Niepert, Mathias

67 publications

ICLR 2025 Active Learning for Neural PDE Solvers Daniel Musekamp, Marimuthu Kalimuthu, David Holzmüller, Makoto Takamoto, Mathias Niepert

ICML 2025 Adaptive Message Passing: A General Framework to Mitigate Oversmoothing, Oversquashing, and Underreaching Federico Errica, Henrik Christiansen, Viktor Zaverkin, Takashi Maruyama, Mathias Niepert, Francesco Alesiani

TMLR 2025 Adaptive Physics-Informed Neural Networks: A Survey Edgar Torres, Mathias Niepert

NeurIPS 2025 CALM-PDE: Continuous and Adaptive Convolutions for Latent Space Modeling of Time-Dependent PDEs Jan Hagnberger, Daniel Musekamp, Mathias Niepert

ICLR 2025 Discrete Copula Diffusion Anji Liu, Oliver Broadrick, Mathias Niepert, Guy Van den Broeck

NeurIPS 2025 ExGra-Med: Extended Context Graph Alignment for Medical Vision-Language Models Duy Minh Ho Nguyen, Nghiem Tuong Diep, Trung Quoc Nguyen, Hoang-Bao Le, Tai Nguyen, Anh-Tien Nguyen, TrungTin Nguyen, Nhat Ho, Pengtao Xie, Roger Wattenhofer, Daniel Sonntag, James Zou, Mathias Niepert

NeurIPS 2025 How Many Tokens Do 3D Point Cloud Transformer Architectures Really Need? Tuan Anh Tran, Duy Minh Ho Nguyen, Hoai-Chau Tran, Michael Barz, Khoa D Doan, Roger Wattenhofer, Vien Anh Ngo, Mathias Niepert, Daniel Sonntag, Paul Swoboda

TMLR 2025 LOGLO-FNO: Efficient Learning of Local and Global Features in Fourier Neural Operators Marimuthu Kalimuthu, David Holzmüller, Mathias Niepert

ICLRW 2025 LOGLO-FNO: Efficient Learning of Local and Global Features in Fourier Neural Operators Marimuthu Kalimuthu, David Holzmüller, Mathias Niepert

NeurIPS 2025 Learning (Approximately) Equivariant Networks via Constrained Optimization Andrei Manolache, Luiz F. O. Chamon, Mathias Niepert

ICLR 2025 Learning to Discretize Denoising Diffusion ODEs Vinh Tong, Dung Trung Hoang, Anji Liu, Guy Van den Broeck, Mathias Niepert

ICML 2025 On Zero-Initialized Attention: Optimal Prompt and Gating Factor Estimation Nghiem Tuong Diep, Huy Nguyen, Chau Nguyen, Minh Le, Duy Minh Ho Nguyen, Daniel Sonntag, Mathias Niepert, Nhat Ho

ICML 2025 Physics-Informed Weakly Supervised Learning for Interatomic Potentials Makoto Takamoto, Viktor Zaverkin, Mathias Niepert

ICML 2025 Position: Graph Learning Will Lose Relevance Due to Poor Benchmarks Maya Bechler-Speicher, Ben Finkelshtein, Fabrizio Frasca, Luis Müller, Jan Tönshoff, Antoine Siraudin, Viktor Zaverkin, Michael M. Bronstein, Mathias Niepert, Bryan Perozzi, Mikhail Galkin, Christopher Morris

TMLR 2025 Prompt Engineering Techniques for Language Model Reasoning Lack Replicability Laurène Vaugrante, Mathias Niepert, Thilo Hagendorff

NeurIPS 2025 Rao-Blackwell Gradient Estimators for Equivariant Denoising Diffusion Vinh Tong, Dung Trung Hoang, Anji Liu, Guy Van den Broeck, Mathias Niepert

ICLRW 2025 Symmetry-Preserving Diffusion Models via Target Symmetrization Vinh Tong, Yun Ye, Dung Trung Hoang, Anji Liu, Guy Van den Broeck, Mathias Niepert

ICML 2025 Tractable Transformers for Flexible Conditional Generation Anji Liu, Xuejie Liu, Dayuan Zhao, Mathias Niepert, Yitao Liang, Guy Van Den Broeck

NeurIPS 2024 Accelerating Transformers with Spectrum-Preserving Token Merging Hoai-Chau Tran, Duy M. H. Nguyen, Duy M. Nguyen, TrungTin Nguyen, Ngan Le, Pengtao Xie, Daniel Sonntag, James Zou, Binh T. Nguyen, Mathias Niepert

NeurIPSW 2024 Active Learning for Neural PDE Solvers Daniel Musekamp, Marimuthu Kalimuthu, David Holzmüller, Makoto Takamoto, Mathias Niepert

ACML 2024 Dude: Dual Distribution-Aware Context Prompt Learning for Large Vision-Language Model Duy Minh Ho Nguyen, An Thai Le, Trung Quoc Nguyen, Nghiem Tuong Diep, Tai Nguyen, Duy Duong-Tran, Jan Peters, Li Shen, Mathias Niepert, Daniel Sonntag

NeurIPS 2024 Higher-Rank Irreducible Cartesian Tensors for Equivariant Message Passing Viktor Zaverkin, Francesco Alesiani, Takashi Maruyama, Federico Errica, Henrik Christiansen, Makoto Takamoto, Nicolas Weber, Mathias Niepert

ICLR 2024 Image Inpainting via Tractable Steering of Diffusion Models Anji Liu, Mathias Niepert, Guy Van den Broeck

MLJ 2024 L2XGNN: Learning to Explain Graph Neural Networks Giuseppe Serra, Mathias Niepert

ICMLW 2024 Physics-Informed Weakly Supervised Learning for Interatomic Potentials Makoto Takamoto, Viktor Zaverkin, Mathias Niepert

NeurIPS 2024 Probabilistic Graph Rewiring via Virtual Nodes Chendi Qian, Andrei Manolache, Christopher Morris, Mathias Niepert

ICLR 2024 Probabilistically Rewired Message-Passing Neural Networks Chendi Qian, Andrei Manolache, Kareem Ahmed, Zhe Zeng, Guy Van den Broeck, Mathias Niepert, Christopher Morris

ICML 2024 Structure-Aware E(3)-Invariant Molecular Conformer Aggregation Networks Duy Minh Ho Nguyen, Nina Lukashina, Tai Nguyen, An Thai Le, Trungtin Nguyen, Nhat Ho, Jan Peters, Daniel Sonntag, Viktor Zaverkin, Mathias Niepert

NeurIPSW 2024 Survey: Adaptive Physics-Informed Neural Networks Edgar Torres, Mathias Niepert

ICLR 2024 Tractable Probabilistic Graph Representation Learning with Graph-Induced Sum-Product Networks Federico Errica, Mathias Niepert

ICLRW 2024 Vectorized Conditional Neural Fields: A Framework for Solving Time-Dependent PDEs Jan Hagnberger, Marimuthu Kalimuthu, Mathias Niepert

ICML 2024 Vectorized Conditional Neural Fields: A Framework for Solving Time-Dependent Parametric Partial Differential Equations Jan Hagnberger, Marimuthu Kalimuthu, Daniel Musekamp, Mathias Niepert

AAAI 2023 Adaptive Perturbation-Based Gradient Estimation for Discrete Latent Variable Models Pasquale Minervini, Luca Franceschi, Mathias Niepert

NeurIPS 2023 LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-Order Graph Matching Duy M. H. Nguyen, Hoang Nguyen, Nghiem Diep, Tan Ngoc Pham, Tri Cao, Binh Nguyen, Paul Swoboda, Nhat Ho, Shadi Albarqouni, Pengtao Xie, Daniel Sonntag, Mathias Niepert

ECML-PKDD 2023 Learning Disentangled Discrete Representations David Friede, Christian Reimers, Heiner Stuckenschmidt, Mathias Niepert

ICML 2023 Learning Neural PDE Solvers with Parameter-Guided Channel Attention Makoto Takamoto, Francesco Alesiani, Mathias Niepert

NeurIPSW 2023 On the Out of Distribution Robustness of Foundation Models in Medical Image Segmentation Duy Minh Ho Nguyen, Tan Ngoc Pham, Nghiem Tuong Diep, Nghi Quoc Phan, Quang Pham, Vinh Tong, Binh T. Nguyen, Ngan Hoang Le, Nhat Ho, Pengtao Xie, Daniel Sonntag, Mathias Niepert

ICLRW 2023 Pdebench: An Extensive Benchmark for Sci- Entific Machine Learning Makoto Takamoto, Timothy Praditia, Raphael Leiteritz, Dan MacKinlay, Francesco Alesiani, Dirk Pflüger, Mathias Niepert

ICMLW 2023 Probabilistic Task-Adaptive Graph Rewiring Chendi Qian, Andrei Manolache, Kareem Ahmed, Zhe Zeng, Guy Van den Broeck, Mathias Niepert, Christopher Morris

ICMLW 2023 SIMPLE: A Gradient Estimator for $k$-Subset Sampling Kareem Ahmed, Zhe Zeng, Mathias Niepert, Guy Van den Broeck

ICLR 2023 SIMPLE: A Gradient Estimator for K-Subset Sampling Kareem Ahmed, Zhe Zeng, Mathias Niepert, Guy Van den Broeck

NeurIPS 2022 Ordered Subgraph Aggregation Networks Chendi Qian, Gaurav Rattan, Floris Geerts, Mathias Niepert, Christopher Morris

NeurIPS 2022 PDEBench: An Extensive Benchmark for Scientific Machine Learning Makoto Takamoto, Timothy Praditia, Raphael Leiteritz, Daniel MacKinlay, Francesco Alesiani, Dirk Pflüger, Mathias Niepert

AAAI 2021 Answering Complex Queries in Knowledge Graphs with Bidirectional Sequence Encoders Bhushan Kotnis, Carolin Lawrence, Mathias Niepert

NeurIPS 2021 Efficient Learning of Discrete-Continuous Computation Graphs David Friede, Mathias Niepert

AAAI 2021 Explaining Neural Matrix Factorization with Gradient Rollback Carolin Lawrence, Timo Sztyler, Mathias Niepert

NeurIPS 2021 Implicit MLE: Backpropagating Through Discrete Exponential Family Distributions Mathias Niepert, Pasquale Minervini, Luca Franceschi

ICLR 2021 Uncertainty Estimation and Calibration with Finite-State Probabilistic RNNs Cheng Wang, Carolin Lawrence, Mathias Niepert

IJCAI 2019 A Comparative Study of Distributional and Symbolic Paradigms for Relational Learning Sebastijan Dumancic, Alberto García-Durán, Mathias Niepert

ICML 2019 Learning Discrete Structures for Graph Neural Networks Luca Franceschi, Mathias Niepert, Massimiliano Pontil, Xiao He

ICML 2019 State-Regularized Recurrent Neural Networks Cheng Wang, Mathias Niepert

UAI 2018 KBlrn: End-to-End Learning of Knowledge Base Representations with Latent, Relational, and Numerical Features Alberto García-Durán, Mathias Niepert

NeurIPS 2017 Learning Graph Representations with Embedding Propagation Alberto Garcia Duran, Mathias Niepert

NeurIPS 2016 Discriminative Gaifman Models Mathias Niepert

ICML 2016 Learning Convolutional Neural Networks for Graphs Mathias Niepert, Mohamed Ahmed, Konstantin Kutzkov

UAI 2015 Learning and Inference in Tractable Probabilistic Knowledge Bases Mathias Niepert, Pedro M. Domingos

AAAI 2015 Lifted Probabilistic Inference for Asymmetric Graphical Models Guy Van den Broeck, Mathias Niepert

ICML 2014 Exchangeable Variable Models Mathias Niepert, Pedro Domingos

AAAI 2014 Tractability Through Exchangeability: A New Perspective on Efficient Probabilistic Inference Mathias Niepert, Guy Van den Broeck

AAAI 2013 RockIt: Exploiting Parallelism and Symmetry for MAP Inference in Statistical Relational Models Jan Noessner, Mathias Niepert, Heiner Stuckenschmidt

AAAI 2013 Symmetry-Aware Marginal Density Estimation Mathias Niepert

UAI 2012 Markov Chains on Orbits of Permutation Groups Mathias Niepert

IJCAI 2011 Log-Linear Description Logics Mathias Niepert, Jan Noessner, Heiner Stuckenschmidt

UAI 2010 A Delayed Column Generation Strategy for Exact K-Bounded MAP Inference in Markov Logic Networks Mathias Niepert

AAAI 2010 A Probabilistic-Logical Framework for Ontology Matching Mathias Niepert, Christian Meilicke, Heiner Stuckenschmidt

UAI 2009 Logical Inference Algorithms and Matrix Representations for Probabilistic Conditional Independence Mathias Niepert

UAI 2008 On the Conditional Independence Implication Problem: A Lattice-Theoretic Approach Mathias Niepert, Dirk Van Gucht, Marc Gyssens