Stephan Mandt

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