General Bounds on Satisfiability Thresholds for Random CSPs via Fourier Analysis

Colin Wei, Stefano Ermon

Variable Elimination in the Fourier Domain

Yexiang Xue, Stefano Ermon, Ronan Le Bras +2

Sliced Score Matching: A Scalable Approach to Density and Score Estimation

Yang Song, Sahaj Garg, Jiaxin Shi +1

Hierarchical Modeling of Seed Variety Yields and Decision Making for Future Planting Plans

Huaiyang Zhong, Xiaocheng Li, David Lobell +2

Learning Hierarchical Features from Generative Models

Shengjia Zhao, Jiaming Song, Stefano Ermon

Mapping Missing Population in Rural India: A Deep Learning Approach with Satellite Imagery

Wenjie Hu, Jay Harshadbhai Patel, Zoe-Alanah Robert +6

Pattern Decomposition with Complex Combinatorial Constraints: Application to Materials Discovery

Stefano Ermon, Ronan Le Bras, Santosh K. Suram +4

Amortized Inference Regularization

Rui Shu, Hung H. Bui, Shengjia Zhao +2

Variational Rejection Sampling

Aditya Grover, Ramki Gummadi, Miguel Lazaro-Gredilla +2

Closing the Gap Between Short and Long XORs for Model Counting

Shengjia Zhao, Sorathan Chaturapruek, Ashish Sabharwal +1

Uncertainty Autoencoders: Learning Compressed Representations via Variational Information Maximization

Aditya Grover, Stefano Ermon

Shape optimization in laminar flow with a label-guided variational autoencoder

Stephan Eismann, Stefan Bartzsch, Stefano Ermon

Predicting Economic Development using Geolocated Wikipedia Articles

Evan Sheehan, Chenlin Meng, Matthew Tan +5

Constructing Unrestricted Adversarial Examples with Generative Models

Yang Song, Rui Shu, Nate Kushman +1

Towards Deeper Understanding of Variational Autoencoding Models

Shengjia Zhao, Jiaming Song, Stefano Ermon

Audio Super Resolution using Neural Networks

Volodymyr Kuleshov, S. Zayd Enam, Stefano Ermon

Fast Amortized Inference and Learning in Log-linear Models with Randomly Perturbed Nearest Neighbor Search

Stephen Mussmann, Daniel Levy, Stefano Ermon

Adversarial Constraint Learning for Structured Prediction

Hongyu Ren, Russell Stewart, Jiaming Song +2

Modeling Sparse Deviations for Compressed Sensing using Generative Models

Manik Dhar, Aditya Grover, Stefano Ermon

Optimization With Parity Constraints: From Binary Codes to Discrete Integration

Stefano Ermon, Carla P. Gomes, Ashish Sabharwal +1

Playing games against nature: optimal policies for renewable resource allocation

Stefano Ermon, Jon Conrad, Carla P. Gomes +1

Training Variational Autoencoders with Buffered Stochastic Variational Inference

Rui Shu, Hung H. Bui, Jay Whang +1

Flow-GAN: Combining Maximum Likelihood and Adversarial Learning in Generative Models

Aditya Grover, Manik Dhar, Stefano Ermon

Stochastic Optimization of Sorting Networks via Continuous Relaxations

Aditya Grover, Eric Wang, Aaron Zweig +1

Solving Marginal MAP Problems with NP Oracles and Parity Constraints

Yexiang Xue, Zhiyuan Li, Stefano Ermon +2

Distributed generation of privacy preserving data with user customization

Xiao Chen, Thomas Navidi, Stefano Ermon +1

Learning to Interpret Satellite Images Using Wikipedia

Evan Sheehan, Burak Uzkent, Chenlin Meng +4

A DIRT-T Approach to Unsupervised Domain Adaptation

Rui Shu, Hung H. Bui, Hirokazu Narui +1

End-to-End Learning of Motion Representation for Video Understanding

Lijie Fan, Wenbing Huang, Chuang Gan +3

Poverty Prediction with Public Landsat 7 Satellite Imagery and Machine Learning

Anthony Perez, Christopher Yeh, George Azzari +3

Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping

Michael Xie, Neal Jean, Marshall Burke +2

A Survey of Human Activity Recognition Using WiFi CSI

Siamak Yousefi, Hirokazu Narui, Sankalp Dayal +2

Multi-Agent Adversarial Inverse Reinforcement Learning

Lantao Yu, Jiaming Song, Stefano Ermon

The paper introduces MA-AIRL, a scalable framework for multi-agent inverse reinforcement learning that learns reward functions in high-dimensional Markov games using an adversarial…

Accurate Uncertainties for Deep Learning Using Calibrated Regression

Volodymyr Kuleshov, Nathan Fenner, Stefano Ermon

Tile2Vec: Unsupervised representation learning for spatially distributed data

Neal Jean, Sherrie Wang, Anshul Samar +3

The Information Autoencoding Family: A Lagrangian Perspective on Latent Variable Generative Models

Shengjia Zhao, Jiaming Song, Stefano Ermon

InfoGAIL: Interpretable Imitation Learning from Visual Demonstrations

Yunzhu Li, Jiaming Song, Stefano Ermon

Neural Joint Source-Channel Coding

Kristy Choi, Kedar Tatwawadi, Aditya Grover +2

Infrastructure Quality Assessment in Africa using Satellite Imagery and Deep Learning

Barak Oshri, Annie Hu, Peter Adelson +6

Uniform Solution Sampling Using a Constraint Solver As an Oracle

Stefano Ermon, Carla P. Gomes, Bart Selman

Improved Training with Curriculum GANs

Rishi Sharma, Shane Barratt, Stefano Ermon +1

Deterministic Policy Optimization by Combining Pathwise and Score Function Estimators for Discrete Action Spaces

Daniel Levy, Stefano Ermon

PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples

Yang Song, Taesup Kim, Sebastian Nowozin +2

Label-Free Supervision of Neural Networks with Physics and Domain Knowledge

Russell Stewart, Stefano Ermon

Neural Variational Inference and Learning in Undirected Graphical Models

Volodymyr Kuleshov, Stefano Ermon

Estimating Uncertainty Online Against an Adversary

Volodymyr Kuleshov, Stefano Ermon

Graphite: Iterative Generative Modeling of Graphs

Aditya Grover, Aaron Zweig, Stefano Ermon

Boosted Generative Models

Aditya Grover, Stefano Ermon

InfoVAE: Information Maximizing Variational Autoencoders

Shengjia Zhao, Jiaming Song, Stefano Ermon

Generative Adversarial Imitation Learning

Jonathan Ho, Stefano Ermon

Bias and Generalization in Deep Generative Models: An Empirical Study

Shengjia Zhao, Hongyu Ren, Arianna Yuan +3

Tight Variational Bounds via Random Projections and I-Projections

Lun-Kai Hsu, Tudor Achim, Stefano Ermon

Taming the Curse of Dimensionality: Discrete Integration by Hashing and Optimization

Stefano Ermon, Carla P. Gomes, Ashish Sabharwal +1

Learning to Interpret Satellite Images in Global Scale Using Wikipedia

Burak Uzkent, Evan Sheehan, Chenlin Meng +4

The paper introduces WikiSatNet, a dataset that pairs georeferenced Wikipedia articles with satellite images, and uses it to pre‑train models that predict article properties, there…

A-NICE-MC: Adversarial Training for MCMC

Jiaming Song, Shengjia Zhao, Stefano Ermon

Multi-Agent Generative Adversarial Imitation Learning

Jiaming Song, Hongyu Ren, Dorsa Sadigh +1

Accelerating Natural Gradient with Higher-Order Invariance

Yang Song, Jiaming Song, Stefano Ermon

Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data by Minimizing Predictive Variance

Neal Jean, Sang Michael Xie, Stefano Ermon

Approximate Inference via Weighted Rademacher Complexity

Jonathan Kuck, Ashish Sabharwal, Stefano Ermon

On the Limits of Learning Representations with Label-Based Supervision

Jiaming Song, Russell Stewart, Shengjia Zhao +1

Calibrated Model-Based Deep Reinforcement Learning

Ali Malik, Volodymyr Kuleshov, Jiaming Song +3

Semi-Supervised Multitask Learning on Multispectral Satellite Images Using Wasserstein Generative Adversarial Networks (GANs) for Predicting Poverty

Anthony Perez, Swetava Ganguli, Stefano Ermon +3

Model-Free Imitation Learning with Policy Optimization

Jonathan Ho, Jayesh K. Gupta, Stefano Ermon

Best arm identification in multi-armed bandits with delayed feedback

Aditya Grover, Todor Markov, Peter Attia +8