Direct Loss Minimization for Training Deep Neural Nets. - Yang Song 011; Alexander G. Schwing; Richard S. Zemel; Raquel Urtasun (CoRR, 2015)

Bayesian Matrix Completion via Adaptive Relaxed Spectral Regularization. - Yang Song 011; Jun Zhu 001 (AAAI, 2016)

Training Deep Neural Networks via Direct Loss Minimization. - Yang Song 011; Alexander G. Schwing; Richard S. Zemel; Raquel Urtasun (ICML, 2016)

Stochastic Gradient Geodesic MCMC Methods. - Chang Liu 030; Jun Zhu 001; Yang Song 011 (NIPS, 2016)

Kernel Bayesian Inference with Posterior Regularization. - Yang Song 011; Jun Zhu 001; Yong Ren (NIPS, 2016)

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)

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

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

Efficient Graph Generation with Graph Recurrent Attention Networks. - Renjie Liao; Yujia Li 001; Yang Song 011; Shenlong Wang; William L. Hamilton; David Duvenaud; Raquel Urtasun; Richard S. Zemel (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)

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

Efficient Graph Generation with Graph Recurrent Attention Networks. - Renjie Liao; Yujia Li 001; Yang Song 011; Shenlong Wang; Charlie Nash; William L. Hamilton; David Duvenaud; Raquel Urtasun; Richard S. Zemel (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)

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

Improved Techniques for Training Score-Based Generative Models. - Yang Song 011; 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)

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

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)

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

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)

Learning Energy-Based Models by Diffusion Recovery Likelihood. - Ruiqi Gao; Yang Song 011; Ben Poole; Ying Nian Wu; Diederik P. Kingma (ICLR, 2021)

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

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

Maximum Likelihood Training of Score-Based Diffusion Models. - Yang Song 011; Conor Durkan; Iain Murray 001; 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)

How to Train Your Energy-Based Models. - Yang Song 011; Diederik P. Kingma (CoRR, 2021)

On Maximum Likelihood Training of Score-Based Generative Models. - Conor Durkan; Yang Song 011 (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)

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

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

Diffusion Models: A Comprehensive Survey of Methods and Applications. - Ling Yang 006; Zhilong Zhang; Yang Song 011; Shenda Hong; Runsheng Xu; Yue Zhao 016; Yingxia Shao; Wentao Zhang 001; Ming-Hsuan Yang 001; Bin Cui 001 (CoRR, 2022)

Diffusion-Based Scene Graph to Image Generation with Masked Contrastive Pre-Training. - Ling Yang 006; Zhilin Huang; Yang Song 011; Shenda Hong; Guohao Li 001; Wentao Zhang 001; Bin Cui 001; Bernard Ghanem; Ming-Hsuan Yang 001 (CoRR, 2022)

Diffusion Models: A Comprehensive Survey of Methods and Applications. - Ling Yang 006; Zhilong Zhang; Yang Song 011; Shenda Hong; Runsheng Xu; Yue Zhao 016; Wentao Zhang 001; Bin Cui 001; Ming-Hsuan Yang 001 (ACM Comput. Surv., 2024)

Improving Diffusion Inverse Problem Solving with Decoupled Noise Annealing. - Bingliang Zhang; Wenda Chu; Julius Berner; Chenlin Meng; Anima Anandkumar; Yang Song 011 (CVPR, 2025)