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Papers
On the Theoretical Expressive Power and the Design Space of Higher-Order Graph Transformers
AISTATS 2024
On the Universality of Volume-Preserving and Coupling-Based Normalizing Flows
ICML 2024
OPTIMAL ROBUST MEMORIZATION WITH RELU NEURAL NETWORKS
ICLR 2024
Position: Future Directions in the Theory of Graph Machine Learning
ICML 2024
Rethinking and Extending the Probabilistic Inference Capacity of GNNs
ICLR 2024
Rethinking the Benefits of Steerable Features in 3D Equivariant Graph Neural Networks
ICLR 2024
Subtractive Mixture Models via Squaring: Representation and Learning
ICLR 2024
The Expressive Capacity of State Space Models: A Formal Language Perspective
NeurIPS 2024
The Expressive Power of Low-Rank Adaptation
ICLR 2024
The Expressive Power of Path-Based Graph Neural Networks
ICML 2024
The Illusion of State in State-Space Models
ICML 2024
Theoretical Foundations of Deep Selective State-Space Models
NeurIPS 2024
Towards a Theory of Machine Learning on Graphs and its Applications in Combinatorial Optimization
IJCAI 2024
Towards Principled Graph Transformers
NeurIPS 2024
Understanding the Expressive Power and Mechanisms of Transformer for Sequence Modeling
NeurIPS 2024
A Complete Expressiveness Hierarchy for Subgraph GNNs via Subgraph Weisfeiler-Lehman Tests
ICML 2023
A New Perspective On the Expressive Equivalence Between Graph Convolution and Attention Models
ACML 2023
Equivariant Polynomials for Graph Neural Networks
ICML 2023
Graph Neural Networks can Recover the Hidden Features Solely from the Graph Structure
ICML 2023
On Enhancing Expressive Power via Compositions of Single Fixed-Size ReLU Network
ICML 2023
Path Neural Networks: Expressive and Accurate Graph Neural Networks
ICML 2023
Recurrent Neural Language Models as Probabilistic Finite-state Automata
EMNLP 2023
The Expressive Power of Ad-Hoc Constraints for Modelling CSPs
AAAI 2023
The Expressive Power of Tuning Only the Normalization Layers
COLT 2023
The Power of Recursion in Graph Neural Networks for Counting Substructures
AISTATS 2023