Stefano Ermon
Publication record assembled from the DBLP archive of ranked conferences.
Papers indexed
188
Venues
19
Active years
2010–2025
Best venue rank
A*
Where they publish
Papers
188 indexed papers, newest first.
| Year | Venue | Title | Authors |
|---|---|---|---|
| 2025 | AISTATS | f-PO: Generalizing Preference Optimization with f-divergence Minimization. | Jiaqi Han, Mingjian Jiang, Yuxuan Song, Stefano Ermon, Minkai Xu |
| 2025 | CVPR | Personalized Preference Fine-tuning of Diffusion Models. | Meihua Dang, Anikait Singh, Linqi Zhou, Stefano Ermon, Jiaming Song |
| 2025 | ICCV | CHORDS: Diffusion Sampling Accelerator with Multi-Core Hierarchical ODE Solvers. | Jiaqi Han, Haotian Ye, Puheng Li, Minkai Xu, James Zou, Stefano Ermon |
| 2025 | ICLR | Data Unlearning in Diffusion Models. | Silas Alberti, Kenan Hasanaliyev, Manav Shah, Stefano Ermon |
| 2025 | ICLR | 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 |
| 2025 | ICLR | CPSample: Classifier Protected Sampling for Guarding Training Data During Diffusion. | Joshua Kazdan, Hao Sun, Jiaqi Han, Felix Petersen, Frederick Vu, Stefano Ermon |
| 2025 | ICLR | TFG-Flow: Training-free Guidance in Multimodal Generative Flow. | Haowei Lin, Shanda Li, Haotian Ye, Yiming Yang, Stefano Ermon, Yitao Liang, Jianzhu Ma |
| 2025 | ICLR | TabDiff: a Mixed-type Diffusion Model for Tabular Data Generation. | Juntong Shi, Minkai Xu, Harper Hua, Hengrui Zhang, Stefano Ermon, Jure Leskovec |
| 2025 | ICLR | Energy-Based Diffusion Language Models for Text Generation. | Minkai Xu, Tomas Geffner, Karsten Kreis, Weili Nie, Yilun Xu, Jure Leskovec, Stefano Ermon, Arash Vahdat |
| 2025 | ICML | Zero-Shot Cyclic Peptide Design via Composable Geometric Constraints. | Dapeng Jiang, Xiangzhe Kong, Jiaqi Han, Mingyu Li, Rui Jiao, Wenbing Huang, Stefano Ermon, Jianzhu Ma, Yang Liu |
| 2025 | ICML | ExPLoRA: Parameter-Efficient Extended Pre-Training to Adapt Vision Transformers under Domain Shifts. | Samar Khanna, Medhanie Irgau, David B. Lobell, Stefano Ermon |
| 2025 | ICML | Smooth Interpolation for Improved Discrete Graph Generative Models. | Yuxuan Song, Juntong Shi, Jingjing Gong, Minkai Xu, Stefano Ermon, Hao Zhou, Wei-Ying Ma |
| 2025 | ICML | Scaling Probabilistic Circuits via Monarch Matrices. | Honghua Zhang, Meihua Dang, Benjie Wang, Stefano Ermon, Nanyun Peng, Guy Van den Broeck |
| 2025 | ICML | Inductive Moment Matching. | Linqi Zhou, Stefano Ermon, Jiaming Song |
| 2025 | IJCNN | Improving Vector-Quantized Image Modeling with Latent Consistency-Matching Diffusion. | Bac Nguyen, Chieh-Hsin Lai, Yuhta Takida, Naoki Murata, Toshimitsu Uesaka, Stefano Ermon, Yuki Mitsufuji |
| 2024 | AAAI | Uncertainty Quantification for Forward and Inverse Problems of PDEs via Latent Global Evolution. | Tailin Wu, Willie Neiswanger, Hongtao Zheng, Stefano Ermon, Jure Leskovec |
| 2024 | AAAI | 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 |
| 2024 | ACL | Disentangling Length from Quality in Direct Preference Optimization. | Ryan Park, Rafael Rafailov, Stefano Ermon, Chelsea Finn |
| 2024 | AISTATS | Privacy-Constrained Policies via Mutual Information Regularized Policy Gradients. | Chris Cundy, Rishi Desai, Stefano Ermon |
| 2024 | CVPR | HIVE: Harnessing Human Feedback for Instructional Visual Editing. | Shu Zhang, Xinyi Yang, Yihao Feng, Can Qin, Chia-Chih Chen, Ning Yu, Zeyuan Chen, Huan Wang, Silvio Savarese, Stefano Ermon, Caiming Xiong, Ran Xu |
| 2024 | CVPR | 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 |
| 2024 | CVPR | 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 |
| 2024 | CVPR | 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 |
| 2024 | CVPR | DreamPropeller: Supercharge Text-to-3D Generation with Parallel Sampling. | Linqi Zhou, Andy Shih, Chenlin Meng, Stefano Ermon |
| 2024 | ICLR | Cross-Modal Contextualized Diffusion Models for Text-Guided Visual Generation and Editing. | Ling Yang, Zhilong Zhang, Zhaochen Yu, Jingwei Liu, Minkai Xu, Stefano Ermon, Bin Cui |
| 2024 | ICLR | SequenceMatch: Imitation Learning for Autoregressive Sequence Modelling with Backtracking. | Chris Cundy, Stefano Ermon |
| 2024 | ICLR | Manifold Preserving Guided Diffusion. | Yutong He, Naoki Murata, Chieh-Hsin Lai, Yuhta Takida, Toshimitsu Uesaka, Dongjun Kim, Wei-Hsiang Liao, Yuki Mitsufuji, J. Zico Kolter, Ruslan Salakhutdinov, Stefano Ermon |
| 2024 | ICLR | 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 |
| 2024 | ICLR | Consistency Trajectory Models: Learning Probability Flow ODE Trajectory of Diffusion. | Dongjun Kim, Chieh-Hsin Lai, Wei-Hsiang Liao, Naoki Murata, Yuhta Takida, Toshimitsu Uesaka, Yutong He, Yuki Mitsufuji, Stefano Ermon |
| 2024 | ICLR | GeoLLM: Extracting Geospatial Knowledge from Large Language Models. | Rohin Manvi, Samar Khanna, Gengchen Mai, Marshall Burke, David B. Lobell, Stefano Ermon |
| 2024 | ICLR | Language Model Detectors Are Easily Optimized Against. | Charlotte Nicks, Eric Mitchell, Rafael Rafailov, Archit Sharma, Christopher D. Manning, Chelsea Finn, Stefano Ermon |
| 2024 | ICLR | Denoising Diffusion Bridge Models. | Linqi Zhou, Aaron Lou, Samar Khanna, Stefano Ermon |
| 2024 | ICML | Mastering Text-to-Image Diffusion: Recaptioning, Planning, and Generating with Multimodal LLMs. | Ling Yang, Zhaochen Yu, Chenlin Meng, Minkai Xu, Stefano Ermon, Bin Cui |
| 2024 | ICML | Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution. | Aaron Lou, Chenlin Meng, Stefano Ermon |
| 2024 | ICML | Large Language Models are Geographically Biased. | Rohin Manvi, Samar Khanna, Marshall Burke, David B. Lobell, Stefano Ermon |
| 2024 | ICML | 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 R, Hajime Asama, Stefano Ermon, Taiji Suzuki, Michael Poli, Atsushi Yamashita |
| 2024 | ICML | Mechanistic Design and Scaling of Hybrid Architectures. | Michael Poli, Armin W. Thomas, Eric Nguyen, Pragaash Ponnusamy, Bjrn Deiseroth, Kristian Kersting, Taiji Suzuki, Brian L. Hie, Stefano Ermon, Christopher R, Ce Zhang, Stefano Massaroli |
| 2024 | ICML | Preference Fine-Tuning of LLMs Should Leverage Suboptimal, On-Policy Data. | Fahim Tajwar, Anikait Singh, Archit Sharma, Rafael Rafailov, Jeff Schneider, Tengyang Xie, Stefano Ermon, Chelsea Finn, Aviral Kumar |
| 2024 | ICML | 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 |
| 2023 | AAAI | Offline Imitation Learning with Suboptimal Demonstrations via Relaxed Distribution Matching. | Lantao Yu, Tianhe Yu, Jiaming Song, Willie Neiswanger, Stefano Ermon |
| 2023 | AISTATS | But Are You Sure? An Uncertainty-Aware Perspective on Explainable AI. | Charles Marx, Youngsuk Park, Hilaf Hasson, Yuyang Wang, Stefano Ermon, Luke Huan |
| 2023 | AISTATS | Ideal Abstractions for Decision-Focused Learning. | Michael Poli, Stefano Massaroli, Stefano Ermon, Bryan Wilder, Eric Horvitz |
| 2023 | CVPR | On Distillation of Guided Diffusion Models. | Chenlin Meng, Robin Rombach, Ruiqi Gao, Diederik P. Kingma, Stefano Ermon, Jonathan Ho, Tim Salimans |
| 2023 | ICCV | GlueGen: Plug and Play Multi-modal Encoders for X-to-image Generation. | Can Qin, Ning Yu, Chen Xing, Shu Zhang, Zeyuan Chen, Stefano Ermon, Yun Fu, Caiming Xiong, Ran Xu |
| 2023 | ICCV | End-to-End Diffusion Latent Optimization Improves Classifier Guidance. | Bram Wallace, Akash Gokul, Stefano Ermon, Nikhil Naik |
| 2023 | ICLR | Generative Modeling Helps Weak Supervision (and Vice Versa). | Benedikt Boecking, Nicholas Carl Roberts, Willie Neiswanger, Stefano Ermon, Frederic Sala, Artur Dubrawski |
| 2023 | ICLR | Extreme Q-Learning: MaxEnt RL without Entropy. | Divyansh Garg, Joey Hejna, Matthieu Geist, Stefano Ermon |
| 2023 | ICLR | Understanding and Adopting Rational Behavior by Bellman Score Estimation. | Kuno Kim, Stefano Ermon |
| 2023 | ICLR | Dual Diffusion Implicit Bridges for Image-to-Image Translation. | Xuan Su, Jiaming Song, Chenlin Meng, Stefano Ermon |
| 2023 | ICML | 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 |
| 2023 | ICML | Reflected Diffusion Models. | Aaron Lou, Stefano Ermon |
| 2023 | ICML | CSP: Self-Supervised Contrastive Spatial Pre-Training for Geospatial-Visual Representations. | Gengchen Mai, Ni Lao, Yutong He, Jiaming Song, Stefano Ermon |
| 2023 | ICML | 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 |
| 2023 | ICML | 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 R |
| 2023 | ICML | Long Horizon Temperature Scaling. | Andy Shih, Dorsa Sadigh, Stefano Ermon |
| 2023 | ICML | Geometric Latent Diffusion Models for 3D Molecule Generation. | Minkai Xu, Alexander S. Powers, Ron O. Dror, Stefano Ermon, Jure Leskovec |
| 2023 | ICML | Deep Latent State Space Models for Time-Series Generation. | Linqi Zhou, Michael Poli, Winnie Xu, Stefano Massaroli, Stefano Ermon |
| 2023 | KDD | Graph and Geometry Generative Modeling for Drug Discovery. | Minkai Xu, Meng Liu, Wengong Jin, Shuiwang Ji, Jure Leskovec, Stefano Ermon |
| 2023 | SIGGRAPH | SIGGRAPH 2023 Course on Diffusion Models. | Chenlin Meng, Jiaming Song, Shuang Li, Jun-Yan Zhu, Stefano Ermon, Tsung-Yi Lin, Chen-Hsuan Lin, Karsten Kreis |
| 2022 | AAAI | 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 |
| 2022 | AISTATS | Density Ratio Estimation via Infinitesimal Classification. | Kristy Choi, Chenlin Meng, Yang Song, Stefano Ermon |
| 2022 | CVPR | Efficient Conditional Pre-training for Transfer Learning. | Shuvam Chakraborty, Burak Uzkent, Kumar Ayush, Kumar Tanmay, Evan Sheehan, Stefano Ermon |
| 2022 | HRI | Conditional Imitation Learning for Multi-Agent Games. | Andy Shih, Stefano Ermon, Dorsa Sadigh |
| 2022 | ICLR | Solving Inverse Problems in Medical Imaging with Score-Based Generative Models. | Yang Song, Liyue Shen, Lei Xing, Stefano Ermon |
| 2022 | ICLR | An Experimental Design Perspective on Model-Based Reinforcement Learning. | Viraj Mehta, Biswajit Paria, Jeff Schneider, Stefano Ermon, Willie Neiswanger |
| 2022 | ICLR | SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations. | Chenlin Meng, Yutong He, Yang Song, Jiaming Song, Jiajun Wu, Jun-Yan Zhu, Stefano Ermon |
| 2022 | ICLR | GeoDiff: A Geometric Diffusion Model for Molecular Conformation Generation. | Minkai Xu, Lantao Yu, Yang Song, Chence Shi, Stefano Ermon, Jian Tang |
| 2022 | ICLR | Comparing Distributions by Measuring Differences that Affect Decision Making. | Shengjia Zhao, Abhishek Sinha, Yutong He, Aidan Perreault, Jiaming Song, Stefano Ermon |
| 2022 | ICML | Imitation Learning by Estimating Expertise of Demonstrators. | Mark Beliaev, Andy Shih, Stefano Ermon, Dorsa Sadigh, Ramtin Pedarsani |
| 2022 | ICML | Modular Conformal Calibration. | Charles Marx, Shengjia Zhao, Willie Neiswanger, Stefano Ermon |
| 2022 | ICML | ButterflyFlow: Building Invertible Layers with Butterfly Matrices. | Chenlin Meng, Linqi Zhou, Kristy Choi, Tri Dao, Stefano Ermon |
| 2022 | ICML | Bit Prioritization in Variational Autoencoders via Progressive Coding. | Rui Shu, Stefano Ermon |
| 2022 | ICML | A General Recipe for Likelihood-free Bayesian Optimization. | Jiaming Song, Lantao Yu, Willie Neiswanger, Stefano Ermon |
| 2022 | UAI | Local calibration: metrics and recalibration. | Rachel Luo, Aadyot Bhatnagar, Yu Bai, Shengjia Zhao, Huan Wang, Caiming Xiong, Silvio Savarese, Stefano Ermon, Edward Schmerling, Marco Pavone |
| 2021 | AAAI | Efficient Poverty Mapping from High Resolution Remote Sensing Images. | Kumar Ayush, Burak Uzkent, Kumar Tanmay, Marshall Burke, David B. Lobell, Stefano Ermon |
| 2021 | AAAI | 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 |
| 2021 | AISTATS | Right Decisions from Wrong Predictions: A Mechanism Design Alternative to Individual Calibration. | Shengjia Zhao, Stefano Ermon |
| 2021 | ICCV | Geography-Aware Self-Supervised Learning. | Kumar Ayush, Burak Uzkent, Chenlin Meng, Kumar Tanmay, Marshall Burke, David B. Lobell, Stefano Ermon |
| 2021 | ICLR | Score-Based Generative Modeling through Stochastic Differential Equations. | Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole |
| 2021 | ICLR | Improved Autoregressive Modeling with Distribution Smoothing. | Chenlin Meng, Jiaming Song, Yang Song, Shengjia Zhao, Stefano Ermon |
| 2021 | ICLR | On the Critical Role of Conventions in Adaptive Human-AI Collaboration. | Andy Shih, Arjun Sawhney, Jovana Kondic, Stefano Ermon, Dorsa Sadigh |
| 2021 | ICLR | Negative Data Augmentation. | Abhishek Sinha, Kumar Ayush, Jiaming Song, Burak Uzkent, Hongxia Jin, Stefano Ermon |
| 2021 | ICLR | Denoising Diffusion Implicit Models. | Jiaming Song, Chenlin Meng, Stefano Ermon |
| 2021 | ICLR | Anytime Sampling for Autoregressive Models via Ordered Autoencoding. | Yilun Xu, Yang Song, Sahaj Garg, Linyuan Gong, Rui Shu, Aditya Grover, Stefano Ermon |
| 2021 | ICLR | Evaluating the Disentanglement of Deep Generative Models through Manifold Topology. | Sharon Zhou, Eric Zelikman, Fred Lu, Andrew Y. Ng, Gunnar E. Carlsson, Stefano Ermon |
| 2021 | ICML | Reward Identification in Inverse Reinforcement Learning. | Kuno Kim, Shivam Garg, Kirankumar Shiragur, Stefano Ermon |
| 2021 | ICML | Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mutual Information. | Willie Neiswanger, Ke Alexander Wang, Stefano Ermon |
| 2021 | ICML | Temporal Predictive Coding For Model-Based Planning In Latent Space. | Tung D. Nguyen, Rui Shu, Tuan Pham, Hung Bui, Stefano Ermon |
| 2021 | ICML | Accelerating Feedforward Computation via Parallel Nonlinear Equation Solving. | Yang Song, Chenlin Meng, Renjie Liao, Stefano Ermon |
| 2021 | KDD | Challenges in KDD and ML for Sustainable Development. | Laure Berti-quille, David Dao, Stefano Ermon, Bedharta Goswami |
| 2021 | UAI | Featurized density ratio estimation. | Kristy Choi, Madeline Liao, Stefano Ermon |
| 2020 | AAAI | AlignFlow: Cycle Consistent Learning from Multiple Domains via Normalizing Flows. | Aditya Grover, Christopher Chute, Rui Shu, Zhangjie Cao, Stefano Ermon |
| 2020 | AAAI | Meta-Amortized Variational Inference and Learning. | Mike Wu, Kristy Choi, Noah D. Goodman, Stefano Ermon |
| 2020 | AISTATS | Gaussianization Flows. | Chenlin Meng, Yang Song, Jiaming Song, Stefano Ermon |
| 2020 | AISTATS | Permutation Invariant Graph Generation via Score-Based Generative Modeling. | Chenhao Niu, Yang Song, Jiaming Song, Shengjia Zhao, Aditya Grover, Stefano Ermon |
| 2020 | AISTATS | A Framework for Sample Efficient Interval Estimation with Control Variates. | Shengjia Zhao, Christopher Yeh, Stefano Ermon |
| 2020 | CVPR | Farm Parcel Delineation Using Spatio-temporal Convolutional Networks. | Han Lin Aung, Burak Uzkent, Marshall Burke, David B. Lobell, Stefano Ermon |
| 2020 | CVPR | Learning When and Where to Zoom With Deep Reinforcement Learning. | Burak Uzkent, Stefano Ermon |
| 2020 | ICLR | Weakly Supervised Disentanglement with Guarantees. | Rui Shu, Yining Chen, Abhishek Kumar, Stefano Ermon, Ben Poole |
| 2020 | ICLR | Understanding the Limitations of Variational Mutual Information Estimators. | Jiaming Song, Stefano Ermon |
| 2020 | ICLR | A Theory of Usable Information under Computational Constraints. | Yilun Xu, Shengjia Zhao, Jiaming Song, Russell Stewart, Stefano Ermon |
| 2020 | ICML | Fair Generative Modeling via Weak Supervision. | Kristy Choi, Aditya Grover, Trisha Singh, Rui Shu, Stefano Ermon |
| 2020 | ICML | Domain Adaptive Imitation Learning. | Kuno Kim, Yihong Gu, Jiaming Song, Shengjia Zhao, Stefano Ermon |
| 2020 | ICML | Predictive Coding for Locally-Linear Control. | Rui Shu, Tung Nguyen, Yinlam Chow, Tuan Pham, Khoat Than, Mohammad Ghavamzadeh, Stefano Ermon, Hung H. Bui |
| 2020 | ICML | Bridging the Gap Between f-GANs and Wasserstein GANs. | Jiaming Song, Stefano Ermon |
| 2020 | ICML | Training Deep Energy-Based Models with f-Divergence Minimization. | Lantao Yu, Yang Song, Jiaming Song, Stefano Ermon |
| 2020 | ICML | Individual Calibration with Randomized Forecasting. | Shengjia Zhao, Tengyu Ma, Stefano Ermon |
| 2020 | IJCAI | Generating Interpretable Poverty Maps using Object Detection in Satellite Images. | Kumar Ayush, Burak Uzkent, Marshall Burke, David B. Lobell, Stefano Ermon |
| 2020 | WACV | Cloud Removal in Satellite Images Using Spatiotemporal Generative Networks. | Vishnu Sarukkai, Anirudh Jain, Burak Uzkent, Stefano Ermon |
| 2020 | WACV | Efficient Object Detection in Large Images Using Deep Reinforcement Learning. | Burak Uzkent, Christopher Yeh, Stefano Ermon |
| 2020 | UAI | Flexible Approximate Inference via Stratified Normalizing Flows. | Chris Cundy, Stefano Ermon |
| 2019 | AAAI | Tile2Vec: Unsupervised Representation Learning for Spatially Distributed Data. | Neal Jean, Sherrie Wang, Anshul Samar, George Azzari, David B. Lobell, Stefano Ermon |
| 2019 | AAAI | InfoVAE: Balancing Learning and Inference in Variational Autoencoders. | Shengjia Zhao, Jiaming Song, Stefano Ermon |
| 2019 | AIES | 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 |
| 2019 | AISTATS | Uncertainty Autoencoders: Learning Compressed Representations via Variational Information Maximization. | Aditya Grover, Stefano Ermon |
| 2019 | AISTATS | Training Variational Autoencoders with Buffered Stochastic Variational Inference. | Rui Shu, Hung H. Bui, Jay Whang, Stefano Ermon |
| 2019 | AISTATS | Learning Controllable Fair Representations. | Jiaming Song, Pratyusha Kalluri, Aditya Grover, Shengjia Zhao, Stefano Ermon |
| 2019 | AISTATS | Differentiable Antithetic Sampling for Variance Reduction in Stochastic Variational Inference. | Mike Wu, Noah D. Goodman, Stefano Ermon |
| 2019 | CVPR | 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 |
| 2019 | ICLR | AlignFlow: Learning from multiple domains via normalizing flows. | Aditya Grover, Christopher Chute, Rui Shu, Zhangjie Cao, Stefano Ermon |
| 2019 | ICLR | 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 |
| 2019 | ICLR | Stochastic Optimization of Sorting Networks via Continuous Relaxations. | Aditya Grover, Eric Wang, Aaron Zweig, Stefano Ermon |
| 2019 | ICLR | Learning Neural PDE Solvers with Convergence Guarantees. | Jun-Ting Hsieh, Shengjia Zhao, Stephan Eismann, Lucia Mirabella, Stefano Ermon |
| 2019 | ICML | Neural Joint Source-Channel Coding. | Kristy Choi, Kedar Tatwawadi, Aditya Grover, Tsachy Weissman, Stefano Ermon |
| 2019 | ICML | Graphite: Iterative Generative Modeling of Graphs. | Aditya Grover, Aaron Zweig, Stefano Ermon |
| 2019 | ICML | Calibrated Model-Based Deep Reinforcement Learning. | Ali Malik, Volodymyr Kuleshov, Jiaming Song, Danny Nemer, Harlan Seymour, Stefano Ermon |
| 2019 | ICML | Adaptive Antithetic Sampling for Variance Reduction. | Hongyu Ren, Shengjia Zhao, Stefano Ermon |
| 2019 | ICML | Multi-Agent Adversarial Inverse Reinforcement Learning. | Lantao Yu, Jiaming Song, Stefano Ermon |
| 2019 | IJCAI | Learning to Interpret Satellite Images using Wikipedia. | Burak Uzkent, Evan Sheehan, Chenlin Meng, Zhongyi Tang, Marshall Burke, David B. Lobell, Stefano Ermon |
| 2019 | IJCAI | Reparameterizable Subset Sampling via Continuous Relaxations. | Sang Michael Xie, Stefano Ermon |
| 2019 | KDD | Predicting Economic Development using Geolocated Wikipedia Articles. | Evan Sheehan, Chenlin Meng, Matthew Tan, Burak Uzkent, Neal Jean, Marshall Burke, David B. Lobell, Stefano Ermon |
| 2019 | UAI | Adaptive Hashing for Model Counting. | Jonathan Kuck, Tri Dao, Shenjia Zhao, Burak Bartan, Ashish Sabharwal, Stefano Ermon |
| 2019 | UAI | Sliced Score Matching: A Scalable Approach to Density and Score Estimation. | Yang Song, Sahaj Garg, Jiaxin Shi, Stefano Ermon |
| 2018 | AAAI | Flow-GAN: Combining Maximum Likelihood and Adversarial Learning in Generative Models. | Aditya Grover, Manik Dhar, Stefano Ermon |
| 2018 | AAAI | Boosted Generative Models. | Aditya Grover, Stefano Ermon |
| 2018 | AAAI | Approximate Inference via Weighted Rademacher Complexity. | Jonathan Kuck, Ashish Sabharwal, Stefano Ermon |
| 2018 | AAAI | Deterministic Policy Optimization by Combining Pathwise and Score Function Estimators for Discrete Action Spaces. | Daniel Levy, Stefano Ermon |
| 2018 | AISTATS | Variational Rejection Sampling. | Aditya Grover, Ramki Gummadi, Miguel Lzaro-Gredilla, Dale Schuurmans, Stefano Ermon |
| 2018 | AISTATS | 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 |
| 2018 | CVPR | End-to-End Learning of Motion Representation for Video Understanding. | Lijie Fan, Wen-bing Huang, Chuang Gan, Stefano Ermon, Boqing Gong, Junzhou Huang |
| 2018 | ICLR | A DIRT-T Approach to Unsupervised Domain Adaptation. | Rui Shu, Hung H. Bui, Hirokazu Narui, Stefano Ermon |
| 2018 | ICLR | PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples. | Yang Song, Taesup Kim, Sebastian Nowozin, Stefano Ermon, Nate Kushman |
| 2018 | ICLR | Multi-Agent Generative Adversarial Imitation Learning. | Jiaming Song, Hongyu Ren, Dorsa Sadigh, Stefano Ermon |
| 2018 | ICML | Modeling Sparse Deviations for Compressed Sensing using Generative Models. | Manik Dhar, Aditya Grover, Stefano Ermon |
| 2018 | ICML | Accurate Uncertainties for Deep Learning Using Calibrated Regression. | Volodymyr Kuleshov, Nathan Fenner, Stefano Ermon |
| 2018 | ICML | Accelerating Natural Gradient with Higher-Order Invariance. | Yang Song, Jiaming Song, Stefano Ermon |
| 2018 | IJCAI | Adversarial Constraint Learning for Structured Prediction. | Hongyu Ren, Russell Stewart, Jiaming Song, Volodymyr Kuleshov, Stefano Ermon |
| 2018 | KDD | Infrastructure Quality Assessment in Africa using Satellite Imagery and Deep Learning. | Barak Oshri, Annie Hu, Peter Adelson, Xiao Chen, Pascaline Dupas, Jeremy Weinstein, Marshall Burke, David B. Lobell, Stefano Ermon |
| 2018 | UAI | Bayesian optimization and attribute adjustment. | Stephan Eismann, Daniel Levy, Rui Shu, Stefan Bartzsch, Stefano Ermon |
| 2018 | UAI | A Lagrangian Perspective on Latent Variable Generative Models. | Shengjia Zhao, Jiaming Song, Stefano Ermon |
| 2017 | AAAI | Estimating Uncertainty Online Against an Adversary. | Volodymyr Kuleshov, Stefano Ermon |
| 2017 | AAAI | Label-Free Supervision of Neural Networks with Physics and Domain Knowledge. | Russell Stewart, Stefano Ermon |
| 2017 | AAAI | General Bounds on Satisfiability Thresholds for Random CSPs via Fourier Analysis. | Colin Wei, Stefano Ermon |
| 2017 | AAAI | Deep Gaussian Process for Crop Yield Prediction Based on Remote Sensing Data. | Jiaxuan You, Xiaocheng Li, Melvin Low, David B. Lobell, Stefano Ermon |
| 2017 | CVPR | Monitoring Ethiopian Wheat Fungus with Satellite Imagery and Deep Feature Learning. | Reid Pryzant, Stefano Ermon, David B. Lobell |
| 2017 | ICLR | Audio Super-Resolution using Neural Networks. | Volodymyr Kuleshov, S. Zayd Enam, Stefano Ermon |
| 2017 | ICLR | Generative Adversarial Learning of Markov Chains. | Jiaming Song, Shengjia Zhao, Stefano Ermon |
| 2017 | ICML | Learning Hierarchical Features from Deep Generative Models. | Shengjia Zhao, Jiaming Song, Stefano Ermon |
| 2017 | UAI | Hybrid Deep Discriminative/Generative Models for Semi-Supervised Learning. | Volodymyr Kuleshov, Stefano Ermon |
| 2017 | UAI | Fast Amortized Inference and Learning in Log-linear Models with Randomly Perturbed Nearest Neighbor Search. | Stephen Mussmann, Daniel Levy, Stefano Ermon |
| 2017 | SCOPES | Stencil Autotuning with Ordinal Regression: Extended Abstract. | Biagio Cosenza, Juan Jos Durillo, Stefano Ermon, Ben H. H. Juurlink |
| 2016 | AAAI | Exact Sampling with Integer Linear Programs and Random Perturbations. | Carolyn Kim, Ashish Sabharwal, Stefano Ermon |
| 2016 | AAAI | Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping. | Sang Michael Xie, Neal Jean, Marshall Burke, David B. Lobell, Stefano Ermon |
| 2016 | AAAI | Closing the Gap Between Short and Long XORs for Model Counting. | Shengjia Zhao, Sorathan Chaturapruek, Ashish Sabharwal, Stefano Ermon |
| 2016 | AISTATS | Tight Variational Bounds via Random Projections and I-Projections. | Lun-Kai Hsu, Tudor Achim, Stefano Ermon |
| 2016 | ICML | Beyond Parity Constraints: Fourier Analysis of Hash Functions for Inference. | Tudor Achim, Ashish Sabharwal, Stefano Ermon |
| 2016 | ICML | Model-Free Imitation Learning with Policy Optimization. | Jonathan Ho, Jayesh K. Gupta, Stefano Ermon |
| 2016 | ICML | Learning and Inference via Maximum Inner Product Search. | Stephen Mussmann, Stefano Ermon |
| 2016 | ICML | Variable Elimination in the Fourier Domain. | Yexiang Xue, Stefano Ermon, Ronan Le Bras, Carla P. Gomes, Bart Selman |
| 2016 | UAI | Sparse Gaussian Processes for Bayesian Optimization. | Mitchell McIntire, Daniel Ratner, Stefano Ermon |
| 2015 | AAAI | Pattern Decomposition with Complex Combinatorial Constraints: Application to Materials Discovery. | Stefano Ermon, Ronan Le Bras, Santosh K. Suram, John M. Gregoire, Carla P. Gomes, Bart Selman, Robert Bruce van Dover |
| 2015 | AAAI | 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 |
| 2015 | AAAI | Uncovering Hidden Structure through Parallel Problem Decomposition for the Set Basis Problem. | Yexiang Xue, Stefano Ermon, Carla P. Gomes, Bart Selman |
| 2015 | ICML | A Hybrid Approach for Probabilistic Inference using Random Projections. | Michael Zhu, Stefano Ermon |
| 2015 | IJCAI | 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 |
| 2015 | UAI | Importance Sampling over Sets: A New Probabilistic Inference Scheme. | Stefan Hadjis, Stefano Ermon |
| 2014 | AAAI | Designing Fast Absorbing Markov Chains. | Stefano Ermon, Carla P. Gomes, Ashish Sabharwal, Bart Selman |
| 2014 | AAAI | Uncovering Hidden Structure through Parallel Problem Decomposition. | Yexiang Xue, Stefano Ermon, Carla P. Gomes, Bart Selman |
| 2014 | ICML | Low-density Parity Constraints for Hashing-Based Discrete Integration. | Stefano Ermon, Carla P. Gomes, Ashish Sabharwal, Bart Selman |
| 2013 | ICML | Taming the Curse of Dimensionality: Discrete Integration by Hashing and Optimization. | Stefano Ermon, Carla P. Gomes, Ashish Sabharwal, Bart Selman |
| 2013 | UAI | Optimization With Parity Constraints: From Binary Codes to Discrete Integration. | Stefano Ermon, Carla P. Gomes, Ashish Sabharwal, Bart Selman |
| 2012 | AAMAS | Probabilistic planning with non-linear utility functions and worst-case guarantees. | Stefano Ermon, Carla P. Gomes, Bart Selman, Alexander Vladimirsky |
| 2012 | UAI | Uniform Solution Sampling Using a Constraint Solver As an Oracle. | Stefano Ermon, Carla P. Gomes, Bart Selman |
| 2012 | SAT | SMT-Aided Combinatorial Materials Discovery. | Stefano Ermon, Ronan LeBras, Carla P. Gomes, Bart Selman, R. Bruce van Dover |
| 2011 | IJCAI | Risk-Sensitive Policies for Sustainable Renewable Resource Allocation. | Stefano Ermon, Jon Conrad, Carla P. Gomes, Bart Selman |
| 2011 | IJCAI | A Flat Histogram Method for Computing the Density of States of Combinatorial Problems. | Stefano Ermon, Carla P. Gomes, Bart Selman |
| 2010 | CP | Computing the Density of States of Boolean Formulas. | Stefano Ermon, Carla P. Gomes, Bart Selman |
| 2010 | UAI | Playing games against nature: optimal policies for renewable resource allocation. | Stefano Ermon, Jon Conrad, Carla P. Gomes, Bart Selman |