Shen, Yuesong

12 publications

ICLRW 2025 Graph Networks Struggle with Variable Scale Christian Koke, Yuesong Shen, Abhishek Saroha, Marvin Eisenberger, Bastian Rieck, Michael M. Bronstein, Daniel Cremers

ICLRW 2025 On Incorporating Scale into Graph Networks Christian Koke, Yuesong Shen, Abhishek Saroha, Marvin Eisenberger, Bastian Rieck, Michael M. Bronstein, Daniel Cremers

ICLRW 2025 On Multi-Scale Graph Representation Learning Christian Koke, Dominik Schnaus, Yuesong Shen, Abhishek Saroha, Marvin Eisenberger, Bastian Rieck, Michael M. Bronstein, Daniel Cremers

ICMLW 2024 Transferability for Graph Convolutional Networks Christian Koke, Abhishek Saroha, Yuesong Shen, Marvin Eisenberger, Michael M. Bronstein, Daniel Cremers

ICML 2024 Variational Learning Is Effective for Large Deep Networks Yuesong Shen, Nico Daheim, Bai Cong, Peter Nickl, Gian Maria Marconi, Bazan Clement Emile Marcel Raoul, Rio Yokota, Iryna Gurevych, Daniel Cremers, Mohammad Emtiyaz Khan, Thomas Möllenhoff

NeurIPSW 2024 Variational Low-Rank Adaptation Using IVON Bai Cong, Nico Daheim, Yuesong Shen, Daniel Cremers, Rio Yokota, Mohammad Emtiyaz Khan, Thomas Möllenhoff

ICML 2023 Beyond In-Domain Scenarios: Robust Density-Aware Calibration Christian Tomani, Futa Kai Waseda, Yuesong Shen, Daniel Cremers

NeurIPSW 2023 ResolvNet: A Graph Convolutional Network with Multi-Scale Consistency Christian Koke, Abhishek Saroha, Yuesong Shen, Marvin Eisenberger, Daniel Cremers

NeurIPSW 2022 A Graph Is More than Its Nodes: Towards Structured Uncertainty-Aware Learning on Graphs Hans Hao-Hsun Hsu, Yuesong Shen, Daniel Cremers

NeurIPS 2022 Deep Combinatorial Aggregation Yuesong Shen, Daniel Cremers

NeurIPS 2022 What Makes Graph Neural Networks Miscalibrated? Hans Hao-Hsun Hsu, Yuesong Shen, Christian Tomani, Daniel Cremers

UAI 2021 Explicit Pairwise Factorized Graph Neural Network for Semi-Supervised Node Classification Yu Wang, Yuesong Shen, Daniel Cremers