Welling, Max

209 publications

TMLR 2025 Adaptive Mesh Quantization for Neural PDE Solvers Winfried van den Dool, Maksim Zhdanov, Yuki M Asano, Max Welling

ICLR 2025 Artificial Kuramoto Oscillatory Neurons Takeru Miyato, Sindy Löwe, Andreas Geiger, Max Welling

ICML 2025 BARNN: A Bayesian Autoregressive and Recurrent Neural Network Dario Coscia, Max Welling, Nicola Demo, Gianluigi Rozza

ICML 2025 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

ICML 2025 Erwin: A Tree-Based Hierarchical Transformer for Large-Scale Physical Systems Maksim Zhdanov, Max Welling, Jan-Willem Van De Meent

NeurIPS 2025 Kuramoto Orientation Diffusion Models Yue Song, T. Anderson Keller, Sevan Brodjian, Takeru Miyato, Yisong Yue, Pietro Perona, Max Welling

ICLRW 2024 DNA: Differential Privacy Neural Augmentation for Contact Tracing Rob Romijnders, Christos Louizos, Yuki M Asano, Max Welling

ICLR 2024 GTA: A Geometry-Aware Attention Mechanism for Multi-View Transformers Takeru Miyato, Bernhard Jaeger, Max Welling, Andreas Geiger

NeurIPSW 2024 Learning Protocols for Non-Equilibrium Conformational Free-Energy Estimation Using Optimal Transport and Conditional Flow Matching Lars Holdijk, Michael M. Bronstein, Max Welling

ICML 2024 Position: Bayesian Deep Learning Is Needed in the Age of Large-Scale AI Theodore Papamarkou, Maria Skoularidou, Konstantina Palla, Laurence Aitchison, Julyan Arbel, David Dunson, Maurizio Filippone, Vincent Fortuin, Philipp Hennig, José Miguel Hernández-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 Rügamer, Yee Whye Teh, Max Welling, Andrew Gordon Wilson, Ruqi Zhang

AAAI 2024 Protect Your Score: Contact-Tracing with Differential Privacy Guarantees Rob Romijnders, Christos Louizos, Yuki M. Asano, Max Welling

ICLR 2024 Traveling Waves Encode the Recent past and Enhance Sequence Learning T. Anderson Keller, Lyle Muller, Terrence Sejnowski, Max Welling

NeurIPS 2024 Variational Flow Matching for Graph Generation Floor Eijkelboom, Grigory Bartosh, Christian A. Naesseth, Max Welling, Jan-Willem van de Meent

ICLR 2023 Clifford Neural Layers for PDE Modeling Johannes Brandstetter, Rianne van den Berg, Max Welling, Jayesh K Gupta

ICCVW 2023 Efficient Neural PDE-Solvers Using Quantization Aware Training Winfried van den Dool, Tijmen Blankevoort, Max Welling, Yuki M. Asano

NeurIPS 2023 Flow Factorized Representation Learning Yue Song, Andy Keller, Nicu Sebe, Max Welling

ICML 2023 Geometric Clifford Algebra Networks David Ruhe, Jayesh K Gupta, Steven De Keninck, Max Welling, Johannes Brandstetter

ICML 2023 Latent Traversals in Generative Models as Potential Flows Yue Song, T. Anderson Keller, Nicu Sebe, Max Welling

ECML-PKDD 2023 Learning Objective-Specific Active Learning Strategies with Attentive Neural Processes Tim Bakker, Herke van Hoof, Max Welling

ICMLW 2023 Lie Point Symmetry and Physics Informed Networks Tara Akhound-Sadegh, Laurence Perreault-Levasseur, Johannes Brandstetter, Max Welling, Siamak Ravanbakhsh

NeurIPS 2023 Lie Point Symmetry and Physics-Informed Networks Tara Akhound-Sadegh, Laurence Perreault-Levasseur, Johannes Brandstetter, Max Welling, Siamak Ravanbakhsh

ICML 2023 Neural Wave Machines: Learning Spatiotemporally Structured Representations with Locally Coupled Oscillatory Recurrent Neural Networks T. Anderson Keller, Max Welling

AISTATS 2023 No Time to Waste: Practical Statistical Contact Tracing with Few Low-Bit Messages Rob Romijnders, Yuki M. Asano, Christos Louizos, Max Welling

NeurIPS 2023 Rotating Features for Object Discovery Sindy Löwe, Phillip Lippe, Francesco Locatello, Max Welling

NeurIPS 2023 Stochastic Optimal Control for Collective Variable Free Sampling of Molecular Transition Paths Lars Holdijk, Yuanqi Du, Ferry Hooft, Priyank Jaini, Berend Ensing, Max Welling

ICLRW 2023 The END: An Equivariant Neural Decoder for Quantum Error Correction Evgenii Egorov, Roberto Bondesan, Max Welling

NeurIPS 2023 Wasserstein Quantum Monte Carlo: A Novel Approach for Solving the Quantum Many-Body Schrödinger Equation Kirill Neklyudov, Jannes Nys, Luca Thiede, Juan Carrasquilla, Qiang Liu, Max Welling, Alireza Makhzani

AISTATS 2022 Orbital MCMC Kirill Neklyudov, Max Welling

NeurIPS 2022 Alleviating Adversarial Attacks on Variational Autoencoders with MCMC Anna Kuzina, Max Welling, Jakub Tomczak

CLeaR 2022 Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data Sindy Löwe, David Madras, Richard Zemel, Max Welling

NeurIPS 2022 Batch Bayesian Optimization on Permutations Using the Acquisition Weighted Kernel Changyong Oh, Roberto Bondesan, Efstratios Gavves, Max Welling

TMLR 2022 Complex-Valued Autoencoders for Object Discovery Sindy Löwe, Phillip Lippe, Maja Rudolph, Max Welling

NeurIPSW 2022 Equivariant 3D-Conditional Diffusion Models for Molecular Linker Design Ilia Igashov, Hannes Stärk, Clement Vignac, Victor Garcia Satorras, Pascal Frossard, Max Welling, Michael M. Bronstein, Bruno Correia

ICML 2022 Equivariant Diffusion for Molecule Generation in 3D Emiel Hoogeboom, Vı́ctor Garcia Satorras, Clément Vignac, Max Welling

ICLR 2022 Geometric and Physical Quantities Improve E(3) Equivariant Message Passing Johannes Brandstetter, Rob Hesselink, Elise van der Pol, Erik J Bekkers, Max Welling

ICML 2022 Lie Point Symmetry Data Augmentation for Neural PDE Solvers Johannes Brandstetter, Max Welling, Daniel E Worrall

ICLR 2022 Message Passing Neural PDE Solvers Johannes Brandstetter, Daniel E. Worrall, Max Welling

ICLR 2022 Multi-Agent MDP Homomorphic Networks Elise van der Pol, Herke van Hoof, Frans A Oliehoek, Max Welling

NeurIPS 2022 On the Symmetries of the Synchronization Problem in Cryo-EM: Multi-Frequency Vector Diffusion Maps on the Projective Plane Gabriele Cesa, Arash Behboodi, Taco S Cohen, Max Welling

ICMLW 2022 Path Integral Stochastic Optimal Control for Sampling Transition Paths Lars Holdijk, Yuanqi Du, Priyank Jaini, Ferry Hooft, Bernd Ensing, Max Welling

AISTATS 2021 Neural Enhanced Belief Propagation on Factor Graphs Víctor Garcia Satorras, Max Welling

AISTATS 2021 Sampling in Combinatorial Spaces with SurVAE Flow Augmented MCMC Priyank Jaini, Didrik Nielsen, Max Welling

ICML 2021 A Practical Method for Constructing Equivariant Multilayer Perceptrons for Arbitrary Matrix Groups Marc Finzi, Max Welling, Andrew Gordon Wilson

NeurIPS 2021 Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions Emiel Hoogeboom, Didrik Nielsen, Priyank Jaini, Patrick Forré, Max Welling

ICML 2021 E(n) Equivariant Graph Neural Networks Vı́ctor Garcia Satorras, Emiel Hoogeboom, Max Welling

NeurIPS 2021 E(n) Equivariant Normalizing Flows Victor Garcia Satorras, Emiel Hoogeboom, Fabian Fuchs, Ingmar Posner, Max Welling

ICML 2021 Federated Learning of User Verification Models Without Sharing Embeddings Hossein Hosseini, Hyunsin Park, Sungrack Yun, Christos Louizos, Joseph Soriaga, Max Welling

ICLR 2021 Gauge Equivariant Mesh CNNs: Anisotropic Convolutions on Geometric Graphs Pim De Haan, Maurice Weiler, Taco Cohen, Max Welling

NeurIPS 2021 Learning Equivariant Energy Based Models with Equivariant Stein Variational Gradient Descent Priyank Jaini, Lars Holdijk, Max Welling

UAI 2021 Mixed Variable Bayesian Optimization with Frequency Modulated Kernels Changyong Oh, Efstratios Gavves, Max Welling

NeurIPS 2021 Modality-Agnostic Topology Aware Localization Farhad Ghazvinian Zanjani, Ilia Karmanov, Hanno Ackermann, Daniel Dijkman, Simone Merlin, Max Welling, Fatih Porikli

NeurIPSW 2021 Modeling Category-Selective Cortical Regions with Topographic Variational Autoencoders T. Anderson Keller, Qinghe Gao, Max Welling

NeurIPSW 2021 Particle Dynamics for Learning EBMs Kirill Neklyudov, Priyank Jaini, Max Welling

ICCVW 2021 Predictive Coding with Topographic Variational Autoencoders T. Anderson Keller, Max Welling

ICLR 2021 Probabilistic Numeric Convolutional Neural Networks Marc Anton Finzi, Roberto Bondesan, Max Welling

ICML 2021 Self Normalizing Flows Thomas A Keller, Jorn W.T. Peters, Priyank Jaini, Emiel Hoogeboom, Patrick Forré, Max Welling

ICML 2021 The Hintons in Your Neural Network: A Quantum Field Theory View of Deep Learning Roberto Bondesan, Max Welling

NeurIPS 2021 Topographic VAEs Learn Equivariant Capsules T. Anderson Keller, Max Welling

JMLR 2020 Ancestral Gumbel-Top-K Sampling for Sampling Without Replacement Wouter Kool, Herke van Hoof, Max Welling

ICLR 2020 Batch-Shaping for Learning Conditional Channel Gated Networks Babak Ehteshami Bejnordi, Tijmen Blankevoort, Max Welling

NeurIPS 2020 Bayesian Bits: Unifying Quantization and Pruning Mart van Baalen, Christos Louizos, Markus Nagel, Rana Ali Amjad, Ying Wang, Tijmen Blankevoort, Max Welling

ICLR 2020 Contrastive Learning of Structured World Models Thomas Kipf, Elise van der Pol, Max Welling

ICLR 2020 Estimating Gradients for Discrete Random Variables by Sampling Without Replacement Wouter Kool, Herke van Hoof, Max Welling

NeurIPS 2020 Experimental Design for MRI by Greedy Policy Search Tim Bakker, Herke van Hoof, Max Welling

ICLR 2020 Gradient $\ell_1$ Regularization for Quantization Robustness Milad Alizadeh, Arash Behboodi, Mart van Baalen, Christos Louizos, Tijmen Blankevoort, Max Welling

ICML 2020 Involutive MCMC: A Unifying Framework Kirill Neklyudov, Max Welling, Evgenii Egorov, Dmitry Vetrov

NeurIPS 2020 MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning Elise van der Pol, Daniel Worrall, Herke van Hoof, Frans Oliehoek, Max Welling

NeurIPS 2020 Natural Graph Networks Pim de Haan, Taco S Cohen, Max Welling

NeurIPS 2020 SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks Fabian Fuchs, Daniel Worrall, Volker Fischer, Max Welling

NeurIPS 2020 SurVAE Flows: Surjections to Bridge the Gap Between VAEs and Flows Didrik Nielsen, Priyank Jaini, Emiel Hoogeboom, Ole Winther, Max Welling

NeurIPS 2020 The Convolution Exponential and Generalized Sylvester Flows Emiel Hoogeboom, Victor Garcia Satorras, Jakub Tomczak, Max Welling

ICLR 2020 To Relieve Your Headache of Training an MRF, Take AdVIL Chongxuan Li, Chao Du, Kun Xu, Max Welling, Jun Zhu, Bo Zhang

JAIR 2020 Variational Bayes in Private Settings (VIPS) Mijung Park, James R. Foulds, Kamalika Chaudhuri, Max Welling

IJCAI 2020 Variational Bayes in Private Settings (VIPS) (Extended Abstract) James R. Foulds, Mijung Park, Kamalika Chaudhuri, Max Welling

FnTML 2019 An Introduction to Variational Autoencoders Diederik P. Kingma, Max Welling

ICLR 2019 Attention, Learn to Solve Routing Problems! Wouter Kool, Herke van Hoof, Max Welling

ICLRW 2019 Buy 4 REINFORCE Samples, Get a Baseline for Free! Wouter Kool, Herke van Hoof, Max Welling

NeurIPS 2019 Combinatorial Bayesian Optimization Using the Graph Cartesian Product Changyong Oh, Jakub Tomczak, Efstratios Gavves, Max Welling

NeurIPS 2019 Combining Generative and Discriminative Models for Hybrid Inference Victor Garcia Satorras, Zeynep Akata, Max Welling

ICLRW 2019 DIVA: Domain Invariant Variational Autoencoder Maximilian Ilse, Jakub M. Tomczak, Christos Louizos, Max Welling

NeurIPS 2019 Deep Scale-Spaces: Equivariance over Scale Daniel Worrall, Max Welling

UAI 2019 Differentiable Probabilistic Models of Scientific Imaging with the Fourier Slice Theorem Karen Ullrich, Rianne Berg, Marcus Brubaker, David Fleet, Max Welling

ICML 2019 Emerging Convolutions for Generative Normalizing Flows Emiel Hoogeboom, Rianne Van Den Berg, Max Welling

ICML 2019 Gauge Equivariant Convolutional Networks and the Icosahedral CNN Taco Cohen, Maurice Weiler, Berkay Kicanaoglu, Max Welling

ICLR 2019 Initialized Equilibrium Propagation for Backprop-Free Training Peter O'Connor, Efstratios Gavves, Max Welling

NeurIPS 2019 Integer Discrete Flows and Lossless Compression Emiel Hoogeboom, Jorn Peters, Rianne van den Berg, Max Welling

NeurIPS 2019 Invert to Learn to Invert Patrick Putzky, Max Welling

ICLR 2019 Relaxed Quantization for Discretized Neural Networks Christos Louizos, Matthias Reisser, Tijmen Blankevoort, Efstratios Gavves, Max Welling

UAI 2019 Sinkhorn AutoEncoders Giorgio Patrini, Rianne Berg, Patrick Forré, Marcello Carioni, Samarth Bhargav, Max Welling, Tim Genewein, Frank Nielsen

ECML-PKDD 2019 Stochastic Activation Actor Critic Methods Wenling Shang, Douwe van der Wal, Herke van Hoof, Max Welling

ICML 2019 Stochastic Beams and Where to Find Them: The Gumbel-Top-K Trick for Sampling Sequences Without Replacement Wouter Kool, Herke Van Hoof, Max Welling

ICLR 2019 The Deep Weight Prior Andrei Atanov, Arsenii Ashukha, Kirill Struminsky, Dmitriy Vetrov, Max Welling

NeurIPS 2019 The Functional Neural Process Christos Louizos, Xiahan Shi, Klamer Schutte, Max Welling

AISTATS 2019 Training a Spiking Neural Network with Equilibrium Propagation Peter O’Connor, Efstratios Gavves, Max Welling

NeurIPS 2018 3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data Maurice Weiler, Mario Geiger, Max Welling, Wouter Boomsma, Taco S Cohen

ICML 2018 Attention-Based Deep Multiple Instance Learning Maximilian Ilse, Jakub Tomczak, Max Welling

ICML 2018 BOCK : Bayesian Optimization with Cylindrical Kernels ChangYong Oh, Efstratios Gavves, Max Welling

NeurIPS 2018 Graphical Generative Adversarial Networks Chongxuan Li, Max Welling, Jun Zhu, Bo Zhang

ICLR 2018 HexaConv Emiel Hoogeboom, Jorn W.T. Peters, Taco S. Cohen, Max Welling

ICLR 2018 Learning Sparse Neural Networks Through L_0 Regularization Christos Louizos, Max Welling, Diederik P. Kingma

ICML 2018 Neural Relational Inference for Interacting Systems Thomas Kipf, Ethan Fetaya, Kuan-Chieh Wang, Max Welling, Richard Zemel

ICLR 2018 Spherical CNNs Taco S. Cohen, Mario Geiger, Jonas Köhler, Max Welling

UAI 2018 Sylvester Normalizing Flows for Variational Inference Rianne van den Berg, Leonard Hasenclever, Jakub M. Tomczak, Max Welling

ICLR 2018 Temporally Efficient Deep Learning with Spikes Peter O'Connor, Efstratios Gavves, Matthias Reisser, Max Welling

AISTATS 2018 VAE with a VampPrior Jakub M. Tomczak, Max Welling

NeurIPS 2017 Bayesian Compression for Deep Learning Christos Louizos, Karen Ullrich, Max Welling

NeurIPS 2017 Causal Effect Inference with Deep Latent-Variable Models Christos Louizos, Uri Shalit, Joris M. Mooij, David Sontag, Richard Zemel, Max Welling

AISTATS 2017 DP-EM: Differentially Private Expectation Maximization Mijung Park, James R. Foulds, Kamalika Choudhary, Max Welling

ICML 2017 Multiplicative Normalizing Flows for Variational Bayesian Neural Networks Christos Louizos, Max Welling

ICLR 2017 Semi-Supervised Classification with Graph Convolutional Networks Thomas N. Kipf, Max Welling

ICLR 2017 Sigma Delta Quantized Networks Peter O'Connor, Max Welling

ICLR 2017 Soft Weight-Sharing for Neural Network Compression Karen Ullrich, Edward Meeds, Max Welling

ICLR 2017 Steerable CNNs Taco S. Cohen, Max Welling

ICLR 2017 Visualizing Deep Neural Network Decisions: Prediction Difference Analysis Luisa M. Zintgraf, Taco S. Cohen, Tameem Adel, Max Welling

ECCV 2016 Computer Vision - ECCV 2016 - 14th European Conference, Amsterdam, the Netherlands, October 11-14, 2016, Proceedings, Part I Bastian Leibe, Jiri Matas, Nicu Sebe, Max Welling

ECCV 2016 Computer Vision - ECCV 2016 - 14th European Conference, Amsterdam, the Netherlands, October 11-14, 2016, Proceedings, Part II Bastian Leibe, Jiri Matas, Nicu Sebe, Max Welling

ECCV 2016 Computer Vision - ECCV 2016 - 14th European Conference, Amsterdam, the Netherlands, October 11-14, 2016, Proceedings, Part III Bastian Leibe, Jiri Matas, Nicu Sebe, Max Welling

ECCV 2016 Computer Vision - ECCV 2016 - 14th European Conference, Amsterdam, the Netherlands, October 11-14, 2016, Proceedings, Part IV Bastian Leibe, Jiri Matas, Nicu Sebe, Max Welling

ECCV 2016 Computer Vision - ECCV 2016 - 14th European Conference, Amsterdam, the Netherlands, October 11-14, 2016, Proceedings, Part V Bastian Leibe, Jiri Matas, Nicu Sebe, Max Welling

ECCV 2016 Computer Vision - ECCV 2016 - 14th European Conference, Amsterdam, the Netherlands, October 11-14, 2016, Proceedings, Part VI Bastian Leibe, Jiri Matas, Nicu Sebe, Max Welling

ECCV 2016 Computer Vision - ECCV 2016 - 14th European Conference, Amsterdam, the Netherlands, October 11-14, 2016, Proceedings, Part VII Bastian Leibe, Jiri Matas, Nicu Sebe, Max Welling

ECCV 2016 Computer Vision - ECCV 2016 - 14th European Conference, Amsterdam, the Netherlands, October 11-14, 2016, Proceedings, Part VIII Bastian Leibe, Jiri Matas, Nicu Sebe, Max Welling

ICML 2016 Group Equivariant Convolutional Networks Taco Cohen, Max Welling

JMLR 2016 Herded Gibbs Sampling Yutian Chen, Luke Bornn, Nando de Freitas, Mareija Eskelin, Jing Fang, Max Welling

NeurIPS 2016 Improved Variational Inference with Inverse Autoregressive Flow Diederik P. Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, Max Welling

UAI 2016 On the Theory and Practice of Privacy-Preserving Bayesian Data Analysis James R. Foulds, Joseph Geumlek, Max Welling, Kamalika Chaudhuri

AISTATS 2016 Scalable MCMC for Mixed Membership Stochastic Blockmodels Wenzhe Li, Sungjin Ahn, Max Welling

ICML 2016 Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors Christos Louizos, Max Welling

ICLR 2016 The Variational Fair Autoencoder Christos Louizos, Kevin Swersky, Yujia Li, Max Welling, Richard S. Zemel

NeurIPS 2015 Bayesian Dark Knowledge Anoop Korattikara Balan, Vivek Rathod, Kevin P. Murphy, Max Welling

UAI 2015 Hamiltonian ABC Edward Meeds, Robert Leenders, Max Welling

ICML 2015 Harmonic Exponential Families on Manifolds Taco Cohen, Max Welling

ICML 2015 Markov Chain Monte Carlo and Variational Inference: Bridging the Gap Tim Salimans, Diederik Kingma, Max Welling

NeurIPS 2015 Optimization Monte Carlo: Efficient and Embarrassingly Parallel Likelihood-Free Inference Ted Meeds, Max Welling

ICLR 2015 Transformation Properties of Learned Visual Representations Taco S. Cohen, Max Welling

NeurIPS 2015 Variational Dropout and the Local Reparameterization Trick Diederik P. Kingma, Tim Salimans, Max Welling

AISTATS 2014 Approximate Slice Sampling for Bayesian Posterior Inference Christopher DuBois, Anoop Korattikara Balan, Max Welling, Padhraic Smyth

ICML 2014 Austerity in MCMC Land: Cutting the Metropolis-Hastings Budget Anoop Korattikara, Yutian Chen, Max Welling

ICLR 2014 Auto-Encoding Variational Bayes Diederik P. Kingma, Max Welling

ICML 2014 Distributed Stochastic Gradient MCMC Sungjin Ahn, Babak Shahbaba, Max Welling

ICML 2014 Efficient Gradient-Based Inference Through Transformations Between Bayes Nets and Neural Nets Diederik Kingma, Max Welling

UAI 2014 GPS-ABC: Gaussian Process Surrogate Approximate Bayesian Computation Edward Meeds, Max Welling

ICML 2014 Learning the Irreducible Representations of Commutative Lie Groups Taco Cohen, Max Welling

NeurIPS 2014 Semi-Supervised Learning with Deep Generative Models Diederik P. Kingma, Shakir Mohamed, Danilo Jimenez Rezende, Max Welling

CVPR 2013 A Lazy Man's Approach to Benchmarking: Semisupervised Classifier Evaluation and Recalibration Peter Welinder, Max Welling, Pietro Perona

AISTATS 2013 Distributed and Adaptive Darting Monte Carlo Through Regenerations Sungjin Ahn, Yutian Chen, Max Welling

AISTATS 2013 Evidence Estimation for Bayesian Partially Observed MRFs Yutian Chen, Max Welling

ICLR 2013 Herded Gibbs Sampling Luke Bornn, Yutian Chen, Nando de Freitas, Mareija Eskelin, Jing Fang, Max Welling

UAI 2012 A Cluster-Cumulant Expansion at the Fixed Points of Belief Propagation Max Welling, Andrew Gelfand, Alexander Ihler

ICML 2012 Bayesian Posterior Sampling via Stochastic Gradient Fisher Scoring Sungjin Ahn, Anoop Korattikara Balan, Max Welling

UAI 2012 Bayesian Structure Learning for Markov Random Fields with a Spike and Slab Prior Yutian Chen, Max Welling

UAI 2012 Generalized Belief Propagation on Tree Robust Structured Region Graphs Andrew Gelfand, Max Welling

AISTATS 2012 Scalable Inference on Kingman’s Coalescent Using Pair Similarity Dilan Gorur, Levi Boyles, Max Welling

NeurIPS 2012 The Time-Marginalized Coalescent Prior for Hierarchical Clustering Levi Boyles, Max Welling

ICML 2011 Bayesian Learning via Stochastic Gradient Langevin Dynamics Max Welling, Yee Whye Teh

AISTATS 2011 Hidden-Unit Conditional Random Fields Laurens Maaten, Max Welling, Lawrence Saul

ICCV 2011 Integrating Local Classifiers Through Nonlinear Dynamics on Label Graphs with an Application to Image Segmentation Yutian Chen, Andrew Gelfand, Charless C. Fowlkes, Max Welling

AISTATS 2011 Statistical Optimization of Non-Negative Matrix Factorization Anoop Korattikara Balan, Levi Boyles, Max Welling, Jingu Kim, Haesun Park

NeurIPS 2011 Statistical Tests for Optimization Efficiency Levi Boyles, Anoop Korattikara, Deva Ramanan, Max Welling

AAAI 2010 Bayesian Matrix Factorization with Side Information and Dirichlet Process Mixtures Ian Porteous, Arthur U. Asuncion, Max Welling

ICML 2010 Dynamical Products of Experts for Modeling Financial Time Series Yutian Chen, Max Welling

NeurIPS 2010 On Herding and the Perceptron Cycling Theorem Andrew Gelfand, Yutian Chen, Laurens Maaten, Max Welling

AISTATS 2010 Parametric Herding Yutian Chen, Max Welling

UAI 2010 Super-Samples from Kernel Herding Yutian Chen, Max Welling, Alexander J. Smola

IJCAI 2009 Bayesian Extreme Components Analysis Yutian Chen, Max Welling

JMLR 2009 Distributed Algorithms for Topic Models David Newman, Arthur Asuncion, Padhraic Smyth, Max Welling

UAI 2009 Herding Dynamic Weights for Partially Observed Random Field Models Max Welling

ICML 2009 Herding Dynamical Weights to Learn Max Welling

UAI 2009 On Smoothing and Inference for Topic Models Arthur U. Asuncion, Max Welling, Padhraic Smyth, Yee Whye Teh

NeurIPS 2008 Asynchronous Distributed Learning of Topic Models Padhraic Smyth, Max Welling, Arthur U. Asuncion

UAI 2008 Hybrid Variational/Gibbs Collapsed Inference in Topic Models Max Welling, Yee Whye Teh, Bert Kappen

CVPR 2008 Incremental Learning of Nonparametric Bayesian Mixture Models Ryan Gomes, Max Welling, Pietro Perona

ICML 2008 Memory Bounded Inference in Topic Models Ryan Gomes, Max Welling, Pietro Perona

AAAI 2008 Multi-HDP: A Non Parametric Bayesian Model for Tensor Factorization Ian Porteous, Evgeniy Bart, Max Welling

CVPR 2008 Unsupervised Learning of Visual Taxonomies Evgeniy Bart, Ian Porteous, Pietro Perona, Max Welling

IJCAI 2007 Collapsed Variational Dirichlet Process Mixture Models Kenichi Kurihara, Max Welling, Yee Whye Teh

NeurIPS 2007 Collapsed Variational Inference for HDP Yee W. Teh, Kenichi Kurihara, Max Welling

NeurIPS 2007 Distributed Inference for Latent Dirichlet Allocation David Newman, Padhraic Smyth, Max Welling, Arthur U. Asuncion

AISTATS 2007 Generalized Darting Monte Carlo Cristian Sminchisescu, Max Welling

NeurIPS 2007 Infinite State Bayes-Nets for Structured Domains Max Welling, Ian Porteous, Evgeniy Bart

NeurIPS 2006 A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation Yee W. Teh, David Newman, Max Welling

NeurIPS 2006 Accelerated Variational Dirichlet Process Mixtures Kenichi Kurihara, Max Welling, Nikos Vlassis

NeurIPS 2006 Bayesian Model Scoring in Markov Random Fields Sridevi Parise, Max Welling

UAI 2006 Bayesian Random Fields: The Bethe-Laplace Approximation Max Welling, Sridevi Parise

UAI 2006 Gibbs Sampling for (Coupled) Infinite Mixture Models in the Stick Breaking Representation Ian Porteous, Alexander T. Ihler, Padhraic Smyth, Max Welling

ICML 2006 The Rate Adapting Poisson Model for Information Retrieval and Object Recognition Peter V. Gehler, Alex Holub, Max Welling

AISTATS 2005 An Expectation Maximization Algorithm for Inferring Offset-Normal Shape Distributions Max Welling

ICCV 2005 Combining Generative Models and Fisher Kernels for Object Recognition Alex Holub, Max Welling, Pietro Perona

AISTATS 2005 Learning in Markov Random Fields with Contrastive Free Energies Max Welling, Charles Sutton

NeurIPS 2005 Products of ``Edge-Perts Max Welling, Peter V. Gehler

AISTATS 2005 Robust Higher Order Statistics Max Welling

UAI 2005 Structured Region Graphs: Morphing EP into GBP Max Welling, Thomas P. Minka, Yee Whye Teh

ICML 2004 Approximate Inference by Markov Chains on Union Spaces Max Welling, Michal Rosen-Zvi, Yee Whye Teh

NeurIPS 2004 Exponential Family Harmoniums with an Application to Information Retrieval Max Welling, Michal Rosen-zvi, Geoffrey E. Hinton

NeCo 2004 Linear Response Algorithms for Approximate Inference in Graphical Models Max Welling, Yee Whye Teh

UAI 2004 On the Choice of Regions for Generalized Belief Propagation Max Welling

UAI 2003 Efficient Parametric Projection Pursuit Density Estimation Max Welling, Richard S. Zemel, Geoffrey E. Hinton

JMLR 2003 Energy-Based Models for Sparse Overcomplete Representations Yee Whye Teh, Max Welling, Simon Osindero, Geoffrey E. Hinton

NeurIPS 2003 Extreme Components Analysis Max Welling, Christopher Williams, Felix V. Agakov

NeurIPS 2003 Linear Response for Approximate Inference Max Welling, Yee W. Teh

AISTATS 2003 On Improving the Efficiency of the Iterative Proportional Fitting Procedure Yee Whye Teh, Max Welling

NeurIPS 2003 Wormholes Improve Contrastive Divergence Max Welling, Andriy Mnih, Geoffrey E. Hinton

NeurIPS 2002 Learning Sparse Topographic Representations with Products of Student-T Distributions Max Welling, Simon Osindero, Geoffrey E. Hinton

NeurIPS 2002 Self Supervised Boosting Max Welling, Richard S. Zemel, Geoffrey E. Hinton

NeCo 2001 A Constrained EM Algorithm for Independent Component Analysis Max Welling, Markus Weber

UAI 2001 Belief Optimization for Binary Networks: A Stable Alternative to Loopy Belief Propagation Max Welling, Yee Whye Teh

NeurIPS 2001 The Unified Propagation and Scaling Algorithm Yee W. Teh, Max Welling

CVPR 2000 Towards Automatic Discovery of Object Categories Markus Weber, Max Welling, Pietro Perona

ECCV 2000 Unsupervised Learning of Models for Recognition Markus Weber, Max Welling, Pietro Perona