← Authors

Stephan Mandt

62 papers · 2014–2025 · 14 conferences · across top CS/AI conferences

Achievements

🐝 Cross-Pollinator (14) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🌈 Renaissance Researcher (5) 🌍 Conference Polyglot (14) πŸŒ‰ Interdisciplinary Bridge πŸƒ Academic Marathon (11) πŸ—ΊοΈ Taxonomy Completionist (20) πŸ† Grand Slam πŸ‘₯ Mega-Team (56) πŸ”¬ Deep Specialist (16) πŸ‘‘ Triple Crown πŸ† Keyword Champion (3) πŸ—ƒοΈ Keyword Collector (203) ⚑ Prolific Year (5) πŸš€ Conference Pioneer πŸ“ˆ Trend Setter πŸ’Ž Century Club (62) πŸ”₯ Unstoppable (10) ❓ The Questioner

Conferences

ICML (17) NIPS (15) ICLR (8) AISTATS (5) UAI (5) CVPR (2) ICCV (2) JMLR (2) AAAI (1) CONLL (1) EMNLP (1) IJCAI (1) IJCNLP (1) WACV (1)

Top co-authors

Robert Bamler (8) Yibo Yang (8) Padhraic Smyth (7) Maja Rudolph (7) Alex Boyd (6) Marius Kloft (6) Ruihan Yang (6) Chen Qiu (5) Kushagra Pandey (5) David Blei (4)

Keywords

variational inference (12) variational autoencoder (9) representation learning (5) stochastic gradient descent (5) autoregressive model (4) word embedding (4) gaussian process (4) diffusion model (4) anomaly detection (4) probabilistic modeling (4) importance sampling (3) markov chain monte carlo (3) latent variable model (3) image compression (3) stochastic gradient (3) variance reduction (3) model compression (3) bayesian inference (3) dimensionality reduction (2) time series (2)

Papers

One Diffusion to Generate Them All CVPR 2025
Heavy-Tailed Diffusion Models ICLR 2025
Generative Uncertainty in Diffusion Models UAI 2025
ClimSim-Online: A Large Multi-Scale Dataset and Framework for Hybrid Physics-ML Climate Emulation JMLR 2025
Variational Control for Guidance in Diffusion Models ICML 2025
Progressive Compression with Universally Quantized Diffusion Models ICLR 2025
AstroCompress: A benchmark dataset for multi-purpose compression of astronomical data ICLR 2025
Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI ICML 2024
Unity by Diversity: Improved Representation Learning for Multimodal VAEs NIPS 2024
Neural NeRF Compression ICML 2024
Understanding Pathologies of Deep Heteroskedastic Regression UAI 2024
Efficient Integrators for Diffusion Generative Models ICLR 2024
Early-Exit Neural Networks with Nested Prediction Sets UAI 2024
Fast samplers for Inverse Problems in Iterative Refinement models NIPS 2024
Precipitation Downscaling with Spatiotemporal Video Diffusion NIPS 2024
Computationally-Efficient Neural Image Compression with Shallow Decoders ICCV 2023
Inference for mark-censored temporal point processes UAI 2023
Estimating the Rate-Distortion Function by Wasserstein Gradient Descent NIPS 2023
ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation NIPS 2023
Zero-Shot Anomaly Detection via Batch Normalization NIPS 2023
Lossy Image Compression with Conditional Diffusion Models NIPS 2023
Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes ICML 2023
Deep Anomaly Detection under Labeling Budget Constraints ICML 2023
Probabilistic Querying of Continuous-Time Event Sequences AISTATS 2023
A Complete Recipe for Diffusion Generative Models ICCV 2023
Predictive Querying for Autoregressive Neural Sequence Models NIPS 2022
Towards Empirical Sandwich Bounds on the Rate-Distortion Function ICLR 2022
Lossless Compression with Probabilistic Circuits ICLR 2022
Structured Stochastic Gradient MCMC ICML 2022
Latent Outlier Exposure for Anomaly Detection with Contaminated Data ICML 2022
Raising the Bar in Graph-level Anomaly Detection IJCAI 2022
Supervised Compression for Resource-Constrained Edge Computing Systems WACV 2022
Scalable Gaussian Process Variational Autoencoders AISTATS 2021
Neural Transformation Learning for Deep Anomaly Detection Beyond Images ICML 2021
Hierarchical Autoregressive Modeling for Neural Video Compression ICLR 2021
Detecting and Adapting to Irregular Distribution Shifts in Bayesian Online Learning NIPS 2021
Extreme Classification via Adversarial Softmax Approximation ICLR 2020
User-Dependent Neural Sequence Models for Continuous-Time Event Data NIPS 2020
GP-VAE: Deep Probabilistic Time Series Imputation AISTATS 2020
Improving Inference for Neural Image Compression NIPS 2020
The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks ICML 2020
How Good is the Bayes Posterior in Deep Neural Networks Really? ICML 2020
Variational Bayesian Quantization ICML 2020
Active Mini-Batch Sampling Using Repulsive Point Processes AAAI 2019
Autoregressive Text Generation Beyond Feedback Loops IJCNLP 2019
Augmenting and Tuning Knowledge Graph Embeddings UAI 2019
Deep Generative Video Compression NIPS 2019
Autoregressive Text Generation Beyond Feedback Loops EMNLP 2019
Continuous Word Embedding Fusion via Spectral Decomposition CONLL 2018
Improving Optimization for Models With Continuous Symmetry Breaking ICML 2018
Quasi-Monte Carlo Variational Inference ICML 2018
Iterative Amortized Inference ICML 2018
Disentangled Sequential Autoencoder ICML 2018
Scalable Generalized Dynamic Topic Models AISTATS 2018
Factorized Variational Autoencoders for Modeling Audience Reactions to Movies CVPR 2017
Perturbative Black Box Variational Inference NIPS 2017
Dynamic Word Embeddings ICML 2017
Stochastic Gradient Descent as Approximate Bayesian Inference JMLR 2017
A Variational Analysis of Stochastic Gradient Algorithms ICML 2016
Exponential Family Embeddings NIPS 2016
Variational Tempering AISTATS 2016
Smoothed Gradients for Stochastic Variational Inference NIPS 2014