Trust Estimation in autonomic networks: a statistical mechanics approach. - Stefano Ermon; Luca Schenato 001; Sandro Zampieri (CDC, 2009)

Collaborative multiagent Gaussian inference in a dynamic environment using belief propagation. - Stefano Ermon; Carla P. Gomes; Bart Selman (AAMAS, 2010)

Computing the Density of States of Boolean Formulas. - Stefano Ermon; Carla P. Gomes; Bart Selman (CP, 2010)

Playing games against nature: optimal policies for renewable resource allocation. - Stefano Ermon; Jon Conrad; Carla P. Gomes; Bart Selman (UAI, 2010)

A message passing approach to multiagent gaussian inference for dynamic processes. - Stefano Ermon; Carla P. Gomes; Bart Selman (AAMAS, 2011)

Risk-Sensitive Policies for Sustainable Renewable Resource Allocation. - Stefano Ermon; Jon Conrad; Carla P. Gomes; Bart Selman (IJCAI, 2011)

A Flat Histogram Method for Computing the Density of States of Combinatorial Problems. - Stefano Ermon; Carla P. Gomes; Bart Selman (IJCAI, 2011)

Accelerated Adaptive Markov Chain for Partition Function Computation. - Stefano Ermon; Carla P. Gomes; Ashish Sabharwal; Bart Selman (NIPS, 2011)

Probabilistic planning with non-linear utility functions and worst-case guarantees. - Stefano Ermon; Carla P. Gomes; Bart Selman; Alexander Vladimirsky (AAMAS, 2012)

Density Propagation and Improved Bounds on the Partition Function. - Stefano Ermon; Carla P. Gomes; Ashish Sabharwal; Bart Selman (NIPS, 2012)

Feature-Enhanced Probabilistic Models for Diffusion Network Inference. - Liaoruo Wang; Stefano Ermon; John E. Hopcroft (ECML/PKDD (2), 2012)

SMT-Aided Combinatorial Materials Discovery. - Stefano Ermon; Ronan LeBras; Carla P. Gomes; Bart Selman; R. Bruce van Dover (SAT, 2012)

Uniform Solution Sampling Using a Constraint Solver As an Oracle. - Stefano Ermon; Carla P. Gomes; Bart Selman (UAI, 2012)

Taming the Curse of Dimensionality: Discrete Integration by Hashing and Optimization. - Stefano Ermon; Carla P. Gomes; Ashish Sabharwal; Bart Selman (ICML (2), 2013)

Embed and Project: Discrete Sampling with Universal Hashing. - Stefano Ermon; Carla P. Gomes; Ashish Sabharwal; Bart Selman (NIPS, 2013)

Optimization With Parity Constraints: From Binary Codes to Discrete Integration. - Stefano Ermon; Carla P. Gomes; Ashish Sabharwal; Bart Selman (UAI, 2013)

Learning policies for battery usage optimization in electric vehicles. - Stefano Ermon; Yexiang Xue; Carla P. Gomes; Bart Selman (Mach. Learn., 2013)

Designing Fast Absorbing Markov Chains. - Stefano Ermon; Carla P. Gomes; Ashish Sabharwal; Bart Selman (AAAI, 2014)

Uncovering Hidden Structure through Parallel Problem Decomposition. - Yexiang Xue; Stefano Ermon; Carla P. Gomes; Bart Selman (AAAI, 2014)

Low-density Parity Constraints for Hashing-Based Discrete Integration. - Stefano Ermon; Carla P. Gomes; Ashish Sabharwal; Bart Selman (ICML, 2014)

Pattern Decomposition with Complex Combinatorial Constraints: Application to Materials Discovery. - Stefano Ermon; Ronan Le Bras 001; Santosh K. Suram; John M. Gregoire; Carla P. Gomes; Bart Selman; Robert Bruce van Dover (AAAI, 2015)

Learning Large-Scale Dynamic Discrete Choice Models of Spatio-Temporal Preferences with Application to Migratory Pastoralism in East Africa. - Stefano Ermon; Yexiang Xue; Russell Toth; Bistra Dilkina; Richard Bernstein; Theodoros Damoulas; Patrick E. Clark; Steve DeGloria; Andrew Mude; Christopher Barrett; Carla P. Gomes (AAAI, 2015)

Uncovering Hidden Structure through Parallel Problem Decomposition for the Set Basis Problem. - Yexiang Xue; Stefano Ermon; Carla P. Gomes; Bart Selman (AAAI Workshop: Computational Sustainability, 2015)

A Hybrid Approach for Probabilistic Inference using Random Projections. - Michael Zhu; Stefano Ermon (ICML, 2015)

Uncovering Hidden Structure through Parallel Problem Decomposition for the Set Basis Problem: Application to Materials Discovery. - Yexiang Xue; Stefano Ermon; Carla P. Gomes; Bart Selman (IJCAI, 2015)

Importance Sampling over Sets: A New Probabilistic Inference Scheme. - Stefan Hadjis; Stefano Ermon (UAI, 2015)

Decision Making And Inference Under Limited Information And High Dimensionality. - Stefano Ermon (2015)

Exact Sampling with Integer Linear Programs and Random Perturbations. - Carolyn Kim; Ashish Sabharwal; Stefano Ermon (AAAI, 2016)

Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping. - Sang Michael Xie; Neal Jean; Marshall Burke; David B. Lobell; Stefano Ermon (AAAI, 2016)

Closing the Gap Between Short and Long XORs for Model Counting. - Shengjia Zhao; Sorathan Chaturapruek; Ashish Sabharwal; Stefano Ermon (AAAI, 2016)

Tight Variational Bounds via Random Projections and I-Projections. - Lun-Kai Hsu; Tudor Achim; Stefano Ermon (AISTATS, 2016)

Beyond Parity Constraints: Fourier Analysis of Hash Functions for Inference. - Tudor Achim; Ashish Sabharwal; Stefano Ermon (ICML, 2016)

Model-Free Imitation Learning with Policy Optimization. - Jonathan Ho; Jayesh K. Gupta; Stefano Ermon (ICML, 2016)

Learning and Inference via Maximum Inner Product Search. - Stephen Mussmann; Stefano Ermon (ICML, 2016)

Variable Elimination in the Fourier Domain. - Yexiang Xue; Stefano Ermon; Ronan Le Bras 001; Carla P. Gomes; Bart Selman (ICML, 2016)

Variational Bayes on Monte Carlo Steroids. - Aditya Grover; Stefano Ermon (NIPS, 2016)

Generative Adversarial Imitation Learning. - Jonathan Ho; Stefano Ermon (NIPS, 2016)

Solving Marginal MAP Problems with NP Oracles and Parity Constraints. - Yexiang Xue; Zhiyuan Li 005; Stefano Ermon; Carla P. Gomes; Bart Selman (NIPS, 2016)

Adaptive Concentration Inequalities for Sequential Decision Problems. - Shengjia Zhao; Enze Zhou; Ashish Sabharwal; Stefano Ermon (NIPS, 2016)

Sparse Gaussian Processes for Bayesian Optimization. - Mitchell McIntire; Daniel Ratner; Stefano Ermon (UAI, 2016)

Reliable Confidence Estimation via Online Learning. - Volodymyr Kuleshov; Stefano Ermon (CoRR, 2016)

Estimating Uncertainty Online Against an Adversary. - Volodymyr Kuleshov; Stefano Ermon (AAAI, 2017)

Label-Free Supervision of Neural Networks with Physics and Domain Knowledge. - Russell Stewart; Stefano Ermon (AAAI, 2017)

General Bounds on Satisfiability Thresholds for Random CSPs via Fourier Analysis. - Colin Wei; Stefano Ermon (AAAI, 2017)

Deep Gaussian Process for Crop Yield Prediction Based on Remote Sensing Data. - Jiaxuan You; Xiaocheng Li; Melvin Low; David B. Lobell; Stefano Ermon (AAAI, 2017)

Monitoring Ethiopian Wheat Fungus with Satellite Imagery and Deep Feature Learning. - Reid Pryzant; Stefano Ermon; David B. Lobell (CVPR Workshops, 2017)

Audio Super-Resolution using Neural Networks. - Volodymyr Kuleshov; S. Zayd Enam; Stefano Ermon (ICLR (Workshop), 2017)

Generative Adversarial Learning of Markov Chains. - Jiaming Song; Shengjia Zhao; Stefano Ermon (ICLR (Workshop), 2017)

Learning Hierarchical Features from Deep Generative Models. - Shengjia Zhao; Jiaming Song; Stefano Ermon (ICML, 2017)

Autotuning Stencil Computations with Structural Ordinal Regression Learning. - Biagio Cosenza; Juan José Durillo; Stefano Ermon; Ben H. H. Juurlink (IPDPS, 2017)

Neural Variational Inference and Learning in Undirected Graphical Models. - Volodymyr Kuleshov; Stefano Ermon (NIPS, 2017)

InfoGAIL: Interpretable Imitation Learning from Visual Demonstrations. - Yunzhu Li; Jiaming Song; Stefano Ermon (NIPS, 2017)

A-NICE-MC: Adversarial Training for MCMC. - Jiaming Song; Shengjia Zhao; Stefano Ermon (NIPS, 2017)

Stencil Autotuning with Ordinal Regression: Extended Abstract. - Biagio Cosenza; Juan José Durillo; Stefano Ermon; Ben H. H. Juurlink (SCOPES, 2017)

Hybrid Deep Discriminative/Generative Models for Semi-Supervised Learning. - Volodymyr Kuleshov; Stefano Ermon (UAI, 2017)

Fast Amortized Inference and Learning in Log-linear Models with Randomly Perturbed Nearest Neighbor Search. - Stephen Mussmann; Daniel Levy; Stefano Ermon (UAI, 2017)

A Survey on Behavior Recognition Using WiFi Channel State Information. - Siamak Yousefi; Hirokazu Narui; Sankalp Dayal; Stefano Ermon; Shahrokh Valaee (IEEE Commun. Mag., 2017)

Flow-GAN: Bridging implicit and prescribed learning in generative models. - Aditya Grover; Manik Dhar; Stefano Ermon (CoRR, 2017)

Inferring The Latent Structure of Human Decision-Making from Raw Visual Inputs. - Yunzhu Li; Jiaming Song; Stefano Ermon (CoRR, 2017)

On the Limits of Learning Representations with Label-Based Supervision. - Jiaming Song; Russell Stewart; Shengjia Zhao; Stefano Ermon (CoRR, 2017)

Learning Hierarchical Features from Generative Models. - Shengjia Zhao; Jiaming Song; Stefano Ermon (CoRR, 2017)

Towards Deeper Understanding of Variational Autoencoding Models. - Shengjia Zhao; Jiaming Song; Stefano Ermon (CoRR, 2017)

InfoVAE: Information Maximizing Variational Autoencoders. - Shengjia Zhao; Jiaming Song; Stefano Ermon (CoRR, 2017)

A Survey of Human Activity Recognition Using WiFi CSI. - Siamak Yousefi; Hirokazu Narui; Sankalp Dayal; Stefano Ermon; Shahrokh Valaee (CoRR, 2017)

Poverty Prediction with Public Landsat 7 Satellite Imagery and Machine Learning. - Anthony Perez 002; Christopher Yeh; George Azzari; Marshall Burke; David B. Lobell; Stefano Ermon (CoRR, 2017)

Hierarchical Modeling of Seed Variety Yields and Decision Making for Future Planting Plans. - Huaiyang Zhong; Xiaocheng Li; David B. Lobell; Stefano Ermon; Margaret L. Brandeau (CoRR, 2017)

Shape optimization in laminar flow with a label-guided variational autoencoder. - Stephan Eismann; Stefan Bartzsch; Stefano Ermon (CoRR, 2017)

Flow-GAN: Combining Maximum Likelihood and Adversarial Learning in Generative Models. - Aditya Grover; Manik Dhar; Stefano Ermon (AAAI, 2018)

Boosted Generative Models. - Aditya Grover; Stefano Ermon (AAAI, 2018)

Approximate Inference via Weighted Rademacher Complexity. - Jonathan Kuck; Ashish Sabharwal; Stefano Ermon (AAAI, 2018)

Deterministic Policy Optimization by Combining Pathwise and Score Function Estimators for Discrete Action Spaces. - Daniel Levy; Stefano Ermon (AAAI, 2018)

Variational Rejection Sampling. - Aditya Grover; Ramki Gummadi; Miguel Lázaro-Gredilla; Dale Schuurmans; Stefano Ermon (AISTATS, 2018)

Best arm identification in multi-armed bandits with delayed feedback. - Aditya Grover; Todor M. Markov; Peter M. Attia; Norman Jin; Nicolas Perkins; Bryan Cheong; Michael H. Chen; Zi Yang; Stephen J. Harris; William C. Chueh; Stefano Ermon (AISTATS, 2018)

End-to-End Learning of Motion Representation for Video Understanding. - Lijie Fan; Wen-bing Huang 001; Chuang Gan 001; Stefano Ermon; Boqing Gong; Junzhou Huang (CVPR, 2018)

Deep Transfer Learning for Crop Yield Prediction with Remote Sensing Data. - Anna X. Wang; Caelin Tran; Nikhil Desai; David B. Lobell; Stefano Ermon (COMPASS, 2018)

A DIRT-T Approach to Unsupervised Domain Adaptation. - Rui Shu; Hung H. Bui; Hirokazu Narui; Stefano Ermon (ICLR (Poster), 2018)

PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples. - Yang Song 011; Taesup Kim; Sebastian Nowozin; Stefano Ermon; Nate Kushman (ICLR (Poster), 2018)

Modeling Sparse Deviations for Compressed Sensing using Generative Models. - Manik Dhar; Aditya Grover; Stefano Ermon (ICML, 2018)

Accurate Uncertainties for Deep Learning Using Calibrated Regression. - Volodymyr Kuleshov; Nathan Fenner; Stefano Ermon (ICML, 2018)

Accelerating Natural Gradient with Higher-Order Invariance. - Yang Song 011; Jiaming Song; Stefano Ermon (ICML, 2018)

Adversarial Constraint Learning for Structured Prediction. - Hongyu Ren; Russell Stewart; Jiaming Song; Volodymyr Kuleshov; Stefano Ermon (IJCAI, 2018)

Infrastructure Quality Assessment in Africa using Satellite Imagery and Deep Learning. - Barak Oshri; Annie Hu; Peter Adelson; Xiao Chen 014; Pascaline Dupas; Jeremy Weinstein; Marshall Burke; David B. Lobell; Stefano Ermon (KDD, 2018)

Streamlining Variational Inference for Constraint Satisfaction Problems. - Aditya Grover; Tudor Achim; Stefano Ermon (NeurIPS, 2018)

Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data by Minimizing Predictive Variance. - Neal Jean; Sang Michael Xie; Stefano Ermon (NeurIPS, 2018)

Amortized Inference Regularization. - Rui Shu; Hung H. Bui; Shengjia Zhao; Mykel J. Kochenderfer; Stefano Ermon (NeurIPS, 2018)

Multi-Agent Generative Adversarial Imitation Learning. - Jiaming Song; Hongyu Ren; Dorsa Sadigh; Stefano Ermon (NeurIPS, 2018)

Constructing Unrestricted Adversarial Examples with Generative Models. - Yang Song 011; Rui Shu; Nate Kushman; Stefano Ermon (NeurIPS, 2018)

Bias and Generalization in Deep Generative Models: An Empirical Study. - Shengjia Zhao; Hongyu Ren; Arianna Yuan; Jiaming Song; Noah D. Goodman; Stefano Ermon (NeurIPS, 2018)

Bayesian optimization and attribute adjustment. - Stephan Eismann; Daniel Levy; Rui Shu; Stefan Bartzsch; Stefano Ermon (UAI, 2018)

A Lagrangian Perspective on Latent Variable Generative Models. - Shengjia Zhao; Jiaming Song; Stefano Ermon (UAI, 2018)

Learning with Weak Supervision from Physics and Data-Driven Constraints. - Hongyu Ren; Russell Stewart; Jiaming Song; Volodymyr Kuleshov; Stefano Ermon (AI Mag., 2018)

Accelerating Natural Gradient with Higher-Order Invariance. - Yang Song 011; Stefano Ermon (CoRR, 2018)

Generative Adversarial Examples. - Yang Song 011; Rui Shu; Nate Kushman; Stefano Ermon (CoRR, 2018)

The Information Autoencoding Family: A Lagrangian Perspective on Latent Variable Generative Models. - Shengjia Zhao; Jiaming Song; Stefano Ermon (CoRR, 2018)

Improved Training with Curriculum GANs. - Rishi Sharma 005; Shane T. Barratt; Stefano Ermon; Vijay S. Pande (CoRR, 2018)

NECST: Neural Joint Source-Channel Coding. - Kristy Choi; Kedar Tatwawadi; Tsachy Weissman; Stefano Ermon (CoRR, 2018)

Tile2Vec: Unsupervised Representation Learning for Spatially Distributed Data. - Neal Jean; Sherrie Wang; Anshul Samar; George Azzari; David B. Lobell; Stefano Ermon (AAAI, 2019)

InfoVAE: Balancing Learning and Inference in Variational Autoencoders. - Shengjia Zhao; Jiaming Song; Stefano Ermon (AAAI, 2019)

Mapping Missing Population in Rural India: A Deep Learning Approach with Satellite Imagery. - Wenjie Hu; Jay Harshadbhai Patel; Zoe-Alanah Robert; Paul Novosad; Samuel Asher; Zhongyi Tang; Marshall Burke; David B. Lobell; Stefano Ermon (AIES, 2019)

Uncertainty Autoencoders: Learning Compressed Representations via Variational Information Maximization. - Aditya Grover; Stefano Ermon (AISTATS, 2019)

Training Variational Autoencoders with Buffered Stochastic Variational Inference. - Rui Shu; Hung H. Bui; Jay Whang; Stefano Ermon (AISTATS, 2019)

Learning Controllable Fair Representations. - Jiaming Song; Pratyusha Kalluri; Aditya Grover; Shengjia Zhao; Stefano Ermon (AISTATS, 2019)

Differentiable Antithetic Sampling for Variance Reduction in Stochastic Variational Inference. - Mike Wu; Noah D. Goodman; Stefano Ermon (AISTATS, 2019)

Semantic Segmentation of Crop Type in Africa: A Novel Dataset and Analysis of Deep Learning Methods. - Rose M. Rustowicz; Robin Cheong; Lijing Wang; Stefano Ermon; Marshall Burke; David B. Lobell (CVPR Workshops, 2019)

AlignFlow: Learning from multiple domains via normalizing flows. - Aditya Grover; Christopher Chute; Rui Shu; Zhangjie Cao; Stefano Ermon (DGS@ICLR, 2019)

Bias Correction of Learned Generative Models via Likelihood-free Importance Weighting. - Aditya Grover; Jiaming Song; Ashish Kapoor; Kenneth Tran; Alekh Agarwal; Eric Horvitz; Stefano Ermon (DGS@ICLR, 2019)

Stochastic Optimization of Sorting Networks via Continuous Relaxations. - Aditya Grover; Eric Wang; Aaron Zweig; Stefano Ermon (ICLR (Poster), 2019)

Learning Neural PDE Solvers with Convergence Guarantees. - Jun-Ting Hsieh; Shengjia Zhao; Stephan Eismann; Lucia Mirabella; Stefano Ermon (ICLR (Poster), 2019)

Neural Joint Source-Channel Coding. - Kristy Choi; Kedar Tatwawadi; Aditya Grover; Tsachy Weissman; Stefano Ermon (ICML, 2019)

Graphite: Iterative Generative Modeling of Graphs. - Aditya Grover; Aaron Zweig; Stefano Ermon (ICML, 2019)

Calibrated Model-Based Deep Reinforcement Learning. - Ali Malik; Volodymyr Kuleshov; Jiaming Song; Danny Nemer; Harlan Seymour; Stefano Ermon (ICML, 2019)

Adaptive Antithetic Sampling for Variance Reduction. - Hongyu Ren; Shengjia Zhao; Stefano Ermon (ICML, 2019)

Multi-Agent Adversarial Inverse Reinforcement Learning. - Lantao Yu; Jiaming Song; Stefano Ermon (ICML, 2019)

Learning to Interpret Satellite Images using Wikipedia. - Burak Uzkent; Evan Sheehan; Chenlin Meng; Zhongyi Tang; Marshall Burke; David B. Lobell; Stefano Ermon (IJCAI, 2019)

Reparameterizable Subset Sampling via Continuous Relaxations. - Sang Michael Xie; Stefano Ermon (IJCAI, 2019)

Predicting Economic Development using Geolocated Wikipedia Articles. - Evan Sheehan; Chenlin Meng; Matthew Tan; Burak Uzkent; Neal Jean; Marshall Burke; David B. Lobell; Stefano Ermon (KDD, 2019)

Temporal FiLM: Capturing Long-Range Sequence Dependencies with Feature-Wise Modulations. - Sawyer Birnbaum; Volodymyr Kuleshov; S. Zayd Enam; Pang Wei Koh; Stefano Ermon (NeurIPS, 2019)

Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting. - Aditya Grover; Jiaming Song; Ashish Kapoor; Kenneth Tran; Alekh Agarwal; Eric Horvitz; Stefano Ermon (NeurIPS, 2019)

Approximating the Permanent by Sampling from Adaptive Partitions. - Jonathan Kuck; Tri Dao; Hamid Rezatofighi; Ashish Sabharwal; Stefano Ermon (NeurIPS, 2019)

Generative Modeling by Estimating Gradients of the Data Distribution. - Yang Song 011; Stefano Ermon (NeurIPS, 2019)

MintNet: Building Invertible Neural Networks with Masked Convolutions. - Yang Song 011; Chenlin Meng; Stefano Ermon (NeurIPS, 2019)

Meta-Inverse Reinforcement Learning with Probabilistic Context Variables. - Lantao Yu; Tianhe Yu; Chelsea Finn; Stefano Ermon (NeurIPS, 2019)

Adaptive Hashing for Model Counting. - Jonathan Kuck; Tri Dao; Shenjia Zhao; Burak Bartan; Ashish Sabharwal; Stefano Ermon (UAI, 2019)

Sliced Score Matching: A Scalable Approach to Density and Score Estimation. - Yang Song 011; Sahaj Garg; Jiaxin Shi; Stefano Ermon (UAI, 2019)

Computational sustainability: computing for a better world and a sustainable future. - Carla P. Gomes; Thomas G. Dietterich; Christopher Barrett; Jon Conrad; Bistra Dilkina; Stefano Ermon; Fei Fang 001; Andrew Farnsworth; Alan Fern; Xiaoli Z. Fern; Daniel Fink 002; Douglas H. Fisher; et al. (Commun. ACM, 2019)

Rapid identification of pathogenic bacteria using Raman spectroscopy and deep learning. - Chi-Sing Ho; Neal Jean; Catherine A. Hogan; Lena Blackmon; Stefanie S. Jeffrey; Mark Holodniy; Niaz Banaei; Amr A. E. Saleh; Stefano Ermon; Jennifer A. Dionne (CoRR, 2019)

Differentiable Subset Sampling. - Sang Michael Xie; Stefano Ermon (CoRR, 2019)

Semi-Supervised Multitask Learning on Multispectral Satellite Images Using Wasserstein Generative Adversarial Networks (GANs) for Predicting Poverty. - Anthony Perez 002; Swetava Ganguli; Stefano Ermon; George Azzari; Marshall Burke; David B. Lobell (CoRR, 2019)

Distributed generation of privacy preserving data with user customization. - Xiao Chen 014; Thomas Navidi; Stefano Ermon; Ram Rajagopal (CoRR, 2019)

Learning to Interpret Satellite Images in Global Scale Using Wikipedia. - Burak Uzkent; Evan Sheehan; Chenlin Meng; Zhongyi Tang; Marshall Burke; David B. Lobell; Stefano Ermon (CoRR, 2019)

Cross Domain Imitation Learning. - Kuno Kim; Yihong Gu; Jiaming Song; Shengjia Zhao; Stefano Ermon (CoRR, 2019)

Unsupervised Out-of-Distribution Detection with Batch Normalization. - Jiaming Song; Yang Song 011; Stefano Ermon (CoRR, 2019)

Fair Generative Modeling via Weak Supervision. - Aditya Grover; Kristy Choi; Rui Shu; Stefano Ermon (CoRR, 2019)

Approximating Human Judgment of Generated Image Quality. - Y. Alex Kolchinski; Sharon Zhou; Shengjia Zhao; Mitchell L. Gordon; Stefano Ermon (CoRR, 2019)

AlignFlow: Cycle Consistent Learning from Multiple Domains via Normalizing Flows. - Aditya Grover; Christopher Chute; Rui Shu; Zhangjie Cao; Stefano Ermon (AAAI, 2020)

Meta-Amortized Variational Inference and Learning. - Mike Wu; Kristy Choi; Noah D. Goodman; Stefano Ermon (AAAI, 2020)

Gaussianization Flows. - Chenlin Meng; Yang Song 011; Jiaming Song; Stefano Ermon (AISTATS, 2020)

Permutation Invariant Graph Generation via Score-Based Generative Modeling. - Chenhao Niu; Yang Song 011; Jiaming Song; Shengjia Zhao; Aditya Grover; Stefano Ermon (AISTATS, 2020)

A Framework for Sample Efficient Interval Estimation with Control Variates. - Shengjia Zhao; Christopher Yeh; Stefano Ermon (AISTATS, 2020)

Multi-agent Adversarial Inverse Reinforcement Learning with Latent Variables. - Nate Gruver; Jiaming Song; Mykel J. Kochenderfer; Stefano Ermon (AAMAS, 2020)

Farm Parcel Delineation Using Spatio-temporal Convolutional Networks. - Han Lin Aung; Burak Uzkent; Marshall Burke; David B. Lobell; Stefano Ermon (CVPR Workshops, 2020)

Learning When and Where to Zoom With Deep Reinforcement Learning. - Burak Uzkent; Stefano Ermon (CVPR, 2020)

Weakly Supervised Disentanglement with Guarantees. - Rui Shu; Yining Chen; Abhishek Kumar; Stefano Ermon; Ben Poole (ICLR, 2020)

Understanding the Limitations of Variational Mutual Information Estimators. - Jiaming Song; Stefano Ermon (ICLR, 2020)

A Theory of Usable Information under Computational Constraints. - Yilun Xu; Shengjia Zhao; Jiaming Song; Russell Stewart; Stefano Ermon (ICLR, 2020)

Fair Generative Modeling via Weak Supervision. - Kristy Choi; Aditya Grover; Trisha Singh; Rui Shu; Stefano Ermon (ICML, 2020)

Domain Adaptive Imitation Learning. - Kuno Kim; Yihong Gu; Jiaming Song; Shengjia Zhao; Stefano Ermon (ICML, 2020)

Predictive Coding for Locally-Linear Control. - Rui Shu; Tung Nguyen; Yinlam Chow; Tuan Pham; Khoat Than; Mohammad Ghavamzadeh; Stefano Ermon; Hung H. Bui (ICML, 2020)

Bridging the Gap Between f-GANs and Wasserstein GANs. - Jiaming Song; Stefano Ermon (ICML, 2020)

Training Deep Energy-Based Models with f-Divergence Minimization. - Lantao Yu; Yang Song 011; Jiaming Song; Stefano Ermon (ICML, 2020)

Individual Calibration with Randomized Forecasting. - Shengjia Zhao; Tengyu Ma 001; Stefano Ermon (ICML, 2020)

Generating Interpretable Poverty Maps using Object Detection in Satellite Images. - Kumar Ayush; Burak Uzkent; Marshall Burke; David B. Lobell; Stefano Ermon (IJCAI, 2020)

Improved Techniques for Training Score-Based Generative Models. - Yang Song 011; Stefano Ermon (NeurIPS, 2020)

HiPPO: Recurrent Memory with Optimal Polynomial Projections. - Albert Gu; Tri Dao; Stefano Ermon; Atri Rudra; Christopher Ré (NeurIPS, 2020)

Belief Propagation Neural Networks. - Jonathan Kuck; Shuvam Chakraborty; Hao Tang 008; Rachel Luo; Jiaming Song; Ashish Sabharwal; Stefano Ermon (NeurIPS, 2020)

Autoregressive Score Matching. - Chenlin Meng; Lantao Yu; Yang Song 011; Jiaming Song; Stefano Ermon (NeurIPS, 2020)

Efficient Learning of Generative Models via Finite-Difference Score Matching. - Tianyu Pang; Taufik Xu; Chongxuan Li; Yang Song 011; Stefano Ermon; Jun Zhu 001 (NeurIPS, 2020)

Probabilistic Circuits for Variational Inference in Discrete Graphical Models. - Andy Shih; Stefano Ermon (NeurIPS, 2020)

Multi-label Contrastive Predictive Coding. - Jiaming Song; Stefano Ermon (NeurIPS, 2020)

Diversity can be Transferred: Output Diversification for White- and Black-box Attacks. - Yusuke Tashiro; Yang Song 011; Stefano Ermon (NeurIPS, 2020)

MOPO: Model-based Offline Policy Optimization. - Tianhe Yu; Garrett Thomas; Lantao Yu; Stefano Ermon; James Y. Zou; Sergey Levine; Chelsea Finn; Tengyu Ma 001 (NeurIPS, 2020)

Flexible Approximate Inference via Stratified Normalizing Flows. - Chris Cundy; Stefano Ermon (UAI, 2020)

Cloud Removal in Satellite Images Using Spatiotemporal Generative Networks. - Vishnu Sarukkai; Anirudh Jain; Burak Uzkent; Stefano Ermon (WACV, 2020)

Efficient Object Detection in Large Images Using Deep Reinforcement Learning. - Burak Uzkent; Christopher Yeh; Stefano Ermon (WACV, 2020)

Nonlinear Equation Solving: A Faster Alternative to Feedforward Computation. - Yang Song 011; Chenlin Meng; Renjie Liao; Stefano Ermon (CoRR, 2020)

Output Diversified Initialization for Adversarial Attacks. - Yusuke Tashiro; Yang Song 011; Stefano Ermon (CoRR, 2020)

Farmland Parcel Delineation Using Spatio-temporal Convolutional Networks. - Han Lin Aung; Burak Uzkent; Marshall Burke; David B. Lobell; Stefano Ermon (CoRR, 2020)

Evaluating the Disentanglement of Deep Generative Models through Manifold Topology. - Sharon Zhou; Eric Zelikman; Fred Lu; Andrew Y. Ng; Stefano Ermon (CoRR, 2020)

Efficient Poverty Mapping using Deep Reinforcement Learning. - Kumar Ayush; Burak Uzkent; Marshall Burke; David B. Lobell; Stefano Ermon (CoRR, 2020)

Predicting Livelihood Indicators from Crowdsourced Street Level Images. - Jihyeon Janel Lee; Dylan Grosz; Sicheng Zeng; Burak Uzkent; Marshall Burke; David B. Lobell; Stefano Ermon (CoRR, 2020)

Unsupervised Calibration under Covariate Shift. - Anusri Pampari; Stefano Ermon (CoRR, 2020)

Efficient Learning of Generative Models via Finite-Difference Score Matching. - Tianyu Pang; Kun Xu 004; Chongxuan Li; Yang Song 011; Stefano Ermon; Jun Zhu 001 (CoRR, 2020)

Privacy Preserving Recalibration under Domain Shift. - Rachel Luo; Shengjia Zhao; Jiaming Song; Jonathan Kuck; Stefano Ermon; Silvio Savarese (CoRR, 2020)

Understanding Classifier Mistakes with Generative Models. - Laëtitia Shao; Yang Song 011; Stefano Ermon (CoRR, 2020)

Using satellite imagery to understand and promote sustainable development. - Marshall Burke; Anne Driscoll; David B. Lobell; Stefano Ermon (CoRR, 2020)

PiRank: Learning To Rank via Differentiable Sorting. - Robin M. E. Swezey; Aditya Grover; Bruno Charron; Stefano Ermon (CoRR, 2020)

Privacy-Constrained Policies via Mutual Information Regularized Policy Gradients. - Chris Cundy; Stefano Ermon (CoRR, 2020)

Closed-loop optimization of fast-charging protocols for batteries with machine learning. - Peter M. Attia; Aditya Grover; Norman Jin; Kristen A. Severson; Todor M. Markov; Yang-Hung Liao; Michael H. Chen; Bryan Cheong; Nicholas Perkins; Zi Yang; Patrick K. Herring; Muratahan Aykol; et al. (Nat., 2020)

Domain Adaptation for Human Fall Detection Using WiFi Channel State Information. - Hirokazu Narui; Rui Shu; Félix F. González-Navarro; Stefano Ermon (Precision Health and Medicine, 2020)

Efficient Poverty Mapping from High Resolution Remote Sensing Images. - Kumar Ayush; Burak Uzkent; Kumar Tanmay; Marshall Burke; David B. Lobell; Stefano Ermon (AAAI, 2021)

Predicting Livelihood Indicators from Community-Generated Street-Level Imagery. - Jihyeon Janel Lee; Dylan Grosz; Burak Uzkent; Sicheng Zeng; Marshall Burke; David B. Lobell; Stefano Ermon (AAAI, 2021)

Right Decisions from Wrong Predictions: A Mechanism Design Alternative to Individual Calibration. - Shengjia Zhao; Stefano Ermon (AISTATS, 2021)

Geography-Aware Self-Supervised Learning. - Kumar Ayush; Burak Uzkent; Chenlin Meng; Kumar Tanmay; Marshall Burke; David B. Lobell; Stefano Ermon (ICCV, 2021)

Score-Based Generative Modeling through Stochastic Differential Equations. - Yang Song 011; Jascha Sohl-Dickstein; Diederik P. Kingma; Abhishek Kumar; Stefano Ermon; Ben Poole (ICLR, 2021)

Improved Autoregressive Modeling with Distribution Smoothing. - Chenlin Meng; Jiaming Song; Yang Song 011; Shengjia Zhao; Stefano Ermon (ICLR, 2021)

On the Critical Role of Conventions in Adaptive Human-AI Collaboration. - Andy Shih; Arjun Sawhney; Jovana Kondic; Stefano Ermon; Dorsa Sadigh (ICLR, 2021)

Negative Data Augmentation. - Abhishek Sinha; Kumar Ayush; Jiaming Song; Burak Uzkent; Hongxia Jin; Stefano Ermon (ICLR, 2021)

Denoising Diffusion Implicit Models. - Jiaming Song; Chenlin Meng; Stefano Ermon (ICLR, 2021)

Anytime Sampling for Autoregressive Models via Ordered Autoencoding. - Yilun Xu; Yang Song 011; Sahaj Garg; Linyuan Gong; Rui Shu; Aditya Grover; Stefano Ermon (ICLR, 2021)

Evaluating the Disentanglement of Deep Generative Models through Manifold Topology. - Sharon Zhou; Eric Zelikman; Fred Lu; Andrew Y. Ng; Gunnar E. Carlsson; Stefano Ermon (ICLR, 2021)

Reward Identification in Inverse Reinforcement Learning. - Kuno Kim; Shivam Garg 001; Kirankumar Shiragur; Stefano Ermon (ICML, 2021)

Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mutual Information. - Willie Neiswanger; Ke Alexander Wang; Stefano Ermon (ICML, 2021)

Temporal Predictive Coding For Model-Based Planning In Latent Space. - Tung D. Nguyen; Rui Shu; Tuan Pham; Hung Bui; Stefano Ermon (ICML, 2021)

Accelerating Feedforward Computation via Parallel Nonlinear Equation Solving. - Yang Song 011; Chenlin Meng; Renjie Liao; Stefano Ermon (ICML, 2021)

Challenges in KDD and ML for Sustainable Development. - Laure Berti-Équille; David Dao; Stefano Ermon; Bedharta Goswami (KDD, 2021)

BCD Nets: Scalable Variational Approaches for Bayesian Causal Discovery. - Chris Cundy; Aditya Grover; Stefano Ermon (NeurIPS, 2021)

IQ-Learn: Inverse soft-Q Learning for Imitation. - Divyansh Garg; Shuvam Chakraborty; Chris Cundy; Jiaming Song; Stefano Ermon (NeurIPS, 2021)

Spatial-Temporal Super-Resolution of Satellite Imagery via Conditional Pixel Synthesis. - Yutong He; Dingjie Wang; Nicholas Lai; William Zhang 004; Chenlin Meng; Marshall Burke; David B. Lobell; Stefano Ermon (NeurIPS, 2021)

Imitation with Neural Density Models. - Kuno Kim; Akshat Jindal; Yang Song 011; Jiaming Song; Yanan Sui; Stefano Ermon (NeurIPS, 2021)

Estimating High Order Gradients of the Data Distribution by Denoising. - Chenlin Meng; Yang Song 011; Wenzhe Li; Stefano Ermon (NeurIPS, 2021)

Reliable Decisions with Threshold Calibration. - Roshni Sahoo; Shengjia Zhao; Alyssa Chen; Stefano Ermon (NeurIPS, 2021)

HyperSPNs: Compact and Expressive Probabilistic Circuits. - Andy Shih; Dorsa Sadigh; Stefano Ermon (NeurIPS, 2021)

D2C: Diffusion-Decoding Models for Few-Shot Conditional Generation. - Abhishek Sinha; Jiaming Song; Chenlin Meng; Stefano Ermon (NeurIPS, 2021)

Maximum Likelihood Training of Score-Based Diffusion Models. - Yang Song 011; Conor Durkan; Iain Murray 001; Stefano Ermon (NeurIPS, 2021)

PiRank: Scalable Learning To Rank via Differentiable Sorting. - Robin M. E. Swezey; Aditya Grover; Bruno Charron; Stefano Ermon (NeurIPS, 2021)

CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation. - Yusuke Tashiro; Jiaming Song; Yang Song 011; Stefano Ermon (NeurIPS, 2021)

Improving Compositionality of Neural Networks by Decoding Representations to Inputs. - Mike Wu; Noah D. Goodman; Stefano Ermon (NeurIPS, 2021)

SustainBench: Benchmarks for Monitoring the Sustainable Development Goals with Machine Learning. - Christopher Yeh; Chenlin Meng; Sherrie Wang; Anne Driscoll; Erik Rozi; Patrick Liu; Jihyeon Janel Lee; Marshall Burke; David B. Lobell; Stefano Ermon (NeurIPS Datasets and Benchmarks, 2021)

Pseudo-Spherical Contrastive Divergence. - Lantao Yu; Jiaming Song; Yang Song 011; Stefano Ermon (NeurIPS, 2021)

Calibrating Predictions to Decisions: A Novel Approach to Multi-Class Calibration. - Shengjia Zhao; Michael P. Kim; Roshni Sahoo; Tengyu Ma 001; Stefano Ermon (NeurIPS, 2021)

Multi-agent Imitation Learning with Copulas. - Hongwei Wang 004; Lantao Yu; Zhangjie Cao; Stefano Ermon (ECML/PKDD (1), 2021)

Featurized density ratio estimation. - Kristy Choi; Madeline Liao; Stefano Ermon (UAI, 2021)

Localized Calibration: Metrics and Recalibration. - Rachel Luo; Aadyot Bhatnagar; Huan Wang 014; Caiming Xiong; Silvio Savarese; Yu Bai 017; Shengjia Zhao; Stefano Ermon (CoRR, 2021)

D2C: Diffusion-Denoising Models for Few-shot Conditional Generation. - Abhishek Sinha; Jiaming Song; Chenlin Meng; Stefano Ermon (CoRR, 2021)

SDEdit: Image Synthesis and Editing with Stochastic Differential Equations. - Chenlin Meng; Yang Song 011; Jiaming Song; Jiajun Wu 001; Jun-Yan Zhu; Stefano Ermon (CoRR, 2021)

On the Opportunities and Risks of Foundation Models. - Rishi Bommasani; Drew A. Hudson; Ehsan Adeli 001; Russ B. Altman; Simran Arora; Sydney von Arx; Michael S. Bernstein; Jeannette Bohg; Antoine Bosselut; Emma Brunskill; Erik Brynjolfsson; Shyamal Buch; et al. (CoRR, 2021)

Equivariant Neural Network for Factor Graphs. - Fan-Yun Sun; Jonathan Kuck; Hao Tang 008; Stefano Ermon (CoRR, 2021)

A Unified Framework for Multi-distribution Density Ratio Estimation. - Lantao Yu; Yujia Jin; Stefano Ermon (CoRR, 2021)

Quantifying and Understanding Adversarial Examples in Discrete Input Spaces. - Volodymyr Kuleshov; Evgenii Nikishin; Shantanu Thakoor; Tingfung Lau; Stefano Ermon (CoRR, 2021)

Scalable deep learning to identify brick kilns and aid regulatory capacity. - Jihyeon Janel Lee; Nina R. Brooks; Fahim Tajwar; Marshall Burke; Stefano Ermon; David B. Lobell; Debashish Biswas; Stephen P. Luby (Proc. Natl. Acad. Sci. USA, 2021)

IS-Count: Large-Scale Object Counting from Satellite Images with Covariate-Based Importance Sampling. - Chenlin Meng; Enci Liu; Willie Neiswanger; Jiaming Song; Marshall Burke; David B. Lobell; Stefano Ermon (AAAI, 2022)

Density Ratio Estimation via Infinitesimal Classification. - Kristy Choi; Chenlin Meng; Yang Song 011; Stefano Ermon (AISTATS, 2022)

Efficient Conditional Pre-training for Transfer Learning. - Shuvam Chakraborty; Burak Uzkent; Kumar Ayush; Kumar Tanmay; Evan Sheehan; Stefano Ermon (CVPR Workshops, 2022)

Understanding economic development in rural Africa using satellite imagery, building footprints and deep models. - Amna Elmustafa; Erik Rozi; Yutong He; Gengchen Mai; Stefano Ermon; Marshall Burke; David B. Lobell (SIGSPATIAL/GIS, 2022)

Towards a foundation model for geospatial artificial intelligence (vision paper). - Gengchen Mai; Chris Cundy; Kristy Choi; Yingjie Hu; Ni Lao; Stefano Ermon (SIGSPATIAL/GIS, 2022)

Conditional Imitation Learning for Multi-Agent Games. - Andy Shih; Stefano Ermon; Dorsa Sadigh (HRI, 2022)

Solving Inverse Problems in Medical Imaging with Score-Based Generative Models. - Yang Song 011; Liyue Shen; Lei Xing 001; Stefano Ermon (ICLR, 2022)

An Experimental Design Perspective on Model-Based Reinforcement Learning. - Viraj Mehta; Biswajit Paria; Jeff Schneider 001; Stefano Ermon; Willie Neiswanger (ICLR, 2022)

SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations. - Chenlin Meng; Yutong He; Yang Song 011; Jiaming Song; Jiajun Wu 001; Jun-Yan Zhu; Stefano Ermon (ICLR, 2022)

GeoDiff: A Geometric Diffusion Model for Molecular Conformation Generation. - Minkai Xu; Lantao Yu; Yang Song 011; Chence Shi; Stefano Ermon; Jian Tang 005 (ICLR, 2022)

Comparing Distributions by Measuring Differences that Affect Decision Making. - Shengjia Zhao; Abhishek Sinha; Yutong He; Aidan Perreault; Jiaming Song; Stefano Ermon (ICLR, 2022)

Imitation Learning by Estimating Expertise of Demonstrators. - Mark Beliaev; Andy Shih; Stefano Ermon; Dorsa Sadigh; Ramtin Pedarsani (ICML, 2022)

Modular Conformal Calibration. - Charles Marx; Shengjia Zhao; Willie Neiswanger; Stefano Ermon (ICML, 2022)

ButterflyFlow: Building Invertible Layers with Butterfly Matrices. - Chenlin Meng; Linqi Zhou; Kristy Choi; Tri Dao; Stefano Ermon (ICML, 2022)

Bit Prioritization in Variational Autoencoders via Progressive Coding. - Rui Shu; Stefano Ermon (ICML, 2022)

A General Recipe for Likelihood-free Bayesian Optimization. - Jiaming Song; Lantao Yu; Willie Neiswanger; Stefano Ermon (ICML, 2022)

Experience Replay with Likelihood-free Importance Weights. - Samarth Sinha; Jiaming Song; Animesh Garg; Stefano Ermon (L4DC, 2022)

SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagery. - Yezhen Cong; Samar Khanna; Chenlin Meng; Patrick Liu; Erik Rozi; Yutong He; Marshall Burke; David B. Lobell; Stefano Ermon (NeurIPS, 2022)

FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness. - Tri Dao; Daniel Y. Fu; Stefano Ermon; Atri Rudra; Christopher Ré (NeurIPS, 2022)

Improving Self-Supervised Learning by Characterizing Idealized Representations. - Yann Dubois; Stefano Ermon; Tatsunori B. Hashimoto; Percy Liang (NeurIPS, 2022)

LISA: Learning Interpretable Skill Abstractions from Language. - Divyansh Garg; Skanda Vaidyanath; Kuno Kim; Jiaming Song; Stefano Ermon (NeurIPS, 2022)

Denoising Diffusion Restoration Models. - Bahjat Kawar; Michael Elad; Stefano Ermon; Jiaming Song (NeurIPS, 2022)

Exploration via Planning for Information about the Optimal Trajectory. - Viraj Mehta; Ian Char; Joseph Abbate; Rory Conlin; Mark D. Boyer; Stefano Ermon; Jeff Schneider 001; Willie Neiswanger (NeurIPS, 2022)

Concrete Score Matching: Generalized Score Matching for Discrete Data. - Chenlin Meng; Kristy Choi; Jiaming Song; Stefano Ermon (NeurIPS, 2022)

Generalizing Bayesian Optimization with Decision-theoretic Entropies. - Willie Neiswanger; Lantao Yu; Shengjia Zhao; Chenlin Meng; Stefano Ermon (NeurIPS, 2022)

Transform Once: Efficient Operator Learning in Frequency Domain. - Michael Poli; Stefano Massaroli; Federico Berto; Jinkyoo Park; Tri Dao; Christopher Ré; Stefano Ermon (NeurIPS, 2022)

Self-Similarity Priors: Neural Collages as Differentiable Fractal Representations. - Michael Poli; Winnie Xu; Stefano Massaroli; Chenlin Meng; Kuno Kim; Stefano Ermon (NeurIPS, 2022)

Training and Inference on Any-Order Autoregressive Models the Right Way. - Andy Shih; Dorsa Sadigh; Stefano Ermon (NeurIPS, 2022)

Local calibration: metrics and recalibration. - Rachel Luo; Aadyot Bhatnagar; Yu Bai 017; Shengjia Zhao; Huan Wang 014; Caiming Xiong; Silvio Savarese; Stefano Ermon; Edward Schmerling; Marco Pavone 001 (UAI, 2022)

Tracking Urbanization in Developing Regions with Remote Sensing Spatial-Temporal Super-Resolution. - Yutong He; William Zhang 004; Chenlin Meng; Marshall Burke; David B. Lobell; Stefano Ermon (CoRR, 2022)

Bayesian Algorithm Execution for Tuning Particle Accelerator Emittance with Partial Measurements. - Sara A. Miskovich; Willie Neiswanger; William Colocho; Claudio Emma; Jacqueline Garrahan; Timothy Maxwell; Christopher Mayes; Stefano Ermon; Auralee Edelen; Daniel Ratner (CoRR, 2022)

JPEG Artifact Correction using Denoising Diffusion Restoration Models. - Bahjat Kawar; Jiaming Song; Stefano Ermon; Michael Elad (CoRR, 2022)

On Distillation of Guided Diffusion Models. - Chenlin Meng; Ruiqi Gao; Diederik P. Kingma; Stefano Ermon; Jonathan Ho; Tim Salimans (CoRR, 2022)

Regularizing Score-based Models with Score Fokker-Planck Equations. - Chieh-Hsin Lai; Yuhta Takida; Naoki Murata; Toshimitsu Uesaka; Yuki Mitsufuji; Stefano Ermon (CoRR, 2022)

LMPriors: Pre-Trained Language Models as Task-Specific Priors. - Kristy Choi; Chris Cundy; Sanjari Srivastava; Stefano Ermon (CoRR, 2022)

Offline Imitation Learning with Suboptimal Demonstrations via Relaxed Distribution Matching. - Lantao Yu; Tianhe Yu; Jiaming Song; Willie Neiswanger; Stefano Ermon (AAAI, 2023)

But Are You Sure? An Uncertainty-Aware Perspective on Explainable AI. - Charles Marx; Youngsuk Park; Hilaf Hasson; Yuyang Wang 001; Stefano Ermon; Luke Huan (AISTATS, 2023)

Ideal Abstractions for Decision-Focused Learning. - Michael Poli; Stefano Massaroli; Stefano Ermon; Bryan Wilder; Eric Horvitz (AISTATS, 2023)

On Distillation of Guided Diffusion Models. - Chenlin Meng; Robin Rombach; Ruiqi Gao; Diederik P. Kingma; Stefano Ermon; Jonathan Ho; Tim Salimans (CVPR, 2023)

The Role of Linguistic Priors in Measuring Compositional Generalization of Vision-Language Models. - Chenwei Wu 002; Li Erran Li; Stefano Ermon; Patrick Haffner; Rong Ge 001; Zaiwei Zhang (ICBINB, 2023)

GlueGen: Plug and Play Multi-modal Encoders for X-to-image Generation. - Can Qin; Ning Yu 006; Chen Xing; Shu Zhang 007; Zeyuan Chen 001; Stefano Ermon; Yun Fu 001; Caiming Xiong; Ran Xu 001 (ICCV, 2023)

End-to-End Diffusion Latent Optimization Improves Classifier Guidance. - Bram Wallace; Akash Gokul; Stefano Ermon; Nikhil Naik (ICCV, 2023)

Generative Modeling Helps Weak Supervision (and Vice Versa). - Benedikt Boecking; Nicholas Carl Roberts; Willie Neiswanger; Stefano Ermon; Frederic Sala; Artur Dubrawski (ICLR, 2023)

Extreme Q-Learning: MaxEnt RL without Entropy. - Divyansh Garg; Joey Hejna; Matthieu Geist; Stefano Ermon (ICLR, 2023)

Understanding and Adopting Rational Behavior by Bellman Score Estimation. - Kuno Kim; Stefano Ermon (ICLR, 2023)

Dual Diffusion Implicit Bridges for Image-to-Image Translation. - Xuan Su; Jiaming Song; Chenlin Meng; Stefano Ermon (ICLR, 2023)

FP-Diffusion: Improving Score-based Diffusion Models by Enforcing the Underlying Score Fokker-Planck Equation. - Chieh-Hsin Lai; Yuhta Takida; Naoki Murata; Toshimitsu Uesaka; Yuki Mitsufuji; Stefano Ermon (ICML, 2023)

Reflected Diffusion Models. - Aaron Lou; Stefano Ermon (ICML, 2023)

CSP: Self-Supervised Contrastive Spatial Pre-Training for Geospatial-Visual Representations. - Gengchen Mai; Ni Lao; Yutong He; Jiaming Song; Stefano Ermon (ICML, 2023)

GibbsDDRM: A Partially Collapsed Gibbs Sampler for Solving Blind Inverse Problems with Denoising Diffusion Restoration. - Naoki Murata; Koichi Saito; Chieh-Hsin Lai; Yuhta Takida; Toshimitsu Uesaka; Yuki Mitsufuji; Stefano Ermon (ICML, 2023)

Hyena Hierarchy: Towards Larger Convolutional Language Models. - Michael Poli; Stefano Massaroli; Eric Nguyen; Daniel Y. Fu; Tri Dao; Stephen Baccus; Yoshua Bengio; Stefano Ermon; Christopher Ré (ICML, 2023)

Long Horizon Temperature Scaling. - Andy Shih; Dorsa Sadigh; Stefano Ermon (ICML, 2023)

Geometric Latent Diffusion Models for 3D Molecule Generation. - Minkai Xu; Alexander S. Powers; Ron O. Dror; Stefano Ermon; Jure Leskovec (ICML, 2023)

Deep Latent State Space Models for Time-Series Generation. - Linqi Zhou; Michael Poli; Winnie Xu; Stefano Massaroli; Stefano Ermon (ICML, 2023)

Graph and Geometry Generative Modeling for Drug Discovery. - Minkai Xu; Meng Liu 015; Wengong Jin; Shuiwang Ji; Jure Leskovec; Stefano Ermon (KDD, 2023)

MUDiff: Unified Diffusion for Complete Molecule Generation. - Chenqing Hua; Sitao Luan; Minkai Xu; Zhitao Ying; Jie Fu; Stefano Ermon; Doina Precup (LoG, 2023)

GEO-Bench: Toward Foundation Models for Earth Monitoring. - Alexandre Lacoste; Nils Lehmann; Pau Rodríguez; Evan D. Sherwin; Hannah Kerner; Björn Lütjens; Jeremy Irvin; David Dao; Hamed Alemohammad; Alexandre Drouin; Mehmet Gunturkun; Gabriel Huang; et al. (NeurIPS, 2023)

Holistic Evaluation of Text-to-Image Models. - Tony Lee; Michihiro Yasunaga; Chenlin Meng; Yifan Mai 001; Joon Sung Park; Agrim Gupta; Yunzhi Zhang; Deepak Narayanan; Hannah Teufel; Marco Bellagente; Minguk Kang; Taesung Park; et al. (NeurIPS, 2023)

Scaling Riemannian Diffusion Models. - Aaron Lou; Minkai Xu; Adam Farris; Stefano Ermon (NeurIPS, 2023)

Calibration by Distribution Matching: Trainable Kernel Calibration Metrics. - Charlie Marx; Sofian Zalouk; Stefano Ermon (NeurIPS, 2023)

Laughing Hyena Distillery: Extracting Compact Recurrences From Convolutions. - Stefano Massaroli; Michael Poli; Daniel Y. Fu; Hermann Kumbong; Rom N. Parnichkun; David W. Romero; Aman Timalsina; Quinn McIntyre; Beidi Chen; Atri Rudra; Ce Zhang 001; Christopher Ré; et al. (NeurIPS, 2023)

HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide Resolution. - Eric Nguyen; Michael Poli; Marjan Faizi; Armin W. Thomas; Michael Wornow; Callum Birch-Sykes; Stefano Massaroli; Aman Patel; Clayton M. Rabideau; Yoshua Bengio; Stefano Ermon; Christopher Ré; et al. (NeurIPS, 2023)

UniControl: A Unified Diffusion Model for Controllable Visual Generation In the Wild. - Can Qin; Shu Zhang 007; Ning Yu 006; Yihao Feng; Xinyi Yang 002; Yingbo Zhou; Huan Wang 014; Juan Carlos Niebles; Caiming Xiong; Silvio Savarese; Stefano Ermon; Yun Fu 001; et al. (NeurIPS, 2023)

Direct Preference Optimization: Your Language Model is Secretly a Reward Model. - Rafael Rafailov; Archit Sharma; Eric Mitchell; Christopher D. Manning; Stefano Ermon; Chelsea Finn (NeurIPS, 2023)

Parallel Sampling of Diffusion Models. - Andy Shih; Suneel Belkhale; Stefano Ermon; Dorsa Sadigh; Nima Anari (NeurIPS, 2023)

Equivariant Flow Matching with Hybrid Probability Transport for 3D Molecule Generation. - Yuxuan Song; Jingjing Gong; Minkai Xu; Ziyao Cao; Yanyan Lan; Stefano Ermon; Hao Zhou 012; Wei-Ying Ma (NeurIPS, 2023)

SIGGRAPH 2023 Course on Diffusion Models. - Chenlin Meng; Jiaming Song; Shuang Li; Jun-Yan Zhu; Stefano Ermon; Tsung-Yi Lin; Chen-Hsuan Lin 001; Karsten Kreis (SIGGRAPH Courses, 2023)

Building Coverage Estimation with Low-resolution Remote Sensing Imagery. - Enci Liu; Chenlin Meng; Matthew Kolodner; Eun Jee Sung; Sihang Chen; Marshall Burke; David B. Lobell; Stefano Ermon (CoRR, 2023)

On the Equivalence of Consistency-Type Models: Consistency Models, Consistent Diffusion Models, and Fokker-Planck Regularization. - Chieh-Hsin Lai; Yuhta Takida; Toshimitsu Uesaka; Naoki Murata; Yuki Mitsufuji; Stefano Ermon (CoRR, 2023)

Sphere2Vec: A General-Purpose Location Representation Learning over a Spherical Surface for Large-Scale Geospatial Predictions. - Gengchen Mai; Yao Xuan; Wenyun Zuo; Yutong He; Jiaming Song; Stefano Ermon; Krzysztof Janowicz; Ni Lao (CoRR, 2023)

SSIF: Learning Continuous Image Representation for Spatial-Spectral Super-Resolution. - Gengchen Mai; Ni Lao; Weiwei Sun; Yuchi Ma; Jiaming Song; Chenlin Meng; Hongxu Ma; Jinmeng Rao; Ziyuan Li; Stefano Ermon (CoRR, 2023)

Discrete Diffusion Language Modeling by Estimating the Ratios of the Data Distribution. - Aaron Lou; Chenlin Meng; Stefano Ermon (CoRR, 2023)

Generative Fractional Diffusion Models. - Gabriel Nobis; Marco Aversa; Maximilian Springenberg; Michael Detzel; Stefano Ermon; Shinichi Nakajima; Roderick Murray-Smith; Sebastian Lapuschkin; Christoph Knochenhauer; Luis Oala; Wojciech Samek (CoRR, 2023)

Scaling Riemannian Diffusion Models. - Aaron Lou; Minkai Xu; Stefano Ermon (CoRR, 2023)

Calibration by Distribution Matching: Trainable Kernel Calibration Metrics. - Charles Marx; Sofian Zalouk; Stefano Ermon (CoRR, 2023)

Equivariant Flow Matching with Hybrid Probability Transport. - Yuxuan Song; Jingjing Gong; Minkai Xu; Ziyao Cao; Yanyan Lan; Stefano Ermon; Hao Zhou 012; Wei-Ying Ma (CoRR, 2023)

Neural Network Compression for Noisy Storage Devices. - Berivan Isik; Kristy Choi; Xin Zheng 013; Tsachy Weissman; Stefano Ermon; H.-S. Philip Wong; Armin Alaghi (ACM Trans. Embed. Comput. Syst., 2023)

MERMAIDE: Learning to Align Learners using Model-Based Meta-Learning. - Arundhati Banerjee; Soham R. Phade; Stefano Ermon; Stephan Zheng (Trans. Mach. Learn. Res., 2023)

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models. - Aarohi Srivastava; Abhinav Rastogi; Abhishek Rao; Abu Awal Md Shoeb; Abubakar Abid; Adam Fisch; Adam R. Brown; Adam Santoro; Aditya Gupta; Adrià Garriga-Alonso; Agnieszka Kluska; Aitor Lewkowycz; et al. (Trans. Mach. Learn. Res., 2023)

Towards general-purpose representation learning of polygonal geometries. - Gengchen Mai; Chiyu Jiang 001; Weiwei Sun; Rui Zhu 008; Yao Xuan; Ling Cai; Krzysztof Janowicz; Stefano Ermon; Ni Lao (GeoInformatica, 2023)

Efficient Spatially Sparse Inference for Conditional GANs and Diffusion Models. - Muyang Li; Ji Lin 002; Chenlin Meng; Stefano Ermon; Song Han 003; Jun-Yan Zhu (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

Uncertainty Quantification for Forward and Inverse Problems of PDEs via Latent Global Evolution. - Tailin Wu; Willie Neiswanger; Hongtao Zheng; Stefano Ermon; Jure Leskovec (AAAI, 2024)

HarvestNet: A Dataset for Detecting Smallholder Farming Activity Using Harvest Piles and Remote Sensing. - Jonathan Xu; Amna Elmustafa; Liya Weldegebriel; Emnet Negash; Richard Lee; Chenlin Meng; Stefano Ermon; David B. Lobell (AAAI, 2024)

Disentangling Length from Quality in Direct Preference Optimization. - Ryan Park; Rafael Rafailov; Stefano Ermon; Chelsea Finn (ACL (Findings), 2024)

Privacy-Constrained Policies via Mutual Information Regularized Policy Gradients. - Chris Cundy; Rishi Desai; Stefano Ermon (AISTATS, 2024)

HIVE: Harnessing Human Feedback for Instructional Visual Editing. - Shu Zhang 007; Xinyi Yang 002; Yihao Feng; Can Qin; Chia-Chih Chen; Ning Yu 006; Zeyuan Chen 001; Huan Wang 014; Silvio Savarese; Stefano Ermon; Caiming Xiong; Ran Xu 001 (CVPR, 2024)

On the Scalability of Diffusion-based Text-to-Image Generation. - Hao Li; Yang Zou; Ying Wang; Orchid Majumder; Yusheng Xie; R. Manmatha; Ashwin Swaminathan; Zhuowen Tu; Stefano Ermon; Stefano Soatto (CVPR, 2024)

Diffusion Model Alignment Using Direct Preference Optimization. - Bram Wallace; Meihua Dang; Rafael Rafailov; Linqi Zhou; Aaron Lou; Senthil Purushwalkam; Stefano Ermon; Caiming Xiong; Shafiq Joty; Nikhil Naik (CVPR, 2024)

DreamPropeller: Supercharge Text-to-3D Generation with Parallel Sampling. - Linqi Zhou; Andy Shih; Chenlin Meng; Stefano Ermon (CVPR, 2024)

TorchSpatial: A Python Package for Spatial Representation Learning and Geo-Aware Model Development. - Qian Cao; Nemin Wu; Zhangyu Wang; Zeping Liu; Yanlin Qi; Jielu Zhang; Joshua Ni; Xiaobai Angela Yao; Hongxu Ma; Lan Mu; Stefano Ermon; Tanuja Ganu; et al. (GeoIndstry@SIGSPATIAL, 2024)

Cross-Modal Contextualized Diffusion Models for Text-Guided Visual Generation and Editing. - Ling Yang 006; Zhilong Zhang; Zhaochen Yu; Jingwei Liu; Minkai Xu; Stefano Ermon; Bin Cui 001 (ICLR, 2024)

SequenceMatch: Imitation Learning for Autoregressive Sequence Modelling with Backtracking. - Chris Cundy; Stefano Ermon (ICLR, 2024)

Manifold Preserving Guided Diffusion. - Yutong He; Naoki Murata; Chieh-Hsin Lai; Yuhta Takida; Toshimitsu Uesaka; Dongjun Kim; Wei-Hsiang Liao 001; Yuki Mitsufuji; J. Zico Kolter; Ruslan Salakhutdinov; Stefano Ermon (ICLR, 2024)

DiffusionSat: A Generative Foundation Model for Satellite Imagery. - Samar Khanna; Patrick Liu; Linqi Zhou; Chenlin Meng; Robin Rombach; Marshall Burke; David B. Lobell; Stefano Ermon (ICLR, 2024)

Consistency Trajectory Models: Learning Probability Flow ODE Trajectory of Diffusion. - Dongjun Kim; Chieh-Hsin Lai; Wei-Hsiang Liao 001; Naoki Murata; Yuhta Takida; Toshimitsu Uesaka; Yutong He; Yuki Mitsufuji; Stefano Ermon (ICLR, 2024)

GeoLLM: Extracting Geospatial Knowledge from Large Language Models. - Rohin Manvi; Samar Khanna; Gengchen Mai; Marshall Burke; David B. Lobell; Stefano Ermon (ICLR, 2024)

Language Model Detectors Are Easily Optimized Against. - Charlotte Nicks; Eric Mitchell; Rafael Rafailov; Archit Sharma; Christopher D. Manning; Chelsea Finn; Stefano Ermon (ICLR, 2024)

Denoising Diffusion Bridge Models. - Linqi Zhou; Aaron Lou; Samar Khanna; Stefano Ermon (ICLR, 2024)

Mastering Text-to-Image Diffusion: Recaptioning, Planning, and Generating with Multimodal LLMs. - Ling Yang 006; Zhaochen Yu; Chenlin Meng; Minkai Xu; Stefano Ermon; Bin Cui 001 (ICML, 2024)

Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution. - Aaron Lou; Chenlin Meng; Stefano Ermon (ICML, 2024)

Large Language Models are Geographically Biased. - Rohin Manvi; Samar Khanna; Marshall Burke; David B. Lobell; Stefano Ermon (ICML, 2024)

State-Free Inference of State-Space Models: The *Transfer Function* Approach. - Rom N. Parnichkun; Stefano Massaroli; Alessandro Moro; Jimmy T. H. Smith; Ramin M. Hasani; Mathias Lechner; Qi An; Christopher Ré; Hajime Asama; Stefano Ermon; Taiji Suzuki; Michael Poli; et al. (ICML, 2024)

Mechanistic Design and Scaling of Hybrid Architectures. - Michael Poli; Armin W. Thomas; Eric Nguyen; Pragaash Ponnusamy; Björn Deiseroth; Kristian Kersting; Taiji Suzuki; Brian L. Hie; Stefano Ermon; Christopher Ré; Ce Zhang 001; Stefano Massaroli (ICML, 2024)

Preference Fine-Tuning of LLMs Should Leverage Suboptimal, On-Policy Data. - Fahim Tajwar; Anikait Singh; Archit Sharma; Rafael Rafailov; Jeff Schneider 001; Tengyang Xie; Stefano Ermon; Chelsea Finn; Aviral Kumar (ICML, 2024)

Equivariant Graph Neural Operator for Modeling 3D Dynamics. - Minkai Xu; Jiaqi Han; Aaron Lou; Jean Kossaifi; Arvind Ramanathan; Kamyar Azizzadenesheli; Jure Leskovec; Stefano Ermon; Anima Anandkumar (ICML, 2024)

Active Learning for Derivative-Based Global Sensitivity Analysis with Gaussian Processes. - Syrine Belakaria; Ben Letham; Jana Doppa; Barbara Engelhardt; Stefano Ermon; Eytan Bakshy (NeurIPS, 2024)

Aligning Target-Aware Molecule Diffusion Models with Exact Energy Optimization. - Siyi Gu; Minkai Xu; Alexander S. Powers; Weili Nie; Tomas Geffner; Karsten Kreis; Jure Leskovec; Arash Vahdat; Stefano Ermon (NeurIPS, 2024)

Geometric Trajectory Diffusion Models. - Jiaqi Han; Minkai Xu; Aaron Lou; Haotian Ye; Stefano Ermon (NeurIPS, 2024)

PaGoDA: Progressive Growing of a One-Step Generator from a Low-Resolution Diffusion Teacher. - Dongjun Kim; Chieh-Hsin Lai; Wei-Hsiang Liao 001; Yuhta Takida; Naoki Murata; Toshimitsu Uesaka; Yuki Mitsufuji; Stefano Ermon (NeurIPS, 2024)

Generative Fractional Diffusion Models. - Gabriel Nobis; Maximilian Springenberg; Marco Aversa; Michael Detzel; Rembert Daems; Roderick Murray-Smith; Shinichi Nakajima; Sebastian Lapuschkin; Stefano Ermon; Tolga Birdal; Manfred Opper; Christoph Knochenhauer; et al. (NeurIPS, 2024)

TrAct: Making First-layer Pre-Activations Trainable. - Felix Petersen; Christian Borgelt; Stefano Ermon (NeurIPS, 2024)

Newton Losses: Using Curvature Information for Learning with Differentiable Algorithms. - Felix Petersen; Christian Borgelt; Tobias Sutter; Hilde Kuehne; Oliver Deussen; Stefano Ermon (NeurIPS, 2024)

Convolutional Differentiable Logic Gate Networks. - Felix Petersen; Hilde Kuehne; Christian Borgelt; Julian Welzel; Stefano Ermon (NeurIPS, 2024)

Self-Refining Diffusion Samplers: Enabling Parallelization via Parareal Iterations. - Nikil Roashan Selvam; Amil Merchant; Stefano Ermon (NeurIPS, 2024)

TorchSpatial: A Location Encoding Framework and Benchmark for Spatial Representation Learning. - Nemin Wu; Qian Cao; Zhangyu Wang; Zeping Liu; Yanlin Qi; Jielu Zhang; Joshua Ni; Xiaobai Angela Yao; Hongxu Ma; Lan Mu; Stefano Ermon; Tanuja Ganu; et al. (NeurIPS, 2024)

TFG: Unified Training-Free Guidance for Diffusion Models. - Haotian Ye; Haowei Lin; Jiaqi Han; Minkai Xu; Sheng Liu; Yitao Liang; Jianzhu Ma; James Y. Zou; Stefano Ermon (NeurIPS, 2024)

Segment Any Change. - Zhuo Zheng; Yanfei Zhong; Liangpei Zhang 001; Stefano Ermon (NeurIPS, 2024)

MADiff: Offline Multi-agent Learning with Diffusion Models. - Zhengbang Zhu; Minghuan Liu; Liyuan Mao; Bingyi Kang; Minkai Xu; Yong Yu 001; Stefano Ermon; Weinan Zhang 001 (NeurIPS, 2024)

ExPLoRA: Parameter-Efficient Extended Pre-Training to Adapt Vision Transformers under Domain Shifts. - Samar Khanna; Medhanie Irgau; David B. Lobell; Stefano Ermon (CoRR, 2024)

Consistency Flow Matching: Defining Straight Flows with Velocity Consistency. - Ling Yang 006; Zixiang Zhang; Zhilong Zhang; Xingchao Liu; Minkai Xu; Wentao Zhang 001; Chenlin Meng; Stefano Ermon; Bin Cui 001 (CoRR, 2024)

Unsupervised Anomaly Detection Using Diffusion Trend Analysis. - Eunwoo Kim; Un Yang; Cheol Lae Roh; Stefano Ermon (CoRR, 2024)

CPSample: Classifier Protected Sampling for Guarding Training Data During Diffusion. - Joshua Kazdan; Hao Sun; Jiaqi Han; Felix Petersen; Stefano Ermon (CoRR, 2024)

Bellman Diffusion: Generative Modeling as Learning a Linear Operator in the Distribution Space. - Yangming Li; Chieh-Hsin Lai; Carola-Bibiane Schönlieb; Yuki Mitsufuji; Stefano Ermon (CoRR, 2024)

Adaptive Inference-Time Compute: LLMs Can Predict if They Can Do Better, Even Mid-Generation. - Rohin Manvi; Anikait Singh; Stefano Ermon (CoRR, 2024)

Trans4D: Realistic Geometry-Aware Transition for Compositional Text-to-4D Synthesis. - Bohan Zeng; Ling Yang 006; Siyu Li; Jiaming Liu; Zixiang Zhang; Juanxi Tian; Kaixin Zhu; Yongzhen Guo; Fu-Yun Wang; Minkai Xu; Stefano Ermon; Wentao Zhang 001 (CoRR, 2024)

Generalizing Stochastic Smoothing for Differentiation and Gradient Estimation. - Felix Petersen; Christian Borgelt; Aashwin Ananda Mishra; Stefano Ermon (CoRR, 2024)

G2D2: Gradient-guided Discrete Diffusion for image inverse problem solving. - Naoki Murata; Chieh-Hsin Lai; Yuhta Takida; Toshimitsu Uesaka; Bac Nguyen; Stefano Ermon; Yuki Mitsufuji (CoRR, 2024)

Mitigating Embedding Collapse in Diffusion Models for Categorical Data. - Bac Nguyen; Chieh-Hsin Lai; Yuhta Takida; Naoki Murata; Toshimitsu Uesaka; Stefano Ermon; Yuki Mitsufuji (CoRR, 2024)

TabDiff: a Multi-Modal Diffusion Model for Tabular Data Generation. - Juntong Shi; Minkai Xu; Harper Hua; Hengrui Zhang; Stefano Ermon; Jure Leskovec (CoRR, 2024)

f-PO: Generalizing Preference Optimization with f-divergence Minimization. - Jiaqi Han; Mingjian Jiang; Yuxuan Song; Jure Leskovec; Stefano Ermon; Minkai Xu (CoRR, 2024)

Efficient Scaling of Diffusion Transformers for Text-to-Image Generation. - Hao Li; Shamit Lal; Zhiheng Li; Yusheng Xie; Ying Wang; Yang Zou; Orchid Majumder; R. Manmatha; Zhuowen Tu; Stefano Ermon; Stefano Soatto; Ashwin Swaminathan (CoRR, 2024)

Multipoint-BAX: a new approach for efficiently tuning particle accelerator emittance via virtual objectives. - Sara A. Miskovich; Willie Neiswanger; William Colocho; Claudio Emma; Jacqueline Garrahan; Timothy Maxwell; Christopher Mayes; Stefano Ermon; Auralee Edelen; Daniel Ratner (Mach. Learn. Sci. Technol., 2024)

f-PO: Generalizing Preference Optimization with f-divergence Minimization. - Jiaqi Han; Mingjian Jiang; Yuxuan Song; Stefano Ermon; Minkai Xu (AISTATS, 2025)

Personalized Preference Fine-tuning of Diffusion Models. - Meihua Dang; Anikait Singh; Linqi Zhou; Stefano Ermon; Jiaming Song (CVPR, 2025)

Data Unlearning in Diffusion Models. - Silas Alberti; Kenan Hasanaliyev; Manav Shah; Stefano Ermon (ICLR, 2025)

TEOChat: A Large Vision-Language Assistant for Temporal Earth Observation Data. - Jeremy Andrew Irvin; Emily Ruoyu Liu; Joyce Chuyi Chen; Ines Dormoy; Jinyoung Kim; Samar Khanna; Zhuo Zheng; Stefano Ermon (ICLR, 2025)

CPSample: Classifier Protected Sampling for Guarding Training Data During Diffusion. - Joshua Kazdan; Hao Sun; Jiaqi Han; Felix Petersen; Frederick Vu; Stefano Ermon (ICLR, 2025)

TFG-Flow: Training-free Guidance in Multimodal Generative Flow. - Haowei Lin; Shanda Li; Haotian Ye; Yiming Yang; Stefano Ermon; Yitao Liang; Jianzhu Ma (ICLR, 2025)

TabDiff: a Mixed-type Diffusion Model for Tabular Data Generation. - Juntong Shi; Minkai Xu; Harper Hua; Hengrui Zhang; Stefano Ermon; Jure Leskovec (ICLR, 2025)

Energy-Based Diffusion Language Models for Text Generation. - Minkai Xu; Tomas Geffner; Karsten Kreis; Weili Nie; Yilun Xu; Jure Leskovec; Stefano Ermon; Arash Vahdat (ICLR, 2025)

Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems. - Xuan Zhang; Limei Wang; Jacob Helwig; Youzhi Luo; Cong Fu 003; Yaochen Xie; Meng Liu 015; Yuchao Lin; Zhao Xu 005; Keqiang Yan; Keir Adams; Maurice Weiler; et al. (Found. Trends Mach. Learn., 2025)

Non-Myopic Multi-Objective Bayesian Optimization. - Syrine Belakaria; Alaleh Ahmadianshalchi; Barbara E. Engelhardt; Stefano Ermon; Jana Doppa (Trans. Mach. Learn. Res., 2025)

Calibrated Probabilistic Forecasts for Arbitrary Sequences. - Charles Marx; Volodymyr Kuleshov; Stefano Ermon (Trans. Mach. Learn. Res., 2025)

G2D2: Gradient-Guided Discrete Diffusion for Inverse Problem Solving. - Naoki Murata; Chieh-Hsin Lai; Yuhta Takida; Toshimitsu Uesaka; Bac Nguyen; Stefano Ermon; Yuki Mitsufuji (Trans. Mach. Learn. Res., 2025)

Training-Free Safe Denoisers for Safe Use of Diffusion Models. - Mingyu Kim; Dongjun Kim; Amman Yusuf; Stefano Ermon; Mijung Park (CoRR, 2025)

FSPO: Few-Shot Preference Optimization of Synthetic Preference Data in LLMs Elicits Effective Personalization to Real Users. - Anikait Singh; Sheryl Hsu; Kyle Hsu; Eric Mitchell; Stefano Ermon; Tatsunori Hashimoto; Archit Sharma; Chelsea Finn (CoRR, 2025)

Changen2: Multi-Temporal Remote Sensing Generative Change Foundation Model. - Zhuo Zheng; Stefano Ermon; Dongjun Kim; Liangpei Zhang 001; Yanfei Zhong (IEEE Trans. Pattern Anal. Mach. Intell., 2025)

Systems and Algorithms for Convolutional Multi-Hybrid Language Models at Scale. - Jerome Ku; Eric Nguyen; David W. Romero; Garyk Brixi; Brandon Yang; Anton Vorontsov; Ali Taghibakhshi; Amy X. Lu; Dave P. Burke; Greg Brockman; Stefano Massaroli; Christopher Ré; et al. (CoRR, 2025)

Inductive Moment Matching. - Linqi Zhou; Stefano Ermon; Jiaming Song (CoRR, 2025)

Preference-Guided Diffusion for Multi-Objective Offline Optimization. - Yashas Annadani; Syrine Belakaria; Stefano Ermon; Stefan Bauer; Barbara E. Engelhardt (CoRR, 2025)

Sharpe Ratio-Guided Active Learning for Preference Optimization in RLHF. - Syrine Belakaria; Joshua Kazdan; Charles Marx; Chris Cundy; Willie Neiswanger; Sanmi Koyejo; Barbara E. Engelhardt; Stefano Ermon (CoRR, 2025)

Reviving Any-Subset Autoregressive Models with Principled Parallel Sampling and Speculative Decoding. - Gabe Guo; Stefano Ermon (CoRR, 2025)

RelDiff: Relational Data Generative Modeling with Graph-Based Diffusion Models. - Valter Hudovernik; Minkai Xu; Juntong Shi; Lovro Subelj; Stefano Ermon; Erik Strumbelj; Jure Leskovec (CoRR, 2025)

Exploring Diffusion Transformer Designs via Grafting. - Keshigeyan Chandrasegaran; Michael Poli; Daniel Y. Fu; Dongjun Kim; Lea M. Hadzic; Manling Li; Agrim Gupta; Stefano Massaroli; Azalia Mirhoseini; Juan Carlos Niebles; Stefano Ermon; Li Fei-Fei 001 (CoRR, 2025)

Scaling Probabilistic Circuits via Monarch Matrices. - Honghua Zhang; Meihua Dang; Benjie Wang 001; Stefano Ermon; Nanyun Peng 001; Guy Van den Broeck (CoRR, 2025)

Mercury: Ultra-Fast Language Models Based on Diffusion. - Samar Khanna; Siddhant Kharbanda; Shufan Li; Harshit Varma; Eric Wang; Sawyer Birnbaum; Ziyang Luo; Yanis Miraoui; Akash Palrecha; Stefano Ermon; Aditya Grover; Volodymyr Kuleshov (CoRR, 2025)

GeoAda: Efficiently Finetune Geometric Diffusion Models with Equivariant Adapters. - Wanjia Zhao; Jiaqi Han; Siyi Gu; Mingjian Jiang; James Zou 001; Stefano Ermon (CoRR, 2025)

Zero-Shot Cyclic Peptide Design via Composable Geometric Constraints. - Dapeng Jiang; Xiangzhe Kong; Jiaqi Han; Mingyu Li; Rui Jiao; Wenbing Huang 001; Stefano Ermon; Jianzhu Ma; Yang Liu 005 (CoRR, 2025)

Discrete Diffusion Trajectory Alignment via Stepwise Decomposition. - Jiaqi Han; Austin Wang; Minkai Xu; Wenda Chu; Meihua Dang; Yisong Yue; Stefano Ermon (CoRR, 2025)

Divergence Minimization Preference Optimization for Diffusion Model Alignment. - Binxu Li; Minkai Xu; Meihua Dang; Stefano Ermon (CoRR, 2025)

Inference-Time Scaling of Diffusion Language Models with Particle Gibbs Sampling. - Meihua Dang; Jiaqi Han; Minkai Xu; Kai Xu; Akash Srivastava; Stefano Ermon (CoRR, 2025)

CHORDS: Diffusion Sampling Accelerator with Multi-core Hierarchical ODE Solvers. - Jiaqi Han; Haotian Ye; Puheng Li; Minkai Xu; James Zou 001; Stefano Ermon (CoRR, 2025)

Probabilistic Graphical Models: A Concise Tutorial. - Jacqueline R. M. A. Maasch; Willie Neiswanger; Stefano Ermon; Volodymyr Kuleshov (CoRR, 2025)