| 2025 | ICLR | Artificial Kuramoto Oscillatory Neurons. | Takeru Miyato, Sindy Lwe, Andreas Geiger, Max Welling |
| 2025 | ICML | Erwin: A Tree-based Hierarchical Transformer for Large-scale Physical Systems. | Maksim Zhdanov, Max Welling, Jan-Willem van de Meent |
| 2025 | ICML | BARNN: A Bayesian Autoregressive and Recurrent Neural Network. | Dario Coscia, Max Welling, Nicola Demo, Gianluigi Rozza |
| 2025 | ICML | Controlled Generation with Equivariant Variational Flow Matching. | Floor Eijkelboom, Heiko Zimmermann, Sharvaree Vadgama, Erik J. Bekkers, Max Welling, Christian A. Naesseth, Jan-Willem van de Meent |
| 2024 | AAAI | Protect Your Score: Contact-Tracing with Differential Privacy Guarantees. | Rob Romijnders, Christos Louizos, Yuki M. Asano, Max Welling |
| 2024 | ICLR | Traveling Waves Encode The Recent Past and Enhance Sequence Learning. | T. Anderson Keller, Lyle Muller, Terrence J. Sejnowski, Max Welling |
| 2024 | ICLR | GTA: A Geometry-Aware Attention Mechanism for Multi-View Transformers. | Takeru Miyato, Bernhard Jaeger, Max Welling, Andreas Geiger |
| 2024 | ICML | Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI. | Theodore Papamarkou, Maria Skoularidou, Konstantina Palla, Laurence Aitchison, Julyan Arbel, David B. Dunson, Maurizio Filippone, Vincent Fortuin, Philipp Hennig, Jos Miguel Hernndez-Lobato, Aliaksandr Hubin, Alexander Immer, Theofanis Karaletsos, Mohammad Emtiyaz Khan, Agustinus Kristiadi, Yingzhen Li, Stephan Mandt, Christopher Nemeth, Michael A. Osborne, Tim G. J. Rudner, David Rgamer, Yee Whye Teh, Max Welling, Andrew Gordon Wilson, Ruqi Zhang |
| 2023 | AISTATS | No time to waste: practical statistical contact tracing with few low-bit messages. | Rob Romijnders, Yuki M. Asano, Christos Louizos, Max Welling |
| 2023 | ICLR | Clifford Neural Layers for PDE Modeling. | Johannes Brandstetter, Rianne van den Berg, Max Welling, Jayesh K. Gupta |
| 2023 | ICML | Neural Wave Machines: Learning Spatiotemporally Structured Representations with Locally Coupled Oscillatory Recurrent Neural Networks. | T. Anderson Keller, Max Welling |
| 2023 | ICML | Geometric Clifford Algebra Networks. | David Ruhe, Jayesh K. Gupta, Steven De Keninck, Max Welling, Johannes Brandstetter |
| 2023 | ICML | Latent Traversals in Generative Models as Potential Flows. | Yue Song, T. Anderson Keller, Nicu Sebe, Max Welling |
| 2022 | AISTATS | Orbital MCMC. | Kirill Neklyudov, Max Welling |
| 2022 | CPAIOR | Deep Policy Dynamic Programming for Vehicle Routing Problems. | Wouter Kool, Herke van Hoof, Joaquim A. S. Gromicho, Max Welling |
| 2022 | ICLR | Geometric and Physical Quantities improve E(3) Equivariant Message Passing. | Johannes Brandstetter, Rob Hesselink, Elise van der Pol, Erik J. Bekkers, Max Welling |
| 2022 | ICLR | Message Passing Neural PDE Solvers. | Johannes Brandstetter, Daniel E. Worrall, Max Welling |
| 2022 | ICLR | Multi-Agent MDP Homomorphic Networks. | Elise van der Pol, Herke van Hoof, Frans A. Oliehoek, Max Welling |
| 2022 | ICML | Lie Point Symmetry Data Augmentation for Neural PDE Solvers. | Johannes Brandstetter, Max Welling, Daniel E. Worrall |
| 2022 | ICML | Equivariant Diffusion for Molecule Generation in 3D. | Emiel Hoogeboom, Victor Garcia Satorras, Clment Vignac, Max Welling |
| 2021 | AISTATS | Sampling in Combinatorial Spaces with SurVAE Flow Augmented MCMC. | Priyank Jaini, Didrik Nielsen, Max Welling |
| 2021 | AISTATS | Neural Enhanced Belief Propagation on Factor Graphs. | Victor Garcia Satorras, Max Welling |
| 2021 | GLOBECOM | Neural Augmentation of Kalman Filter with Hypernetwork for Channel Tracking. | Kumar Pratik, Rana Ali Amjad, Arash Behboodi, Joseph B. Soriaga, Max Welling |
| 2021 | ICLR | Probabilistic Numeric Convolutional Neural Networks. | Marc Anton Finzi, Roberto Bondesan, Max Welling |
| 2021 | ICLR | Gauge Equivariant Mesh CNNs: Anisotropic convolutions on geometric graphs. | Pim de Haan, Maurice Weiler, Taco Cohen, Max Welling |
| 2021 | ICML | The Hintons in your Neural Network: a Quantum Field Theory View of Deep Learning. | Roberto Bondesan, Max Welling |
| 2021 | ICML | A Practical Method for Constructing Equivariant Multilayer Perceptrons for Arbitrary Matrix Groups. | Marc Finzi, Max Welling, Andrew Gordon Wilson |
| 2021 | ICML | Federated Learning of User Verification Models Without Sharing Embeddings. | Hossein Hosseini, Hyunsin Park, Sungrack Yun, Christos Louizos, Joseph Soriaga, Max Welling |
| 2021 | ICML | Self Normalizing Flows. | T. Anderson Keller, Jorn W. T. Peters, Priyank Jaini, Emiel Hoogeboom, Patrick Forr, Max Welling |
| 2021 | ICML | E(n) Equivariant Graph Neural Networks. | Victor Garcia Satorras, Emiel Hoogeboom, Max Welling |
| 2021 | MICCAI | Learning to Predict Error for MRI Reconstruction. | Shi Hu, Nicola Pezzotti, Max Welling |
| 2021 | MICCAI | Evaluation of the Robustness of Learned MR Image Reconstruction to Systematic Deviations Between Training and Test Data for the Models from the fastMRI Challenge. | Patricia M. Johnson, Geunu Jeong, Kerstin Hammernik, Jo Schlemper, Chen Qin, Jinming Duan, Daniel Rueckert, Jingu Lee, Nicola Pezzotti, Elwin de Weerdt, Sahar Yousefi, Mohamed S. Elmahdy, Jeroen Hendrikus Franciscus Van Gemert, Christophe Schlke, Mariya Doneva, Tim Nielsen, Sergey Kastryulin, Boudewijn P. F. Lelieveldt, Matthias J. P. van Osch, Marius Staring, Eric Z. Chen, Puyang Wang, Xiao Chen, Terrence Chen, Vishal M. Patel, Shanhui Sun, Hyungseob Shin, Yohan Jun, Taejoon Eo, Sewon Kim, Taeseong Kim, Dosik Hwang, Patrick Putzky, Dimitrios Karkalousos, Jonas Teuwen, Nikita Miriakov, Bart Bakker, Matthan W. A. Caan, Max Welling, Matthew J. Muckley, Florian Knoll |
| 2021 | UAI | Mixed variable Bayesian optimization with frequency modulated kernels. | ChangYong Oh, Efstratios Gavves, Max Welling |
| 2020 | BMVC | Relational Generalized Few-Shot Learning. | Xiahan Shi, Leonard Salewski, Martin Schiegg, Max Welling |
| 2020 | CVPR | Guided Variational Autoencoder for Disentanglement Learning. | Zheng Ding, Yifan Xu, Weijian Xu, Gaurav Parmar, Yang Yang, Max Welling, Zhuowen Tu |
| 2020 | ICLR | Estimating Gradients for Discrete Random Variables by Sampling without Replacement. | Wouter Kool, Herke van Hoof, Max Welling |
| 2020 | ICLR | Gradient $\ell_1$ Regularization for Quantization Robustness. | Milad Alizadeh, Arash Behboodi, Mart van Baalen, Christos Louizos, Tijmen Blankevoort, Max Welling |
| 2020 | ICLR | Batch-shaping for learning conditional channel gated networks. | Babak Ehteshami Bejnordi, Tijmen Blankevoort, Max Welling |
| 2020 | ICLR | Contrastive Learning of Structured World Models. | Thomas N. Kipf, Elise van der Pol, Max Welling |
| 2020 | ICLR | To Relieve Your Headache of Training an MRF, Take AdVIL. | Chongxuan Li, Chao Du, Kun Xu, Max Welling, Jun Zhu, Bo Zhang |
| 2020 | ICML | Involutive MCMC: a Unifying Framework. | Kirill Neklyudov, Max Welling, Evgenii Egorov, Dmitry P. Vetrov |
| 2020 | IJCAI | Variational Bayes in Private Settings (VIPS) (Extended Abstract). | James R. Foulds, Mijung Park, Kamalika Chaudhuri, Max Welling |
| 2020 | ICPRAM | Integrating Generative Modeling into Deep Learning. | Max Welling |
| 2019 | AISTATS | Training a Spiking Neural Network with Equilibrium Propagation. | Peter O'Connor, Efstratios Gavves, Max Welling |
| 2019 | ICCV | Data-Free Quantization Through Weight Equalization and Bias Correction. | Markus Nagel, Mart van Baalen, Tijmen Blankevoort, Max Welling |
| 2019 | ICLR | The Deep Weight Prior. | Andrei Atanov, Arsenii Ashukha, Kirill Struminsky, Dmitry P. Vetrov, Max Welling |
| 2019 | ICLR | DIVA: Domain Invariant Variational Autoencoder. | Maximilian Ilse, Jakub M. Tomczak, Christos Louizos, Max Welling |
| 2019 | ICLR | Attention, Learn to Solve Routing Problems! | Wouter Kool, Herke van Hoof, Max Welling |
| 2019 | ICLR | Buy 4 REINFORCE Samples, Get a Baseline for Free! | Wouter Kool, Herke van Hoof, Max Welling |
| 2019 | ICLR | Relaxed Quantization for Discretized Neural Networks. | Christos Louizos, Matthias Reisser, Tijmen Blankevoort, Efstratios Gavves, Max Welling |
| 2019 | ICLR | Initialized Equilibrium Propagation for Backprop-Free Training. | Peter O'Connor, Efstratios Gavves, Max Welling |
| 2019 | ICML | Gauge Equivariant Convolutional Networks and the Icosahedral CNN. | Taco Cohen, Maurice Weiler, Berkay Kicanaoglu, Max Welling |
| 2019 | ICML | Emerging Convolutions for Generative Normalizing Flows. | Emiel Hoogeboom, Rianne van den Berg, Max Welling |
| 2019 | ICML | Stochastic Beams and Where To Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement. | Wouter Kool, Herke van Hoof, Max Welling |
| 2019 | MICCAI | Supervised Uncertainty Quantification for Segmentation with Multiple Annotations. | Shi Hu, Daniel E. Worrall, Stefan Knegt, Bas Veeling, Henkjan J. Huisman, Max Welling |
| 2019 | UAI | Sinkhorn AutoEncoders. | Giorgio Patrini, Rianne van den Berg, Patrick Forr, Marcello Carioni, Samarth Bhargav, Max Welling, Tim Genewein, Frank Nielsen |
| 2019 | UAI | Differentiable Probabilistic Models of Scientific Imaging with the Fourier Slice Theorem. | Karen Ullrich, Rianne van den Berg, Marcus A. Brubaker, David J. Fleet, Max Welling |
| 2018 | AISTATS | VAE with a VampPrior. | Jakub M. Tomczak, Max Welling |
| 2018 | ICLR | Spherical CNNs. | Taco S. Cohen, Mario Geiger, Jonas Khler, Max Welling |
| 2018 | ICLR | HexaConv. | Emiel Hoogeboom, Jorn W. T. Peters, Taco S. Cohen, Max Welling |
| 2018 | ICLR | Learning Sparse Neural Networks through L_0 Regularization. | Christos Louizos, Max Welling, Diederik P. Kingma |
| 2018 | ICLR | Temporally Efficient Deep Learning with Spikes. | Peter O'Connor, Efstratios Gavves, Matthias Reisser, Max Welling |
| 2018 | ICML | Attention-based Deep Multiple Instance Learning. | Maximilian Ilse, Jakub M. Tomczak, Max Welling |
| 2018 | ICML | Neural Relational Inference for Interacting Systems. | Thomas N. Kipf, Ethan Fetaya, Kuan-Chieh Wang, Max Welling, Richard S. Zemel |
| 2018 | ICML | BOCK : Bayesian Optimization with Cylindrical Kernels. | ChangYong Oh, Efstratios Gavves, Max Welling |
| 2018 | MICCAI | Mean Field Network Based Graph Refinement with Application to Airway Tree Extraction. | Raghavendra Selvan, Max Welling, Jesper Holst Pedersen, Jens Petersen, Marleen de Bruijne |
| 2018 | MICCAI | Rotation Equivariant CNNs for Digital Pathology. | Bastiaan S. Veeling, Jasper Linmans, Jim Winkens, Taco Cohen, Max Welling |
| 2018 | UAI | Sylvester Normalizing Flows for Variational Inference. | Rianne van den Berg, Leonard Hasenclever, Jakub M. Tomczak, Max Welling |
| 2017 | AISTATS | DP-EM: Differentially Private Expectation Maximization. | Mijung Park, James R. Foulds, Kamalika Choudhary, Max Welling |
| 2017 | ICLR | Steerable CNNs. | Taco S. Cohen, Max Welling |
| 2017 | ICLR | Semi-Supervised Classification with Graph Convolutional Networks. | Thomas N. Kipf, Max Welling |
| 2017 | ICLR | Sigma Delta Quantized Networks. | Peter O'Connor, Max Welling |
| 2017 | ICLR | Soft Weight-Sharing for Neural Network Compression. | Karen Ullrich, Edward Meeds, Max Welling |
| 2017 | ICLR | Visualizing Deep Neural Network Decisions: Prediction Difference Analysis. | Luisa M. Zintgraf, Taco S. Cohen, Tameem Adel, Max Welling |
| 2017 | ICML | Multiplicative Normalizing Flows for Variational Bayesian Neural Networks. | Christos Louizos, Max Welling |
| 2016 | AISTATS | Scalable MCMC for Mixed Membership Stochastic Blockmodels. | Wenzhe Li, Sungjin Ahn, Max Welling |
| 2016 | ICML | Group Equivariant Convolutional Networks. | Taco Cohen, Max Welling |
| 2016 | ICML | Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors. | Christos Louizos, Max Welling |
| 2016 | UAI | On the Theory and Practice of Privacy-Preserving Bayesian Data Analysis. | James R. Foulds, Joseph Geumlek, Max Welling, Kamalika Chaudhuri |
| 2015 | ICML | Harmonic Exponential Families on Manifolds. | Taco Cohen, Max Welling |
| 2015 | ICML | Markov Chain Monte Carlo and Variational Inference: Bridging the Gap. | Tim Salimans, Diederik P. Kingma, Max Welling |
| 2015 | KDD | Large-Scale Distributed Bayesian Matrix Factorization using Stochastic Gradient MCMC. | Sungjin Ahn, Anoop Korattikara, Nathan Liu, Suju Rajan, Max Welling |
| 2015 | UAI | Hamiltonian ABC. | Edward Meeds, Robert Leenders, Max Welling |
| 2014 | AISTATS | Approximate Slice Sampling for Bayesian Posterior Inference. | Christopher DuBois, Anoop Korattikara Balan, Max Welling, Padhraic Smyth |
| 2014 | ICML | Distributed Stochastic Gradient MCMC. | Sungjin Ahn, Babak Shahbaba, Max Welling |
| 2014 | ICML | Austerity in MCMC Land: Cutting the Metropolis-Hastings Budget. | Anoop Korattikara Balan, Yutian Chen, Max Welling |
| 2014 | ICML | Learning the Irreducible Representations of Commutative Lie Groups. | Taco Cohen, Max Welling |
| 2014 | ICML | Efficient Gradient-Based Inference through Transformations between Bayes Nets and Neural Nets. | Diederik P. Kingma, Max Welling |
| 2014 | UAI | GPS-ABC: Gaussian Process Surrogate Approximate Bayesian Computation. | Edward Meeds, Max Welling |
| 2013 | AISTATS | Distributed and Adaptive Darting Monte Carlo through Regenerations. | Sungjin Ahn, Yutian Chen, Max Welling |
| 2013 | AISTATS | Evidence Estimation for Bayesian Partially Observed MRFs. | Yutian Chen, Max Welling |
| 2013 | CVPR | A Lazy Man's Approach to Benchmarking: Semisupervised Classifier Evaluation and Recalibration. | Peter Welinder, Max Welling, Pietro Perona |
| 2013 | KDD | Stochastic collapsed variational Bayesian inference for latent Dirichlet allocation. | James R. Foulds, Levi Boyles, Christopher DuBois, Padhraic Smyth, Max Welling |
| 2012 | ICML | Bayesian Posterior Sampling via Stochastic Gradient Fisher Scoring. | Sungjin Ahn, Anoop Korattikara Balan, Max Welling |
| 2012 | ITA | Exchangeable inconsistent priors for Bayesian posterior inference. | Max Welling, Ian Porteous, Kenichi Kurihara |
| 2012 | UAI | Bayesian Structure Learning for Markov Random Fields with a Spike and Slab Prior. | Yutian Chen, Max Welling |
| 2012 | UAI | Generalized Belief Propagation on Tree Robust Structured Region Graphs. | Andrew Gelfand, Max Welling |
| 2012 | UAI | A Cluster-Cumulant Expansion at the Fixed Points of Belief Propagation. | Max Welling, Andrew Gelfand, Alexander Ihler |
| 2011 | ICCV | Integrating local classifiers through nonlinear dynamics on label graphs with an application to image segmentation. | Yutian Chen, Andrew Gelfand, Charless C. Fowlkes, Max Welling |
| 2011 | ICML | Bayesian Learning via Stochastic Gradient Langevin Dynamics. | Max Welling, Yee Whye Teh |
| 2010 | AAAI | Bayesian Matrix Factorization with Side Information and Dirichlet Process Mixtures. | Ian Porteous, Arthur U. Asuncion, Max Welling |
| 2010 | ICML | Dynamical Products of Experts for Modeling Financial Time Series. | Yutian Chen, Max Welling |
| 2010 | UAI | Super-Samples from Kernel Herding. | Yutian Chen, Max Welling, Alexander J. Smola |
| 2009 | ICML | Herding dynamical weights to learn. | Max Welling |
| 2009 | IJCAI | Bayesian Extreme Components Analysis. | Yutian Chen, Max Welling |
| 2009 | UAI | On Smoothing and Inference for Topic Models. | Arthur U. Asuncion, Max Welling, Padhraic Smyth, Yee Whye Teh |
| 2009 | UAI | Herding Dynamic Weights for Partially Observed Random Field Models. | Max Welling |
| 2008 | AAAI | Multi-HDP: A Non Parametric Bayesian Model for Tensor Factorization. | Ian Porteous, Evgeniy Bart, Max Welling |
| 2008 | CVPR | Unsupervised learning of visual taxonomies. | Evgeniy Bart, Ian Porteous, Pietro Perona, Max Welling |
| 2008 | CVPR | Incremental learning of nonparametric Bayesian mixture models. | Ryan Gomes, Max Welling, Pietro Perona |
| 2008 | ICML | Memory bounded inference in topic models. | Ryan Gomes, Max Welling, Pietro Perona |
| 2008 | KDD | Fast collapsed gibbs sampling for latent dirichlet allocation. | Ian Porteous, David Newman, Alexander Ihler, Arthur U. Asuncion, Padhraic Smyth, Max Welling |
| 2008 | UAI | Hybrid Variational/Gibbs Collapsed Inference in Topic Models. | Max Welling, Yee Whye Teh, Bert Kappen |
| 2008 | SDM | Deterministic Latent Variable Models and Their Pitfalls. | Max Welling, Chaitanya Chemudugunta, Nathan Sutter |
| 2007 | ICANN | A Distributed Message Passing Algorithm for Sensor Localization. | Max Welling, Joseph J. Lim |
| 2007 | IJCAI | Collapsed Variational Dirichlet Process Mixture Models. | Kenichi Kurihara, Max Welling, Yee Whye Teh |
| 2006 | ICML | The rate adapting poisson model for information retrieval and object recognition. | Peter V. Gehler, Alex Holub, Max Welling |
| 2006 | UAI | Gibbs Sampling for (Coupled) Infinite Mixture Models in the Stick Breaking Representation. | Ian Porteous, Alexander T. Ihler, Padhraic Smyth, Max Welling |
| 2006 | UAI | Bayesian Random Fields: The Bethe-Laplace Approximation. | Max Welling, Sridevi Parise |
| 2006 | SDM | Bayesian K-Means as a "Maximization-Expectation" Algorithm. | Max Welling, Kenichi Kurihara |
| 2005 | AISTATS | An Expectation Maximization Algorithm for Inferring Offset-Normal Shape Distributions. | Max Welling |
| 2005 | AISTATS | Robust Higher Order Statistics. | Max Welling |
| 2005 | AISTATS | Learning in Markov Random Fields with Contrastive Free Energies. | Max Welling, Charles Sutton |
| 2005 | ICCV | Combining Generative Models and Fisher Kernels for Object Recognition. | Alex Holub, Max Welling, Pietro Perona |
| 2005 | UAI | Structured Region Graphs: Morphing EP into GBP. | Max Welling, Thomas P. Minka, Yee Whye Teh |
| 2004 | ICML | Approximate inference by Markov chains on union spaces. | Max Welling, Michal Rosen-Zvi, Yee Whye Teh |
| 2004 | UAI | On the Choice of Regions for Generalized Belief Propagation. | Max Welling |
| 2003 | AISTATS | On Improving the Efficiency of the Iterative Proportional Fitting Procedure. | Yee Whye Teh, Max Welling |
| 2003 | UAI | Efficient Parametric Projection Pursuit Density Estimation. | Max Welling, Richard S. Zemel, Geoffrey E. Hinton |
| 2002 | ICANN | A New Learning Algorithm for Mean Field Boltzmann Machines. | Max Welling, Geoffrey E. Hinton |
| 2001 | UAI | Belief Optimization for Binary Networks: A Stable Alternative to Loopy Belief Propagation. | Max Welling, Yee Whye Teh |
| 2000 | CVPR | Towards Automatic Discovery of Object Categories. | Markus Weber, Max Welling, Pietro Perona |
| 2000 | ECCV | Unsupervised Learning of Models for Recognition. | Markus Weber, Max Welling, Pietro Perona |