Lim, Derek

24 publications

ICLRW 2025 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

ICLRW 2025 Learning on LoRAs: GL-Equivariant Processing of Low-Rank Weight Spaces for Large Finetuned Models Theo Putterman, Derek Lim, Yoav Gelberg, Stefanie Jegelka, Haggai Maron

NeurIPS 2024 A Canonicalization Perspective on Invariant and Equivariant Learning George Ma, Yifei Wang, Derek Lim, Stefanie Jegelka, Yisen Wang

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

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

ICLR 2024 Structuring Representation Geometry with Rotationally Equivariant Contrastive Learning Sharut Gupta, Joshua Robinson, Derek Lim, Soledad Villar, Stefanie Jegelka

NeurIPS 2024 The Empirical Impact of Neural Parameter Symmetries, or Lack Thereof Derek Lim, Theo Putterman, Robin Walters, Haggai Maron, Stefanie Jegelka

ICMLW 2024 The Empirical Impact of Neural Parameter Symmetries, or Lack Thereof Derek Lim, Theo Putterman, Robin Walters, Haggai Maron, Stefanie Jegelka

ICML 2023 Equivariant Polynomials for Graph Neural Networks Omri Puny, Derek Lim, Bobak Kiani, Haggai Maron, Yaron Lipman

NeurIPS 2023 Expressive Sign Equivariant Networks for Spectral Geometric Learning Derek Lim, Joshua W. Robinson, Stefanie Jegelka, Haggai Maron

ICLRW 2023 Expressive Sign Equivariant Networks for Spectral Geometric Learning Derek Lim, Joshua Robinson, Stefanie Jegelka, Yaron Lipman, Haggai Maron

ICMLW 2023 Expressive Sign Equivariant Networks for Spectral Geometric Learning Derek Lim, Joshua Robinson, Stefanie Jegelka, Haggai Maron

ICML 2023 Graph Inductive Biases in Transformers Without Message Passing Liheng Ma, Chen Lin, Derek Lim, Adriana Romero-Soriano, Puneet K. Dokania, Mark Coates, Philip Torr, Ser-Nam Lim

ICMLW 2023 Learning Structured Representations with Equivariant Contrastive Learning Sharut Gupta, Joshua Robinson, Derek Lim, Soledad Villar, Stefanie Jegelka

ICMLW 2023 Positional Encodings as Group Representations: A Unified Framework Derek Lim, Hannah Lawrence, Ningyuan Teresa Huang, Erik Henning Thiede

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

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

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

ICLRW 2022 Sign and Basis Invariant Networks for Spectral Graph Representation Learning Derek Lim, Joshua David Robinson, Lingxiao Zhao, Tess Smidt, Suvrit Sra, Haggai Maron, Stefanie Jegelka

ICML 2022 Understanding Doubly Stochastic Clustering Tianjiao Ding, Derek Lim, Rene Vidal, Benjamin D Haeffele

NeurIPS 2021 Equivariant Manifold Flows Isay Katsman, Aaron Lou, Derek Lim, Qingxuan Jiang, Ser Nam Lim, Christopher M De Sa

ICMLW 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 2020 Neural Manifold Ordinary Differential Equations Aaron Lou, Derek Lim, Isay Katsman, Leo Huang, Qingxuan Jiang, Ser Nam Lim, Christopher M De Sa