor matrices; and (ii) a novel adaptive damping strategy, computed cheaply from current iterates, which robustly handles the conditioning of the ground-truth solution. PrecGD maintains linear convergence even under noise and achieves the information-theoretically optimal estimation error bound. Experiments across diverse overparameterized matrix sensing and factorization tasks confirm substantial improvements in both convergence speed and reconstruction accuracy.

31 citations5 influentialRead paper

Approximating Nash Social Welfare by Matching and Local Search

Nov 07, 2022Symposium on the Theory of Computing

This paper studies Nash social welfare (NSW) maximization under submodular utilities, addressing both symmetric and weighted (asymmetric) settings, while simultaneously pursuing approximation efficiency and fairness—specifically EFX. We propose the first deterministic algorithmic framework that integrates bipartite matching with local search. For the symmetric case, it achieves a $(4+varepsilon)$-approximation to optimal NSW, drastically improving upon the previous best ratio of 380; for the weighted case, it attains a $(omega+2+varepsilon)$-approximation, where $omega$ is the largest weight ratio. Crucially, it is the first polynomial-time algorithm to simultaneously guarantee $12$-EFX fairness and $(8+varepsilon)$-NSW approximation—breaking the prior barrier that precluded constant-factor NSW approximations under EFX. Our core innovation lies in unifying matching structures with submodular optimization, leveraging a weighted geometric mean objective to jointly approximate efficiency and fairness.

18 citations5 influentialRead paper
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