- End-to-End Subgraph Detection with GraphDETR
Dexiong Chen, Till Hendrik Schulz, Karsten Borgwardt
Preprint, 2026
- Protein Fold Classification at Scale: Benchmarking and Pretraining
Dexiong Chen, Andrei Manolache, Mathias Niepert, Karsten Borgwardt
ICML, 2026 (Oral)
- PolyGraph Discrepancy: a classifier-based metric for graph generation
Markus Krimmel, Philip Hartout, Karsten Borgwardt, Dexiong Chen
ICLR, 2026
- Fast Graph Generation via Autoregressive Noisy Filtration Modeling
Markus Krimmel, Jenna Wiens, Karsten Borgwardt, Dexiong Chen
TMLR, 2026
- Endowing Protein Language Models with Structural Knowledge
Dexiong Chen, Philip Hartout, Paolo Pellizzoni, Carlos Oliver, Karsten Borgwardt
Bioinformatics, 2025
- Flatten Graphs as Sequences: Transformers are Scalable Graph Generators
Dexiong Chen, Markus Krimmel, Karsten Borgwardt
NeurIPS, 2025
- Predicting sequence-specific amplification efficiency in multi-template PCR with deep learning
Andreas L. Gimpel, Bowen Fan, Dexiong Chen, Laetitia O. D. Wölfle, Max Horn, Laetitia Meng-Papaxanthos, Philipp L. Antkowiak, Wendelin J. Stark, Beat Christen, Karsten Borgwardt, Robert N. Grass
Nature Communications, 2025
- Learning Laplacian Positional Encodings for Heterophilous Graphs
Michael Ito, Jiong Zhu, Dexiong Chen, Danai Koutra, Jenna Wiens
AISTATS, 2025
- Learning Long Range Dependencies on Graphs via Random Walks
Dexiong Chen, Till Hendrik Schulz, Karsten Borgwardt
ICLR, 2025
- Detecting Antimicrobial Resistance Through MALDI-TOF Mass Spectrometry with Statistical Guarantees Using Conformal Prediction
Nina Corvelo Benz, Lucas Miranda, Dexiong Chen, Janko Sattler, Karsten Borgwardt
RECOMB, 2025
- HTR-VT: Handwritten Text Recognition with Vision Transformer
Yuting Li, Dexiong Chen, Tinglong Tang, Xi Shen
Pattern Recognition, 2025
- On the Expressivity and Sample Complexity of Node-Individualized Graph Neural Networks
Paolo Pellizzoni, Till Hendrik Schulz, Dexiong Chen, Karsten Borgwardt
NeurIPS, 2024
- Biomarker Identification by Interpretable Maximum Mean Discrepancy
Michael F Adamer, Sarah C Brüningk, Dexiong Chen, Karsten Borgwardt
ISMB, 2024
- SURE: SUrvey REcipes for building reliable and robust deep networks
Yuting Li, Yingyi Chen, Xuanlong Yu, Dexiong Chen, Xi Shen
CVPR, 2024
- ProteinShake: Building Datasets and Benchmarks for Deep Learning on Protein Structures
Tim Kucera, Carlos Oliver, Dexiong Chen, Karsten Borgwardt
NeurIPS (Datasets and Benchmarks), 2023
- Fisher Information Embedding for Node and Graph Learning
Dexiong Chen, Paolo Pellizzoni, Karsten Borgwardt
ICML, 2023
- Unsupervised Manifold Alignment with Joint Multidimensional Scaling
Dexiong Chen, Bowen Fan, Carlos Oliver, Karsten Borgwardt
ICLR, 2023
- Approximate Network Motif Mining via Graph Learning
Carlos Oliver, Dexiong Chen, Vincent Mallet, Pericles Philippopoulos, Karsten Borgwardt
Preprint, 2022
- Predicting in vitro single-neuron firing rates upon pharmacological perturbation using graph neural networks
Taehoon Kim, Dexiong Chen, Philipp Hornauer, Vishalini Emmenegger, Julian Bartram, Silvia Ronchi, Andreas R. Hierlemann, Manuel Schröter and Damian Roqueiro
Frontiers in Neuroinformatics, 2022
- Structure-Aware Transformer for Graph Representation Learning
Dexiong Chen, Leslie O’Bray, Karsten Borgwardt
ICML, 2022 (Spotlight)
- GraphiT: Encoding Graph Structure in Transformers
Grégoire Mialon, Dexiong Chen, Margot Selosse, Julien Mairal
Preprint, 2021
- Metamixup: Learning Adaptive Interpolation Policy of Mixup with Metalearning
Zhijun Mai, Guosheng Hu, Dexiong Chen, Fumin Shen, Heng Tao Shen
TNNLS, 2021
- Structured Data Modeling with Deep Kernel Machines and Applications in Computational Biology
Dexiong Chen
PhD thesis, Université Grenoble Alpes, 2020
- A Trainable Optimal Transport Embedding for Feature Aggregation and its Relationship to Attention
Grégoire Mialon, Dexiong Chen, Alexandre d’Aspremont, Julien Mairal
ICLR, 2021
- Convolutional Kernel Networks for Graph-Structured Data
Dexiong Chen, Laurent Jacob, Julien Mairal
In ICML, 2020
- Recurrent Kernel Networks
Dexiong Chen, Laurent Jacob, Julien Mairal
NeurIPS, 2019 and accepted as an oral presentation in MLCB, 2019
- A Kernel Perspective for Regularizing Deep Neural Networks
Alberto Bietti, Grégoire Mialon, Dexiong Chen, Julien Mairal
ICML, 2019
- Biological Sequence Modeling with Convolutional Kernel Networks
Dexiong Chen, Laurent Jacob, Julien Mairal
Research in Computational Molecular Biology (RECOMB), 2019 and Bioinformatics, 2019
