← Authors

Stefano Ermon

220 papers · 2011–2025 · 12 conferences · across top CS/AI conferences

Achievements

πŸ—ΊοΈ Taxonomy Completionist (51) 🐝 Cross-Pollinator (14) πŸƒ Academic Marathon (14) 🌍 Conference Polyglot (12) 🐣 Hot Topic Early Bird 🌈 Renaissance Researcher (8) πŸŒ‰ Interdisciplinary Bridge 🧭 Keyword Pioneer 🌟 Keyword Trendsetter Combo (10) 🏠 Conference Loyalist (80) 🧬 Topic Evolution 🀝 Dynamic Duo (43) πŸ† Grand Slam πŸ‘‘ Triple Crown πŸ”¬ Deep Specialist (37) πŸ† Keyword Champion (6) πŸ”₯ Unstoppable (15) ⚑ Prolific Year (30) πŸ“ˆ Trend Setter πŸ—ƒοΈ Keyword Collector (199) πŸš€ Conference Pioneer πŸ’Ž Century Club (220) ❓ The Questioner

Conferences

NIPS (80) ICML (51) ICLR (37) AISTATS (16) AAAI (10) CVPR (8) IJCAI (5) UAI (5) ICCV (4) WACV (2) ACL (1) L4DC (1)

Top co-authors

Jiaming Song (43) Chenlin Meng (28) Yang Song (26) Shengjia Zhao (23) Aditya Grover (17) Minkai Xu (16) Marshall Burke (11) Rui Shu (11) Ashish Sabharwal (10) Willie Neiswanger (10)

Research topics

Reinforcement Learning (1) Privacy (1) Probability (1)

Keywords

generative model (27) diffusion model (26) variational inference (20) image generation (16) representation learning (12) score matching (11) imitation learning (10) satellite imagery (9) density estimation (9) neural network (9) variational autoencoder (8) probabilistic inference (8) remote sensing (8) transfer learning (7) generative adversarial network (6) partition function (6) inverse reinforcement learning (6) graphical model (6) uncertainty quantification (6) latent space (6)

Papers

CPSample: Classifier Protected Sampling for Guarding Training Data During Diffusion ICLR 2025
TEOChat: A Large Vision-Language Assistant for Temporal Earth Observation Data ICLR 2025
TabDiff: a Mixed-type Diffusion Model for Tabular Data Generation ICLR 2025
Zero-Shot Cyclic Peptide Design via Composable Geometric Constraints ICML 2025
Energy-Based Diffusion Language Models for Text Generation ICLR 2025
ExPLoRA: Parameter-Efficient Extended Pre-Training to Adapt Vision Transformers under Domain Shifts ICML 2025
Personalized Preference Fine-tuning of Diffusion Models CVPR 2025
Data Unlearning in Diffusion Models ICLR 2025
TFG-Flow: Training-free Guidance in Multimodal Generative Flow ICLR 2025
Smooth Interpolation for Improved Discrete Graph Generative Models ICML 2025
Scaling Probabilistic Circuits via Monarch Matrices ICML 2025
Inductive Moment Matching ICML 2025
$f$-PO: Generalizing Preference Optimization with $f$-divergence Minimization AISTATS 2025
CHORDS: Diffusion Sampling Accelerator with Multi-core Hierarchical ODE Solvers ICCV 2025
TorchSpatial: A Location Encoding Framework and Benchmark for Spatial Representation Learning NIPS 2024
Convolutional Differentiable Logic Gate Networks NIPS 2024
Equivariant Graph Neural Operator for Modeling 3D Dynamics ICML 2024
Preference Fine-Tuning of LLMs Should Leverage Suboptimal, On-Policy Data ICML 2024
Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution ICML 2024
Uncertainty Quantification for Forward and Inverse Problems of PDEs via Latent Global Evolution AAAI 2024
HarvestNet: A Dataset for Detecting Smallholder Farming Activity Using Harvest Piles and Remote Sensing AAAI 2024
Disentangling Length from Quality in Direct Preference Optimization ACL 2024
Privacy-Constrained Policies via Mutual Information Regularized Policy Gradients AISTATS 2024
Mechanistic Design and Scaling of Hybrid Architectures ICML 2024
State-Free Inference of State-Space Models: The *Transfer Function* Approach ICML 2024
Large Language Models are Geographically Biased ICML 2024
Mastering Text-to-Image Diffusion: Recaptioning, Planning, and Generating with Multimodal LLMs ICML 2024
HIVE: Harnessing Human Feedback for Instructional Visual Editing CVPR 2024
On the Scalability of Diffusion-based Text-to-Image Generation CVPR 2024
DreamPropeller: Supercharge Text-to-3D Generation with Parallel Sampling CVPR 2024
Diffusion Model Alignment Using Direct Preference Optimization CVPR 2024
Language Model Detectors Are Easily Optimized Against ICLR 2024
SequenceMatch: Imitation Learning for Autoregressive Sequence Modelling with Backtracking ICLR 2024
Consistency Trajectory Models: Learning Probability Flow ODE Trajectory of Diffusion ICLR 2024
Manifold Preserving Guided Diffusion ICLR 2024
DiffusionSat: A Generative Foundation Model for Satellite Imagery ICLR 2024
Cross-Modal Contextualized Diffusion Models for Text-Guided Visual Generation and Editing ICLR 2024
Denoising Diffusion Bridge Models ICLR 2024
GeoLLM: Extracting Geospatial Knowledge from Large Language Models ICLR 2024
MADiff: Offline Multi-agent Learning with Diffusion Models NIPS 2024
Self-Refining Diffusion Samplers: Enabling Parallelization via Parareal Iterations NIPS 2024
PaGoDA: Progressive Growing of a One-Step Generator from a Low-Resolution Diffusion Teacher NIPS 2024
TFG: Unified Training-Free Guidance for Diffusion Models NIPS 2024
Generative Fractional Diffusion Models NIPS 2024
Geometric Trajectory Diffusion Models NIPS 2024
Newton Losses: Using Curvature Information for Learning with Differentiable Algorithms NIPS 2024
Aligning Target-Aware Molecule Diffusion Models with Exact Energy Optimization NIPS 2024
Active Learning for Derivative-Based Global Sensitivity Analysis with Gaussian Processes NIPS 2024
Segment Any Change NIPS 2024
TrAct: Making First-layer Pre-Activations Trainable NIPS 2024
HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide Resolution NIPS 2023
GEO-Bench: Toward Foundation Models for Earth Monitoring NIPS 2023
Direct Preference Optimization: Your Language Model is Secretly a Reward Model NIPS 2023
Holistic Evaluation of Text-to-Image Models NIPS 2023
Generative Modeling Helps Weak Supervision (and Vice Versa) ICLR 2023
But Are You Sure? An Uncertainty-Aware Perspective on Explainable AI AISTATS 2023
Ideal Abstractions for Decision-Focused Learning AISTATS 2023
Long Horizon Temperature Scaling ICML 2023
Scaling Riemannian Diffusion Models NIPS 2023
Geometric Latent Diffusion Models for 3D Molecule Generation ICML 2023
Deep Latent State Space Models for Time-Series Generation ICML 2023
Dual Diffusion Implicit Bridges for Image-to-Image Translation ICLR 2023
CSP: Self-Supervised Contrastive Spatial Pre-Training for Geospatial-Visual Representations ICML 2023
GibbsDDRM: A Partially Collapsed Gibbs Sampler for Solving Blind Inverse Problems with Denoising Diffusion Restoration ICML 2023
Hyena Hierarchy: Towards Larger Convolutional Language Models ICML 2023
End-to-End Diffusion Latent Optimization Improves Classifier Guidance ICCV 2023
GlueGen: Plug and Play Multi-modal Encoders for X-to-image Generation ICCV 2023
Offline Imitation Learning with Suboptimal Demonstrations via Relaxed Distribution Matching AAAI 2023
FP-Diffusion: Improving Score-based Diffusion Models by Enforcing the Underlying Score Fokker-Planck Equation ICML 2023
Reflected Diffusion Models ICML 2023
Equivariant Flow Matching with Hybrid Probability Transport for 3D Molecule Generation NIPS 2023
Parallel Sampling of Diffusion Models NIPS 2023
On Distillation of Guided Diffusion Models CVPR 2023
Extreme Q-Learning: MaxEnt RL without Entropy ICLR 2023
Understanding and Adopting Rational Behavior by Bellman Score Estimation ICLR 2023
Laughing Hyena Distillery: Extracting Compact Recurrences From Convolutions NIPS 2023
Calibration by Distribution Matching: Trainable Kernel Calibration Metrics NIPS 2023
UniControl: A Unified Diffusion Model for Controllable Visual Generation In the Wild NIPS 2023
SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations ICLR 2022
Solving Inverse Problems in Medical Imaging with Score-Based Generative Models ICLR 2022
Comparing Distributions by Measuring Differences that Affect Decision Making ICLR 2022
GeoDiff: A Geometric Diffusion Model for Molecular Conformation Generation ICLR 2022
Local calibration: metrics and recalibration UAI 2022
Experience Replay with Likelihood-free Importance Weights L4DC 2022
SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagery NIPS 2022
Training and Inference on Any-Order Autoregressive Models the Right Way NIPS 2022
Transform Once: Efficient Operator Learning in Frequency Domain NIPS 2022
Improving Self-Supervised Learning by Characterizing Idealized Representations NIPS 2022
FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness NIPS 2022
Generalizing Bayesian Optimization with Decision-theoretic Entropies NIPS 2022
LISA: Learning Interpretable Skill Abstractions from Language NIPS 2022
Denoising Diffusion Restoration Models NIPS 2022
Exploration via Planning for Information about the Optimal Trajectory NIPS 2022
Efficient Spatially Sparse Inference for Conditional GANs and Diffusion Models NIPS 2022
Self-Similarity Priors: Neural Collages as Differentiable Fractal Representations NIPS 2022
Concrete Score Matching: Generalized Score Matching for Discrete Data NIPS 2022
IS-Count: Large-Scale Object Counting from Satellite Images with Covariate-Based Importance Sampling AAAI 2022
A General Recipe for Likelihood-free Bayesian Optimization ICML 2022
Bit Prioritization in Variational Autoencoders via Progressive Coding ICML 2022
ButterflyFlow: Building Invertible Layers with Butterfly Matrices ICML 2022
Modular Conformal Calibration ICML 2022
Imitation Learning by Estimating Expertise of Demonstrators ICML 2022
Density Ratio Estimation via Infinitesimal Classification AISTATS 2022
An Experimental Design Perspective on Model-Based Reinforcement Learning ICLR 2022
Temporal Predictive Coding For Model-Based Planning In Latent Space ICML 2021
Accelerating Feedforward Computation via Parallel Nonlinear Equation Solving ICML 2021
Predicting Livelihood Indicators from Community-Generated Street-Level Imagery AAAI 2021
Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mutual Information ICML 2021
Efficient Poverty Mapping from High Resolution Remote Sensing Images AAAI 2021
Spatial-Temporal Super-Resolution of Satellite Imagery via Conditional Pixel Synthesis NIPS 2021
Improving Compositionality of Neural Networks by Decoding Representations to Inputs NIPS 2021
Estimating High Order Gradients of the Data Distribution by Denoising NIPS 2021
CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation NIPS 2021
Pseudo-Spherical Contrastive Divergence NIPS 2021
Calibrating Predictions to Decisions: A Novel Approach to Multi-Class Calibration NIPS 2021
Geography-Aware Self-Supervised Learning ICCV 2021
PiRank: Scalable Learning To Rank via Differentiable Sorting NIPS 2021
D2C: Diffusion-Decoding Models for Few-Shot Conditional Generation NIPS 2021
HyperSPNs: Compact and Expressive Probabilistic Circuits NIPS 2021
Reward Identification in Inverse Reinforcement Learning ICML 2021
BCD Nets: Scalable Variational Approaches for Bayesian Causal Discovery NIPS 2021
Imitation with Neural Density Models NIPS 2021
IQ-Learn: Inverse soft-Q Learning for Imitation NIPS 2021
Reliable Decisions with Threshold Calibration NIPS 2021
Maximum Likelihood Training of Score-Based Diffusion Models NIPS 2021
Featurized density ratio estimation UAI 2021
Negative Data Augmentation ICLR 2021
Evaluating the Disentanglement of Deep Generative Models through Manifold Topology ICLR 2021
On the Critical Role of Conventions in Adaptive Human-AI Collaboration ICLR 2021
Denoising Diffusion Implicit Models ICLR 2021
Anytime Sampling for Autoregressive Models via Ordered Autoencoding ICLR 2021
Improved Autoregressive Modeling with Distribution Smoothing ICLR 2021
Score-Based Generative Modeling through Stochastic Differential Equations ICLR 2021
Right Decisions from Wrong Predictions: A Mechanism Design Alternative to Individual Calibration AISTATS 2021
Efficient Object Detection in Large Images Using Deep Reinforcement Learning WACV 2020
Belief Propagation Neural Networks NIPS 2020
HiPPO: Recurrent Memory with Optimal Polynomial Projections NIPS 2020
Diversity can be Transferred: Output Diversification for White- and Black-box Attacks NIPS 2020
Probabilistic Circuits for Variational Inference in Discrete Graphical Models NIPS 2020
Autoregressive Score Matching NIPS 2020
Multi-label Contrastive Predictive Coding NIPS 2020
Improved Techniques for Training Score-Based Generative Models NIPS 2020
MOPO: Model-based Offline Policy Optimization NIPS 2020
Efficient Learning of Generative Models via Finite-Difference Score Matching NIPS 2020
AlignFlow: Cycle Consistent Learning from Multiple Domains via Normalizing Flows AAAI 2020
Meta-Amortized Variational Inference and Learning AAAI 2020
Gaussianization Flows AISTATS 2020
Permutation Invariant Graph Generation via Score-Based Generative Modeling AISTATS 2020
A Framework for Sample Efficient Interval Estimation with Control Variates AISTATS 2020
Learning When and Where to Zoom With Deep Reinforcement Learning CVPR 2020
Weakly Supervised Disentanglement with Guarantees ICLR 2020
A Theory of Usable Information under Computational Constraints ICLR 2020
Understanding the Limitations of Variational Mutual Information Estimators ICLR 2020
Fair Generative Modeling via Weak Supervision ICML 2020
Domain Adaptive Imitation Learning ICML 2020
Predictive Coding for Locally-Linear Control ICML 2020
Bridging the Gap Between f-GANs and Wasserstein GANs ICML 2020
Training Deep Energy-Based Models with f-Divergence Minimization ICML 2020
Individual Calibration with Randomized Forecasting ICML 2020
Generating Interpretable Poverty Maps using Object Detection in Satellite Images IJCAI 2020
Flexible Approximate Inference via Stratified Normalizing Flows UAI 2020
Cloud Removal from Satellite Images using Spatiotemporal Generator Networks WACV 2020
Calibrated Model-Based Deep Reinforcement Learning ICML 2019
Adaptive Antithetic Sampling for Variance Reduction ICML 2019
InfoVAE: Balancing Learning and Inference in Variational Autoencoders AAAI 2019
Tile2Vec: Unsupervised Representation Learning for Spatially Distributed Data AAAI 2019
Approximating the Permanent by Sampling from Adaptive Partitions NIPS 2019
Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting NIPS 2019
MintNet: Building Invertible Neural Networks with Masked Convolutions NIPS 2019
Meta-Inverse Reinforcement Learning with Probabilistic Context Variables NIPS 2019
Generative Modeling by Estimating Gradients of the Data Distribution NIPS 2019
Multi-Agent Adversarial Inverse Reinforcement Learning ICML 2019
Reparameterizable Subset Sampling via Continuous Relaxations IJCAI 2019
Temporal FiLM: Capturing Long-Range Sequence Dependencies with Feature-Wise Modulations. NIPS 2019
Adaptive Hashing for Model Counting UAI 2019
Sliced Score Matching: A Scalable Approach to Density and Score Estimation UAI 2019
Stochastic Optimization of Sorting Networks via Continuous Relaxations ICLR 2019
Learning Neural PDE Solvers with Convergence Guarantees ICLR 2019
Neural Joint Source-Channel Coding ICML 2019
Learning to Interpret Satellite Images using Wikipedia IJCAI 2019
Differentiable Antithetic Sampling for Variance Reduction in Stochastic Variational Inference AISTATS 2019
Uncertainty Autoencoders: Learning Compressed Representations via Variational Information Maximization AISTATS 2019
Learning Controllable Fair Representations AISTATS 2019
Training Variational Autoencoders with Buffered Stochastic Variational Inference AISTATS 2019
Graphite: Iterative Generative Modeling of Graphs ICML 2019
Multi-Agent Generative Adversarial Imitation Learning NIPS 2018
Accelerating Natural Gradient with Higher-Order Invariance ICML 2018
Adversarial Constraint Learning for Structured Prediction IJCAI 2018
PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples ICLR 2018
A DIRT-T Approach to Unsupervised Domain Adaptation ICLR 2018
End-to-End Learning of Motion Representation for Video Understanding CVPR 2018
Best arm identification in multi-armed bandits with delayed feedback AISTATS 2018
Variational Rejection Sampling AISTATS 2018
Modeling Sparse Deviations for Compressed Sensing using Generative Models ICML 2018
Bias and Generalization in Deep Generative Models: An Empirical Study NIPS 2018
Accurate Uncertainties for Deep Learning Using Calibrated Regression ICML 2018
Amortized Inference Regularization NIPS 2018
Streamlining Variational Inference for Constraint Satisfaction Problems NIPS 2018
Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data by Minimizing Predictive Variance NIPS 2018
Constructing Unrestricted Adversarial Examples with Generative Models NIPS 2018
Learning Hierarchical Features from Deep Generative Models ICML 2017
InfoGAIL: Interpretable Imitation Learning from Visual Demonstrations NIPS 2017
A-NICE-MC: Adversarial Training for MCMC NIPS 2017
Neural Variational Inference and Learning in Undirected Graphical Models NIPS 2017
Learning and Inference via Maximum Inner Product Search ICML 2016
Variational Bayes on Monte Carlo Steroids NIPS 2016
Tight Variational Bounds via Random Projections and I-Projections AISTATS 2016
Generative Adversarial Imitation Learning NIPS 2016
Solving Marginal MAP Problems with NP Oracles and Parity Constraints NIPS 2016
Adaptive Concentration Inequalities for Sequential Decision Problems NIPS 2016
Variable Elimination in the Fourier Domain ICML 2016
Beyond Parity Constraints: Fourier Analysis of Hash Functions for Inference ICML 2016
Model-Free Imitation Learning with Policy Optimization ICML 2016
A Hybrid Approach for Probabilistic Inference using Random Projections ICML 2015
Uncovering Hidden Structure through Parallel Problem Decomposition for the Set Basis Problem: Application to Materials Discovery IJCAI 2015
Low-density Parity Constraints for Hashing-Based Discrete Integration ICML 2014
Embed and Project: Discrete Sampling with Universal Hashing NIPS 2013
Taming the Curse of Dimensionality: Discrete Integration by Hashing and Optimization ICML 2013
Density Propagation and Improved Bounds on the Partition Function NIPS 2012
Accelerated Adaptive Markov Chain for Partition Function Computation NIPS 2011