← Authors

Max Welling

127 papers · 2003–2025 · 12 conferences · across top CS/AI conferences

Achievements

🐝 Cross-Pollinator (13) 🌍 Conference Polyglot (12) 🐣 Hot Topic Early Bird πŸ—ΊοΈ Taxonomy Completionist (37) 🧭 Keyword Pioneer 🌈 Renaissance Researcher (7) πŸŒ‰ Interdisciplinary Bridge πŸƒ Academic Marathon (22) 🏠 Conference Loyalist (46) 🌟 Keyword Trendsetter Combo (8) 🀝 Dynamic Duo (13) πŸ‘‘ Triple Crown 🌱 Topic Pioneer πŸ† Keyword Champion (2) πŸ† Grand Slam πŸ‘₯ Mega-Team (25) πŸ”¬ Deep Specialist (30) πŸ“ˆ Trend Setter πŸ”₯ Unstoppable (20) ⚑ Prolific Year (5) πŸ’Ž Century Club (127) πŸš€ Conference Pioneer πŸ—ƒοΈ Keyword Collector (154)

Conferences

NIPS (46) ICML (30) ICLR (22) AISTATS (15) JMLR (4) UAI (3) CVPR (2) AAAI (1) CLEAR (1) ICCV (1) IJCAI (1) MIDL (1)

Top co-authors

Christos Louizos (13) Emiel Hoogeboom (10) Efstratios Gavves (8) Herke van Hoof (7) Yutian Chen (6) Johannes Brandstetter (6) Priyank Jaini (6) Taco Cohen (5) Tijmen Blankevoort (5) Rianne van den Berg (5)

Research topics

Differential Privacy (1) Privacy (1)

Keywords

variational inference (19) bayesian inference (16) generative model (14) neural network (11) variational autoencoder (9) markov chain monte carlo (9) normalizing flow (9) gibbs sampling (7) representation learning (6) graph neural network (6) topic model (6) equivariant neural network (6) stochastic gradient (5) dynamical system (5) latent variable model (5) latent dirichlet allocation (5) bayesian network (5) hierarchical dirichlet process (4) gaussian process (4) graphical model (4)

Papers

Artificial Kuramoto Oscillatory Neurons ICLR 2025
Controlled Generation with Equivariant Variational Flow Matching ICML 2025
Erwin: A Tree-based Hierarchical Transformer for Large-scale Physical Systems ICML 2025
BARNN: A Bayesian Autoregressive and Recurrent Neural Network ICML 2025
Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI ICML 2024
Variational Flow Matching for Graph Generation NIPS 2024
Traveling Waves Encode The Recent Past and Enhance Sequence Learning ICLR 2024
Protect Your Score: Contact-Tracing with Differential Privacy Guarantees AAAI 2024
GTA: A Geometry-Aware Attention Mechanism for Multi-View Transformers ICLR 2024
Flow Factorized Representation Learning NIPS 2023
Rotating Features for Object Discovery NIPS 2023
Lie Point Symmetry and Physics-Informed Networks NIPS 2023
Stochastic Optimal Control for Collective Variable Free Sampling of Molecular Transition Paths NIPS 2023
No time to waste: practical statistical contact tracing with few low-bit messages AISTATS 2023
Latent Traversals in Generative Models as Potential Flows ICML 2023
Geometric Clifford Algebra Networks ICML 2023
Neural Wave Machines: Learning Spatiotemporally Structured Representations with Locally Coupled Oscillatory Recurrent Neural Networks ICML 2023
Wasserstein Quantum Monte Carlo: A Novel Approach for Solving the Quantum Many-Body SchrΓΆdinger Equation NIPS 2023
Clifford Neural Layers for PDE Modeling ICLR 2023
Batch Bayesian Optimization on Permutations using the Acquisition Weighted Kernel NIPS 2022
Orbital MCMC AISTATS 2022
Equivariant Diffusion for Molecule Generation in 3D ICML 2022
Lie Point Symmetry Data Augmentation for Neural PDE Solvers ICML 2022
Message Passing Neural PDE Solvers ICLR 2022
Geometric and Physical Quantities improve E(3) Equivariant Message Passing ICLR 2022
Multi-Agent MDP Homomorphic Networks ICLR 2022
Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data CLEAR 2022
On the symmetries of the synchronization problem in Cryo-EM: Multi-Frequency Vector Diffusion Maps on the Projective Plane NIPS 2022
Alleviating Adversarial Attacks on Variational Autoencoders with MCMC NIPS 2022
Mixed variable Bayesian optimization with frequency modulated kernels UAI 2021
Neural Enhanced Belief Propagation on Factor Graphs AISTATS 2021
Sampling in Combinatorial Spaces with SurVAE Flow Augmented MCMC AISTATS 2021
E(n) Equivariant Normalizing Flows NIPS 2021
Modality-Agnostic Topology Aware Localization NIPS 2021
Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions NIPS 2021
Learning Equivariant Energy Based Models with Equivariant Stein Variational Gradient Descent NIPS 2021
Topographic VAEs learn Equivariant Capsules NIPS 2021
Self Normalizing Flows ICML 2021
The Hintons in your Neural Network: a Quantum Field Theory View of Deep Learning ICML 2021
E(n) Equivariant Graph Neural Networks ICML 2021
A Practical Method for Constructing Equivariant Multilayer Perceptrons for Arbitrary Matrix Groups ICML 2021
Federated Learning of User Verification Models Without Sharing Embeddings ICML 2021
Probabilistic Numeric Convolutional Neural Networks ICLR 2021
Gauge Equivariant Mesh CNNs: Anisotropic convolutions on geometric graphs ICLR 2021
Contrastive Learning of Structured World Models ICLR 2020
DIVA: Domain Invariant Variational Autoencoders MIDL 2020
Ancestral Gumbel-Top-k Sampling for Sampling Without Replacement JMLR 2020
Variational Bayes in Private Settings (VIPS) (Extended Abstract) IJCAI 2020
SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks NIPS 2020
Natural Graph Networks NIPS 2020
MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning NIPS 2020
Bayesian Bits: Unifying Quantization and Pruning NIPS 2020
SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows NIPS 2020
The Convolution Exponential and Generalized Sylvester Flows NIPS 2020
Experimental design for MRI by greedy policy search NIPS 2020
Involutive MCMC: a Unifying Framework ICML 2020
Guided Variational Autoencoder for Disentanglement Learning CVPR 2020
Gradient $\ell_1$ Regularization for Quantization Robustness ICLR 2020
Batch-shaping for learning conditional channel gated networks ICLR 2020
Estimating Gradients for Discrete Random Variables by Sampling without Replacement ICLR 2020
To Relieve Your Headache of Training an MRF, Take AdVIL ICLR 2020
Deep Scale-spaces: Equivariance Over Scale NIPS 2019
The Functional Neural Process NIPS 2019
Gauge Equivariant Convolutional Networks and the Icosahedral CNN ICML 2019
Initialized Equilibrium Propagation for Backprop-Free Training ICLR 2019
Differentiable Probabilistic Models of Scientific Imaging with the Fourier Slice Theorem UAI 2019
Invert to Learn to Invert NIPS 2019
Combining Generative and Discriminative Models for Hybrid Inference NIPS 2019
Integer Discrete Flows and Lossless Compression NIPS 2019
Combinatorial Bayesian Optimization using the Graph Cartesian Product NIPS 2019
Training a Spiking Neural Network with Equilibrium Propagation AISTATS 2019
Sinkhorn AutoEncoders UAI 2019
Emerging Convolutions for Generative Normalizing Flows ICML 2019
Stochastic Beams and Where To Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement ICML 2019
Data-Free Quantization Through Weight Equalization and Bias Correction ICCV 2019
The Deep Weight Prior ICLR 2019
Relaxed Quantization for Discretized Neural Networks ICLR 2019
Attention, Learn to Solve Routing Problems! ICLR 2019
Graphical Generative Adversarial Networks NIPS 2018
3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data NIPS 2018
Spherical CNNs ICLR 2018
HexaConv ICLR 2018
Temporally Efficient Deep Learning with Spikes ICLR 2018
Learning Sparse Neural Networks through L_0 Regularization ICLR 2018
VAE with a VampPrior AISTATS 2018
BOCK : Bayesian Optimization with Cylindrical Kernels ICML 2018
Attention-based Deep Multiple Instance Learning ICML 2018
Neural Relational Inference for Interacting Systems ICML 2018
Bayesian Compression for Deep Learning NIPS 2017
Causal Effect Inference with Deep Latent-Variable Models NIPS 2017
DP-EM: Differentially Private Expectation Maximization AISTATS 2017
Multiplicative Normalizing Flows for Variational Bayesian Neural Networks ICML 2017
Improved Variational Inference with Inverse Autoregressive Flow NIPS 2016
Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors ICML 2016
Group Equivariant Convolutional Networks ICML 2016
Herded Gibbs Sampling JMLR 2016
Scalable MCMC for Mixed Membership Stochastic Blockmodels AISTATS 2016
Harmonic Exponential Families on Manifolds ICML 2015
Optimization Monte Carlo: Efficient and Embarrassingly Parallel Likelihood-Free Inference NIPS 2015
Bayesian dark knowledge NIPS 2015
Variational Dropout and the Local Reparameterization Trick NIPS 2015
Markov Chain Monte Carlo and Variational Inference: Bridging the Gap ICML 2015
Efficient Gradient-Based Inference through Transformations between Bayes Nets and Neural Nets ICML 2014
Learning the Irreducible Representations of Commutative Lie Groups ICML 2014
Distributed Stochastic Gradient MCMC ICML 2014
Austerity in MCMC Land: Cutting the Metropolis-Hastings Budget ICML 2014
Approximate Slice Sampling for Bayesian Posterior Inference AISTATS 2014
Semi-supervised Learning with Deep Generative Models NIPS 2014
A Lazy Man's Approach to Benchmarking: Semisupervised Classifier Evaluation and Recalibration CVPR 2013
Distributed and Adaptive Darting Monte Carlo through Regenerations AISTATS 2013
Evidence Estimation for Bayesian Partially Observed MRFs AISTATS 2013
The Time-Marginalized Coalescent Prior for Hierarchical Clustering NIPS 2012
Scalable Inference on Kingman’s Coalescent using Pair Similarity AISTATS 2012
Hidden-Unit Conditional Random Fields AISTATS 2011
Statistical Optimization of Non-Negative Matrix Factorization AISTATS 2011
Statistical Tests for Optimization Efficiency NIPS 2011
On Herding and the Perceptron Cycling Theorem NIPS 2010
Parametric Herding AISTATS 2010
Distributed Algorithms for Topic Models JMLR 2009
Asynchronous Distributed Learning of Topic Models NIPS 2008
Infinite State Bayes-Nets for Structured Domains NIPS 2007
Collapsed Variational Inference for HDP NIPS 2007
Distributed Inference for Latent Dirichlet Allocation NIPS 2007
A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation NIPS 2006
Accelerated Variational Dirichlet Process Mixtures NIPS 2006
Bayesian Model Scoring in Markov Random Fields NIPS 2006
Energy-Based Models for Sparse Overcomplete Representations JMLR 2003