Discriminative Nonparametric Latent Feature Relational Models with Data Augmentation. - Bei Chen; Ning Chen 002; Jun Zhu 001; Jiaming Song; Bo Zhang 010 (AAAI, 2016)

Online-offline consistency exploration based on the alternating direction method of multipliers. - Tianqi Lv; Jiaming Song; Xiaojuan Wang; Nianhao Liu; Mo Chen (ICCSE, 2016)

Factored Temporal Sigmoid Belief Networks for Sequence Learning. - Jiaming Song; Zhe Gan; Lawrence Carin (ICML, 2016)

Max-Margin Nonparametric Latent Feature Models for Link Prediction. - Jun Zhu 001; Jiaming Song; Bei Chen (CoRR, 2016)

SPARQL Query Parallel Processing: A Survey. - Jiaying Feng; Chenhong Meng; Jiaming Song; Xiaowang Zhang; Zhiyong Feng 002; Lei Zou 001 (BigData Congress, 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)

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)

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)

Char-Level Neural Network for Network Anomaly Behavior Detection. - Sheng Wang; Jiaming Song; Ruixu Guo (HCC, 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)

Multi-Agent Generative Adversarial Imitation Learning. - Jiaming Song; Hongyu Ren; Dorsa Sadigh; 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)

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

MapSQ: A Plugin-based MapReduce Framework for SPARQL Queries on GPU. - Jiaming Song; Xiaowang Zhang; Peng Peng 001; Zhiyong Feng 002; Lei Zou 001 (WWW (Companion Volume), 2018)

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

An Empirical Analysis of Proximal Policy Optimization with Kronecker-factored Natural Gradients. - Jiaming Song; Yuhuai Wu (CoRR, 2018)

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

gSMat: A Scalable Sparse Matrix-based Join for SPARQL Query Processing. - Xiaowang Zhang; Mingyue Zhang; Peng Peng 001; Jiaming Song; Zhiyong Feng 002; Lei Zou 001 (CoRR, 2018)

Malicious behaviour classification in web logs based on an improved Xgboost algorithm. - Jiaming Song; Xiaojuan Wang; Lei Jin; Jingwen You (Int. J. Web Eng. Technol., 2018)

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

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

A New Scheduling Model for Tire Production and Transportation Among Distributed Factories. - Jiaming Song; Xiaobo Song; Xiaoli Wang (CIS, 2019)

A Scalable Sparse Matrix-Based Join for SPARQL Query Processing. - Xiaowang Zhang; Mingyue Zhang; Peng Peng 001; Jiaming Song; Zhiyong Feng 002; Lei Zou 001 (DASFAA (3), 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)

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

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

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)

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)

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

Robust and On-the-Fly Dataset Denoising for Image Classification. - Jiaming Song; Yann N. Dauphin; Michael Auli; Tengyu Ma 001 (ECCV (29), 2020)

DOA Estimation for Arbitrarily Distributed Subarrays in UAV Swarm. - Dian Fan 001; Gan Guo; Jiaming Song; Lanfei Li; Yue Zhu (ICCC, 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)

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

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)

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

Robust and On-the-fly Dataset Denoising for Image Classification. - Jiaming Song; Lunjia Hu; Yann N. Dauphin; Michael Auli; Tengyu Ma 001 (CoRR, 2020)

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

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

Variational Automatic Curriculum Learning for Sparse-Reward Cooperative Multi-Agent Problems. - Jiayu Chen 005; Yuanxin Zhang; Yuanfan Xu; Huimin Ma 001; Huazhong Yang; Jiaming Song; Yu Wang 002; Yi Wu 013 (NeurIPS, 2021)

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

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

D2C: Diffusion-Decoding Models for Few-Shot Conditional Generation. - Abhishek Sinha; Jiaming Song; Chenlin Meng; 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)

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

Multi-Installment Scheduling for Large-Scale Workload Computation with Result Retrieval. - Xiaoli Wang 001; Bharadwaj Veeravalli; Jiaming Song (Neurocomputing, 2021)

Compression, generation, and inference via supervised learning. - Jiaming Song (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)

BigDL 2.0: Seamless Scaling of AI Pipelines from Laptops to Distributed Cluster. - Jason Jinquan Dai; Ding Ding; Dongjie Shi; Shengsheng Huang; Jiao Wang; Xin Qiu 006; Kai Huang; Guoqiong Song; Yang Wang; Qiyuan Gong; Jiaming Song; Shan Yu; et al. (CVPR, 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)

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

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)

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

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

eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers. - Yogesh Balaji; Seungjun Nah; Xun Huang; Arash Vahdat; Jiaming Song; Karsten Kreis; Miika Aittala; Timo Aila; Samuli Laine; Bryan Catanzaro; Tero Karras; Ming-Yu Liu 001 (CoRR, 2022)

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

Affordance Diffusion: Synthesizing Hand-Object Interactions. - Yufei Ye; Xueting Li; Abhinav Gupta 001; Shalini De Mello; Stan Birchfield; Jiaming Song; Shubham Tulsiani; Sifei Liu (CVPR, 2023)

DiffCollage: Parallel Generation of Large Content with Diffusion Models. - Qinsheng Zhang; Jiaming Song; Xun Huang; Yongxin Chen; Ming-Yu Liu 001 (CVPR, 2023)

PhysDiff: Physics-Guided Human Motion Diffusion Model. - Ye Yuan 007; Jiaming Song; Umar Iqbal 001; Arash Vahdat; Jan Kautz (ICCV, 2023)

Pseudoinverse-Guided Diffusion Models for Inverse Problems. - Jiaming Song; Arash Vahdat; Morteza Mardani; Jan Kautz (ICLR, 2023)

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

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

Loss-Guided Diffusion Models for Plug-and-Play Controllable Generation. - Jiaming Song; Qinsheng Zhang; Hongxu Yin; Morteza Mardani; Ming-Yu Liu 001; Jan Kautz; Yongxin Chen; Arash Vahdat (ICML, 2023)

SMRD: SURE-Based Robust MRI Reconstruction with Diffusion Models. - Batu Ozturkler; Chao Liu 064; Benjamin Eckart; Morteza Mardani; Jiaming Song; Jan Kautz (MICCAI (3), 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)

Score-based Diffusion Models in Function Space. - Jae Hyun Lim 001; Nikola B. Kovachki; Ricardo Baptista; Christopher Beckham; Kamyar Azizzadenesheli; Jean Kossaifi; Vikram Voleti; Jiaming Song; Karsten Kreis; Jan Kautz; Christopher Pal; Arash Vahdat; et al. (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)

Improved Order Analysis and Design of Exponential Integrator for Diffusion Models Sampling. - Qinsheng Zhang; Jiaming Song; Yongxin Chen (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)

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)

HUSCH: an integrated single-cell transcriptome atlas for human tissue gene expression visualization and analyses. - Xiaoying Shi; Zhiguang Yu; Pengfei Ren; Xin Dong; Xuanxin Ding; Jiaming Song; Jing Zhang; Taiwen Li; Chenfei Wang (Nucleic Acids Res., 2023)

CSK-CNN: Network Intrusion Detection Model Based on Two-Layer Convolution Neural Network for Handling Imbalanced Dataset. - Jiaming Song; Xiaojuan Wang; Mingshu He; Lei Jin 003 (Inf., 2023)

On the Design and Evaluation of an Optimal Security-and-Time Cognizant Data Placement for Dynamic Fog Environments. - Xiaoli Wang 001; Bharadwaj Veeravalli; Jiaming Song; Honghu Liu (IEEE Trans. Parallel Distributed Syst., 2023)

Accelerate Multi-Agent Reinforcement Learning in Zero-Sum Games with Subgame Curriculum Learning. - Jiayu Chen 005; Zelai Xu; Yunfei Li 005; Chao Yu 005; Jiaming Song; Huazhong Yang; Fei Fang 001; Yu Wang 002; Yi Wu 013 (AAAI, 2024)

DiffiT: Diffusion Vision Transformers for Image Generation. - Ali Hatamizadeh; Jiaming Song; Guilin Liu; Jan Kautz; Arash Vahdat (ECCV (8), 2024)

Seer: Language Instructed Video Prediction with Latent Diffusion Models. - Xianfan Gu; Chuan Wen; Weirui Ye; Jiaming Song; Yang Gao 029 (ICLR, 2024)

A Variational Perspective on Solving Inverse Problems with Diffusion Models. - Morteza Mardani; Jiaming Song; Jan Kautz; Arash Vahdat (ICLR, 2024)

Edify Image: High-Quality Image Generation with Pixel Space Laplacian Diffusion Models. - Yuval Atzmon; Maciej Bala; Yogesh Balaji; Tiffany Cai; Yin Cui; Jiaojiao Fan; Yunhao Ge; Siddharth Gururani; Jacob Samuel Huffman; Ronald Isaac; Pooya Jannaty; Tero Karras; et al. (CoRR, 2024)

LPI-based resource allocation strategy for multiple targets tracking in CMIMO radar system with array division. - Jiaming Song; Ting Cheng 001; Yumeng Wang 002; Luqing Liu; Zishu He (Signal Process., 2024)

AGG: Amortized Generative 3D Gaussians for Single Image to 3D. - Dejia Xu; Ye Yuan 012; Morteza Mardani; Sifei Liu; Jiaming Song; Zhangyang Wang; Arash Vahdat (Trans. Mach. Learn. Res., 2024)

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

Decentralized Diffusion Models. - David McAllister; Matthew Tancik; Jiaming Song; Angjoo Kanazawa (CVPR, 2025)

Ideas in Inference-time Scaling can Benefit Generative Pre-training Algorithms. - Jiaming Song; Linqi Zhou (CoRR, 2025)

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

Self-NPO: Negative Preference Optimization of Diffusion Models by Simply Learning from Itself without Explicit Preference Annotations. - Fu-Yun Wang; Keqiang Sun; Yao Teng; Xihui Liu; Jiaming Song; Hongsheng Li (CoRR, 2025)

SABER: Switchable and Balanced Training for Efficient LLM Reasoning. - Kai Zhao; Yanjun Zhao; Jiaming Song; Shien He; Lusheng Zhang; Qiang Zhang; Tianjiao Li (CoRR, 2025)