Neural Manifold Ordinary Differential Equations. - Aaron Lou; Derek Lim; Isay Katsman; Leo Huang; Qingxuan Jiang; Ser-Nam Lim; Christopher De Sa (NeurIPS, 2020)

Doubly Stochastic Subspace Clustering. - Derek Lim; René Vidal; Benjamin D. Haeffele (CoRR, 2020)

Expertise and Dynamics within Crowdsourced Musical Knowledge Curation: A Case Study of the Genius Platform. - Derek Lim; Austin R. Benson (ICWSM, 2021)

Equivariant Manifold Flows. - Isay Katsman; Aaron Lou; Derek Lim; Qingxuan Jiang; Ser-Nam Lim; Christopher De Sa (NeurIPS, 2021)

Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods. - Derek Lim; Felix Hohne; Xiuyu Li; Sijia Linda Huang; Vaishnavi Gupta; Omkar Bhalerao; Ser-Nam Lim (NeurIPS, 2021)

New Benchmarks for Learning on Non-Homophilous Graphs. - Derek Lim; Xiuyu Li; Felix Hohne; Ser-Nam Lim (CoRR, 2021)

Equivariant Subgraph Aggregation Networks. - Beatrice Bevilacqua; Fabrizio Frasca; Derek Lim; Balasubramaniam Srinivasan; Chen Cai; Gopinath Balamurugan; Michael M. Bronstein; Haggai Maron (ICLR, 2022)

Understanding Doubly Stochastic Clustering. - Tianjiao Ding; Derek Lim; René Vidal; Benjamin D. Haeffele (ICML, 2022)

The First Learning on Graphs Conference: Preface. - Bastian Rieck; Razvan Pascanu; Yuanqi Du; Hannes Stärk; Derek Lim; Chaitanya K. Joshi; Andreea Deac; Iulia Duta; Joshua Robinson 001; Gabriele Corso; Leonardo Cotta; Yanqiao Zhu 001; et al. (LoG, 2022)

The Doubly Stochastic Single Eigenvalue Problem: A Computational Approach. - Amit Harlev; Charles R. Johnson; Derek Lim (Exp. Math., 2022)

The Power of Recursion in Graph Neural Networks for Counting Substructures. - Behrooz Tahmasebi; Derek Lim; Stefanie Jegelka (AISTATS, 2023)

Sign and Basis Invariant Networks for Spectral Graph Representation Learning. - Derek Lim; Joshua David Robinson; Lingxiao Zhao; Tess E. Smidt; Suvrit Sra; Haggai Maron; Stefanie Jegelka (ICLR, 2023)

Graph Inductive Biases in Transformers without Message Passing. - Liheng Ma; Chen Lin 003; Derek Lim; Adriana Romero-Soriano; Puneet K. Dokania; Mark Coates; Philip H. S. Torr; Ser-Nam Lim (ICML, 2023)

Equivariant Polynomials for Graph Neural Networks. - Omri Puny; Derek Lim; Bobak Toussi Kiani; Haggai Maron; Yaron Lipman (ICML, 2023)

Expressive Sign Equivariant Networks for Spectral Geometric Learning. - Derek Lim; Joshua Robinson 001; Stefanie Jegelka; Haggai Maron (NeurIPS, 2023)

Structuring Representation Geometry with Rotationally Equivariant Contrastive Learning. - Sharut Gupta; Joshua Robinson 001; Derek Lim; Soledad Villar; Stefanie Jegelka (ICLR, 2024)

Graph Metanetworks for Processing Diverse Neural Architectures. - Derek Lim; Haggai Maron; Marc T. Law; Jonathan Lorraine; James Lucas (ICLR, 2024)

Position: Future Directions in the Theory of Graph Machine Learning. - Christopher Morris 001; Fabrizio Frasca; Nadav Dym; Haggai Maron; Ismail Ilkan Ceylan; Ron Levie; Derek Lim; Michael M. Bronstein; Martin Grohe; Stefanie Jegelka (ICML, 2024)

The Empirical Impact of Neural Parameter Symmetries, or Lack Thereof. - Derek Lim; Theo Putterman; Robin Walters 001; Haggai Maron; Stefanie Jegelka (NeurIPS, 2024)

A Canonicalization Perspective on Invariant and Equivariant Learning. - George Ma; Yifei Wang 001; Derek Lim; Stefanie Jegelka; Yisen Wang 001 (NeurIPS, 2024)

Future Directions in Foundations of Graph Machine Learning. - Christopher Morris 001; Nadav Dym; Haggai Maron; Ismail Ilkan Ceylan; Fabrizio Frasca; Ron Levie; Derek Lim; Michael M. Bronstein; Martin Grohe; Stefanie Jegelka (CoRR, 2024)

A Canonization Perspective on Invariant and Equivariant Learning. - George Ma; Yifei Wang 001; Derek Lim; Stefanie Jegelka; Yisen Wang 001 (CoRR, 2024)

The Empirical Impact of Neural Parameter Symmetries, or Lack Thereof. - Derek Lim; Moe Putterman; Robin Walters 001; Haggai Maron; Stefanie Jegelka (CoRR, 2024)

Learning on LoRAs: GL-Equivariant Processing of Low-Rank Weight Spaces for Large Finetuned Models. - Moe Putterman; Derek Lim; Yoav Gelberg; Stefanie Jegelka; Haggai Maron (CoRR, 2024)

Learning on LLM Output Signatures for gray-box LLM Behavior Analysis. - Guy Bar-Shalom; Fabrizio Frasca; Derek Lim; Yoav Gelberg; Yftah Ziser; Ran El-Yaniv; Gal Chechik; Haggai Maron (CoRR, 2025)