Stefano Ermon
Associate Professor of Computer Science and Senior Fellow at the Woods Institute for the Environment
Develops probabilistic and generative models such as diffusion models, with applications to sustainability.
Stefano Ermon's lab at Stanford works on generative & foundation models, probabilistic & causal inference and image & video generation. 114 papers since 2023, 97 at top venues, mostly ICLR and NeurIPS. Applications close .
At a glance
- papers, 2023–2026
- 30/ year
- at T1 venues
- 85%
- PhD students
- —
- MS students
- —
- alumni listed
- —
Papers by yearfrom DBLP, 2023–2026
- 15 papers2026 · T1 13 · collecting
- 24 papers2025 · T1 19
- 38 papers2024 · T1 35
- 37 papers2023 · T1 30
T1 venuesOtherVenues: ICLR, NeurIPS, ICML, CVPR, AISTATS
Research focuspaper titles that use each topic's words
- Generative & foundation models41 papers
- diffusion models19 papers
- Image & video generation18 papers
- Probabilistic & causal inference6 papers
- sustainability0 papers
Paper list
From DBLP.
202615 papers
- CVPR (T1)Adaptive Spectral Feature Forecasting for Diffusion Sampling Acceleration (opens in a new tab)
- ICLR (T1)Align Your Structures: Generating Trajectories with Structure Pretraining for Molecular Dynamics (opens in a new tab)
- IEEE Signal Process. Mag.Aligning Large Language Models With Human Feedback: Mathematical foundations and algorithm design [Special Issue on the Mathematics of Deep Learning] (opens in a new tab)
- ICLR (T1)CMT: Mid-Training for Efficient Learning of Consistency, Mean Flow, and Flow-Map Models (opens in a new tab)
- ICLR (T1)DiffusionNFT: Online Diffusion Reinforcement with Forward Process (opens in a new tab)
- ICLR (T1)Discrete Diffusion Trajectory Alignment via Stepwise Decomposition (opens in a new tab)
- ICLR (T1)DistillKac: Few-Step Image Generation via Damped Wave Equations (opens in a new tab)
- FAccTEnergy Scaling Laws for Diffusion Models: Quantifying Compute in Image Generation (opens in a new tab)
- ICLR (T1)FSPO: Few-Shot Optimization of Synthetic Preferences Effectively Personalizes to Real Users (opens in a new tab)
- ICLR (T1)Improved Object-Centric Diffusion Learning with Registers and Contrastive Alignment (opens in a new tab)
- ICLR (T1)InfoTok: Adaptive Discrete Video Tokenizer via Information-Theoretic Compression (opens in a new tab)
- CVPR (T1)MeanFlow Transformers with Representation Autoencoders (opens in a new tab)
202524 papers
- Found. Trends Mach. Learn.Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems (opens in a new tab)
- Trans. Mach. Learn. Res.Calibrated Probabilistic Forecasts for Arbitrary Sequences (opens in a new tab)
- TPAMI (T1)Changen2: Multi-Temporal Remote Sensing Generative Change Foundation Model (opens in a new tab)
- ICCV (T1)CHORDS: Diffusion Sampling Accelerator with Multi-Core Hierarchical ODE Solvers (opens in a new tab)
- ICLR (T1)CPSample: Classifier Protected Sampling for Guarding Training Data During Diffusion (opens in a new tab)
- ICLR (T1)Data Unlearning in Diffusion Models (opens in a new tab)
- ICLR (T1)Energy-Based Diffusion Language Models for Text Generation (opens in a new tab)
- ICML (T1)ExPLoRA: Parameter-Efficient Extended Pre-Training to Adapt Vision Transformers under Domain Shifts (opens in a new tab)
- NeurIPS (T1)Exploring Diffusion Transformer Designs via Grafting (opens in a new tab)
- AISTATS (T1)f-PO: Generalizing Preference Optimization with f-divergence Minimization (opens in a new tab)
- Trans. Mach. Learn. Res.G2D2: Gradient-Guided Discrete Diffusion for Inverse Problem Solving (opens in a new tab)
- NeurIPS (T1)GeoAda: Efficiently Finetune Geometric Diffusion Models with Equivariant Adapters (opens in a new tab)
202438 papers
- NeurIPS (T1)Active Learning for Derivative-Based Global Sensitivity Analysis with Gaussian Processes (opens in a new tab)
- NeurIPS (T1)Aligning Target-Aware Molecule Diffusion Models with Exact Energy Optimization (opens in a new tab)
- ICLR (T1)Consistency Trajectory Models: Learning Probability Flow ODE Trajectory of Diffusion (opens in a new tab)
- NeurIPS (T1)Convolutional Differentiable Logic Gate Networks (opens in a new tab)
- ICLR (T1)Cross-Modal Contextualized Diffusion Models for Text-Guided Visual Generation and Editing (opens in a new tab)
- ICLR (T1)Denoising Diffusion Bridge Models (opens in a new tab)
- CVPR (T1)Diffusion Model Alignment Using Direct Preference Optimization (opens in a new tab)
- ICLR (T1)DiffusionSat: A Generative Foundation Model for Satellite Imagery (opens in a new tab)
- ICML (T1)Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution (opens in a new tab)
- Findings of ACLDisentangling Length from Quality in Direct Preference Optimization (opens in a new tab)
- CVPR (T1)DreamPropeller: Supercharge Text-to-3D Generation with Parallel Sampling (opens in a new tab)
- ICML (T1)Equivariant Graph Neural Operator for Modeling 3D Dynamics (opens in a new tab)
202337 papers
- Trans. Mach. Learn. Res.Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models (opens in a new tab)
- AISTATS (T1)But Are You Sure? An Uncertainty-Aware Perspective on Explainable AI (opens in a new tab)
- NeurIPS (T1)Calibration by Distribution Matching: Trainable Kernel Calibration Metrics (opens in a new tab)
- ICML (T1)CSP: Self-Supervised Contrastive Spatial Pre-Training for Geospatial-Visual Representations (opens in a new tab)
- ICML (T1)Deep Latent State Space Models for Time-Series Generation (opens in a new tab)
- NeurIPS (T1)Direct Preference Optimization: Your Language Model is Secretly a Reward Model (opens in a new tab)
- ICLR (T1)Dual Diffusion Implicit Bridges for Image-to-Image Translation (opens in a new tab)
- TPAMI (T1)Efficient Spatially Sparse Inference for Conditional GANs and Diffusion Models (opens in a new tab)
- ICCV (T1)End-to-End Diffusion Latent Optimization Improves Classifier Guidance (opens in a new tab)
- NeurIPS (T1)Equivariant Flow Matching with Hybrid Probability Transport for 3D Molecule Generation (opens in a new tab)
- ICLR (T1)Extreme Q-Learning: MaxEnt RL without Entropy (opens in a new tab)
- ICML (T1)FP-Diffusion: Improving Score-based Diffusion Models by Enforcing the Underlying Score Fokker-Planck Equation (opens in a new tab)
Where alumni went
Alumni are not listed on the lab homepage.
Apply to StanfordFall 2027
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