Skip to content
YJYeonwoo Jeong

Research

Combinatorial optimization for deep learning

Ph.D. work on making neural networks efficient by treating the design choices — which channels, which layers, which codes — as discrete optimization problems with tractable solutions.

  1. NeurIPS 2025

    NEXT-EVAL: Next Evaluation of Traditional and LLM Web Data Record Extraction

    S. Kim, N. Kim, Y. Jeong

    • An evaluation framework comparing structure-based extractors against LLM extractors on identical pages, exposing distinct failure modes — hallucinated fields and schema drift versus brittleness across DOM variations.
    • Feeding LLMs a flat JSON representation of the DOM instead of raw HTML recovers structural signal that is otherwise lost; the resulting hybrid reaches F1 0.9567.
    BibTeX
  2. ICBC 2023first author

    Efficient Liquidity Providing via Margin Liquidity

    Y. Jeong, C. Jeoung, H. Jeong, S. Han, J. Kim

    • Introduced margin liquidity to reduce divergence risk for liquidity providers in decentralized exchanges.
    BibTeX
  3. ICML 2023

    Efficient Latency-aware CNN Depth Compression via Two-stage Dynamic Programming

    J. Kim, Y. Jeong, D. Lee, H. O. Song

    • Formulated a subset selection problem that replaces inefficient activation layers with identity functions.
    • Solved a surrogate objective via two-stage dynamic programming.
    BibTeX
  4. AISTATS 2022first author

    Optimal Channel Selection with Discrete QCQP

    Y. Jeong, D. Lee, G. An, C. Son, H. O. Song

    • An optimal channel selection method cast as a discrete quadratically constrained quadratic program.
    BibTeX
  5. ICML 2019first author

    Learning Discrete and Continuous Factors of Data via Alternating Disentanglement

    Y. Jeong, H. O. Song

    • An efficient procedure that implicitly penalizes total correlation by controlling information flow between latent variables.
    • Jointly learns discrete and continuous latent factors in an alternating maximization framework — treating disentanglement as a structural prior for interpretability.
    BibTeX
  6. CVPR 2019first author

    End-to-end Efficient Representation Learning via Cascading Combinatorial Optimization

    Y. Jeong, Y. Kim, H. O. Song

    • An end-to-end learning algorithm with significantly increased quantization granularity.
    • Keeps computational complexity practical through hierarchically quantized representations.
    BibTeX
  7. ICML 2018first author

    Efficient End-to-end Learning for Quantizable Representations

    Y. Jeong, H. O. Song

    • Formulated the search for an optimal sparse binary hash code as a combinatorial optimization problem.
    • Solved it as a minimum cost flow problem for efficient representation learning.
    BibTeX