Learning on LLM Output Signatures for gray-box LLM Behavior Analysis.
CoRR, March, 2025
Learning on LoRAs: GL-Equivariant Processing of Low-Rank Weight Spaces for Large Finetuned Models.
CoRR, 2024
The Empirical Impact of Neural Parameter Symmetries, or Lack Thereof.
CoRR, 2024
A Canonization Perspective on Invariant and Equivariant Learning.
CoRR, 2024
Future Directions in Foundations of Graph Machine Learning.
CoRR, 2024
A Canonicalization Perspective on Invariant and Equivariant Learning.
Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024
The Empirical Impact of Neural Parameter Symmetries, or Lack Thereof.
Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024
Position: Future Directions in the Theory of Graph Machine Learning.
Proceedings of the Forty-first International Conference on Machine Learning, 2024
Graph Metanetworks for Processing Diverse Neural Architectures.
Proceedings of the Twelfth International Conference on Learning Representations, 2024
Structuring Representation Geometry with Rotationally Equivariant Contrastive Learning.
Proceedings of the Twelfth International Conference on Learning Representations, 2024
Expressive Sign Equivariant Networks for Spectral Geometric Learning.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023
Equivariant Polynomials for Graph Neural Networks.
Proceedings of the International Conference on Machine Learning, 2023
Graph Inductive Biases in Transformers without Message Passing.
Proceedings of the International Conference on Machine Learning, 2023
Sign and Basis Invariant Networks for Spectral Graph Representation Learning.
Proceedings of the Eleventh International Conference on Learning Representations, 2023
The Power of Recursion in Graph Neural Networks for Counting Substructures.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023
The Doubly Stochastic Single Eigenvalue Problem: A Computational Approach.
Exp. Math., 2022
The First Learning on Graphs Conference: Preface.
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Proceedings of the Learning on Graphs Conference, 2022
Understanding Doubly Stochastic Clustering.
Proceedings of the International Conference on Machine Learning, 2022
Equivariant Subgraph Aggregation Networks.
Proceedings of the Tenth International Conference on Learning Representations, 2022
New Benchmarks for Learning on Non-Homophilous Graphs.
CoRR, 2021
Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021
Equivariant Manifold Flows.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021
Expertise and Dynamics within Crowdsourced Musical Knowledge Curation: A Case Study of the Genius Platform.
Proceedings of the Fifteenth International AAAI Conference on Web and Social Media, 2021
Doubly Stochastic Subspace Clustering.
CoRR, 2020
Neural Manifold Ordinary Differential Equations.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020