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ORCIDStefano Ermon
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- affiliation: Stanford University
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2020 – today
- 2025
[j17]Xuan Zhang, Limei Wang, Jacob Helwig, Youzhi Luo, Cong Fu, Yaochen Xie, Meng Liu, Yuchao Lin, Zhao Xu, Keqiang Yan, Keir Adams, Maurice Weiler, Xiner Li, Tianfan Fu, Yucheng Wang, Alex Strasser, Haiyang Yu, Yuqing Xie, Xiang Fu, Shenglong Xu, Yi Liu, Yuanqi Du, Alexandra Saxton, Hongyi Ling, Hannah Lawrence, Hannes Stärk, Shurui Gui, Carl Edwards, Nicholas Gao, Adriana Ladera, Tailin Wu, Elyssa F. Hofgard, Aria Mansouri Tehrani, Rui Wang, Ameya Daigavane, Montgomery Bohde
, Jerry Kurtin, Qian Huang, Tuong Phung, Minkai Xu, Chaitanya K. Joshi, Simon V. Mathis, Kamyar Azizzadenesheli, Ada Fang, Alán Aspuru-Guzik, Erik J. Bekkers, Michael M. Bronstein, Marinka Zitnik, Anima Anandkumar, Stefano Ermon, Pietro Liò, Rose Yu, Stephan Günnemann, Jure Leskovec, Heng Ji, Jimeng Sun, Regina Barzilay, Tommi S. Jaakkola, Connor W. Coley, Xiaoning Qian, Xiaofeng Qian, Tess E. Smidt, Shuiwang Ji:
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems. Found. Trends Mach. Learn. 18(4): 385-912 (2025)- [j16]Zhuo Zheng, Stefano Ermon, Dongjun Kim, Liangpei Zhang, Yanfei Zhong:
Changen2: Multi-Temporal Remote Sensing Generative Change Foundation Model. IEEE Trans. Pattern Anal. Mach. Intell. 47(2): 725-741 (2025) - [j15]Syrine Belakaria, Alaleh Ahmadianshalchi, Barbara E. Engelhardt, Stefano Ermon, Jana Doppa:
Non-Myopic Multi-Objective Bayesian Optimization. Trans. Mach. Learn. Res. 2025 (2025) - [j14]Charles Marx, Volodymyr Kuleshov, Stefano Ermon:
Calibrated Probabilistic Forecasts for Arbitrary Sequences. Trans. Mach. Learn. Res. 2025 (2025) - [j13]Naoki Murata, Chieh-Hsin Lai, Yuhta Takida, Toshimitsu Uesaka, Bac Nguyen, Stefano Ermon, Yuki Mitsufuji:
G2D2: Gradient-Guided Discrete Diffusion for Inverse Problem Solving. Trans. Mach. Learn. Res. 2025 (2025) - [c283]Jiaqi Han, Mingjian Jiang, Yuxuan Song, Stefano Ermon, Minkai Xu:
f-PO: Generalizing Preference Optimization with f-divergence Minimization. AISTATS 2025: 1144-1152 - [c282]Meihua Dang, Anikait Singh, Linqi Zhou, Stefano Ermon, Jiaming Song:
Personalized Preference Fine-tuning of Diffusion Models. CVPR 2025: 8020-8030 - [c281]Silas Alberti, Kenan Hasanaliyev, Manav Shah, Stefano Ermon:
Data Unlearning in Diffusion Models. ICLR 2025 - [c280]Jeremy Andrew Irvin, Emily Ruoyu Liu, Joyce Chuyi Chen, Ines Dormoy, Jinyoung Kim, Samar Khanna, Zhuo Zheng, Stefano Ermon:
TEOChat: A Large Vision-Language Assistant for Temporal Earth Observation Data. ICLR 2025 - [c279]Joshua Kazdan, Hao Sun, Jiaqi Han, Felix Petersen, Frederick Vu, Stefano Ermon:
CPSample: Classifier Protected Sampling for Guarding Training Data During Diffusion. ICLR 2025 - [c278]Haowei Lin, Shanda Li, Haotian Ye, Yiming Yang, Stefano Ermon, Yitao Liang, Jianzhu Ma:
TFG-Flow: Training-free Guidance in Multimodal Generative Flow. ICLR 2025 - [c277]Juntong Shi, Minkai Xu, Harper Hua, Hengrui Zhang, Stefano Ermon, Jure Leskovec:
TabDiff: a Mixed-type Diffusion Model for Tabular Data Generation. ICLR 2025 - [c276]Minkai Xu, Tomas Geffner, Karsten Kreis, Weili Nie, Yilun Xu, Jure Leskovec, Stefano Ermon, Arash Vahdat:
Energy-Based Diffusion Language Models for Text Generation. ICLR 2025 - [c275]Dapeng Jiang, Xiangzhe Kong, Jiaqi Han, Mingyu Li, Rui Jiao, Wenbing Huang, Stefano Ermon, Jianzhu Ma, Yang Liu:
Zero-Shot Cyclic Peptide Design via Composable Geometric Constraints. ICML 2025 - [c274]Samar Khanna, Medhanie Irgau, David B. Lobell, Stefano Ermon:
ExPLoRA: Parameter-Efficient Extended Pre-Training to Adapt Vision Transformers under Domain Shifts. ICML 2025 - [c273]Yuxuan Song, Juntong Shi, Jingjing Gong, Minkai Xu, Stefano Ermon, Hao Zhou, Wei-Ying Ma:
Smooth Interpolation for Improved Discrete Graph Generative Models. ICML 2025 - [c272]Honghua Zhang, Meihua Dang, Benjie Wang, Stefano Ermon, Nanyun Peng, Guy Van den Broeck:
Scaling Probabilistic Circuits via Monarch Matrices. ICML 2025 - [c271]Linqi Zhou, Stefano Ermon, Jiaming Song:
Inductive Moment Matching. ICML 2025 - [c270]Bac Nguyen, Chieh-Hsin Lai, Yuhta Takida, Naoki Murata, Toshimitsu Uesaka, Stefano Ermon, Yuki Mitsufuji:
Improving Vector-Quantized Image Modeling with Latent Consistency-Matching Diffusion. IJCNN 2025: 1-8 - [i297]Meihua Dang, Anikait Singh, Linqi Zhou, Stefano Ermon, Jiaming Song:
Personalized Preference Fine-tuning of Diffusion Models. CoRR abs/2501.06655 (2025) - [i296]Haowei Lin, Shanda Li, Haotian Ye, Yiming Yang, Stefano Ermon, Yitao Liang, Jianzhu Ma:
TFG-Flow: Training-free Guidance in Multimodal Generative Flow. CoRR abs/2501.14216 (2025) - [i295]Mingyu Kim, Dongjun Kim, Amman Yusuf, Stefano Ermon, Mijung Park:
Training-Free Safe Denoisers for Safe Use of Diffusion Models. CoRR abs/2502.08011 (2025) - [i294]Anikait Singh, Sheryl Hsu, Kyle Hsu, Eric Mitchell, Stefano Ermon, Tatsunori Hashimoto, Archit Sharma, Chelsea Finn:
FSPO: Few-Shot Preference Optimization of Synthetic Preference Data in LLMs Elicits Effective Personalization to Real Users. CoRR abs/2502.19312 (2025) - [i293]Silas Alberti, Kenan Hasanaliyev, Manav Shah, Stefano Ermon:
Data Unlearning in Diffusion Models. CoRR abs/2503.01034 (2025) - [i292]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, Patrick D. Hsu, Brian L. Hie, Stefano Ermon, Michael Poli:
Systems and Algorithms for Convolutional Multi-Hybrid Language Models at Scale. CoRR abs/2503.01868 (2025) - [i291]Linqi Zhou, Stefano Ermon, Jiaming Song:
Inductive Moment Matching. CoRR abs/2503.07565 (2025) - [i290]Yashas Annadani, Syrine Belakaria, Stefano Ermon, Stefan Bauer, Barbara E. Engelhardt:
Preference-Guided Diffusion for Multi-Objective Offline Optimization. CoRR abs/2503.17299 (2025) - [i289]Syrine Belakaria, Joshua Kazdan, Charles Marx, Chris Cundy, Willie Neiswanger, Sanmi Koyejo, Barbara E. Engelhardt, Stefano Ermon:
Sharpe Ratio-Guided Active Learning for Preference Optimization in RLHF. CoRR abs/2503.22137 (2025) - [i288]Gabe Guo, Stefano Ermon:
Reviving Any-Subset Autoregressive Models with Principled Parallel Sampling and Speculative Decoding. CoRR abs/2504.20456 (2025) - [i287]Valter Hudovernik, Minkai Xu, Juntong Shi, Lovro Subelj, Stefano Ermon, Erik Strumbelj, Jure Leskovec:
RelDiff: Relational Data Generative Modeling with Graph-Based Diffusion Models. CoRR abs/2506.00710 (2025) - [i286]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:
Exploring Diffusion Transformer Designs via Grafting. CoRR abs/2506.05340 (2025) - [i285]Honghua Zhang, Meihua Dang, Benjie Wang, Stefano Ermon, Nanyun Peng, Guy Van den Broeck:
Scaling Probabilistic Circuits via Monarch Matrices. CoRR abs/2506.12383 (2025) - [i284]Samar Khanna, Siddhant Kharbanda, Shufan Li, Harshit Varma, Eric Wang, Sawyer Birnbaum, Ziyang Luo, Yanis Miraoui, Akash Palrecha, Stefano Ermon, Aditya Grover, Volodymyr Kuleshov:
Mercury: Ultra-Fast Language Models Based on Diffusion. CoRR abs/2506.17298 (2025) - [i283]Wanjia Zhao, Jiaqi Han, Siyi Gu, Mingjian Jiang, James Zou, Stefano Ermon:
GeoAda: Efficiently Finetune Geometric Diffusion Models with Equivariant Adapters. CoRR abs/2507.02085 (2025) - [i282]Dapeng Jiang, Xiangzhe Kong, Jiaqi Han, Mingyu Li, Rui Jiao, Wenbing Huang, Stefano Ermon, Jianzhu Ma, Yang Liu:
Zero-Shot Cyclic Peptide Design via Composable Geometric Constraints. CoRR abs/2507.04225 (2025) - [i281]Jiaqi Han, Austin Wang, Minkai Xu, Wenda Chu, Meihua Dang, Yisong Yue, Stefano Ermon:
Discrete Diffusion Trajectory Alignment via Stepwise Decomposition. CoRR abs/2507.04832 (2025) - [i280]Binxu Li, Minkai Xu, Meihua Dang, Stefano Ermon:
Divergence Minimization Preference Optimization for Diffusion Model Alignment. CoRR abs/2507.07510 (2025) - [i279]Meihua Dang, Jiaqi Han, Minkai Xu, Kai Xu, Akash Srivastava, Stefano Ermon:
Inference-Time Scaling of Diffusion Language Models with Particle Gibbs Sampling. CoRR abs/2507.08390 (2025) - [i278]Jiaqi Han, Haotian Ye, Puheng Li, Minkai Xu, James Zou, Stefano Ermon:
CHORDS: Diffusion Sampling Accelerator with Multi-core Hierarchical ODE Solvers. CoRR abs/2507.15260 (2025) - [i277]Jacqueline R. M. A. Maasch, Willie Neiswanger, Stefano Ermon, Volodymyr Kuleshov:
Probabilistic Graphical Models: A Concise Tutorial. CoRR abs/2507.17116 (2025) - [i276]Kaiwen Zheng, Huayu Chen, Haotian Ye, Haoxiang Wang, Qinsheng Zhang, Kai Jiang, Hang Su, Stefano Ermon, Jun Zhu, Ming-Yu Liu:
DiffusionNFT: Online Diffusion Reinforcement with Forward Process. CoRR abs/2509.16117 (2025) - [i275]Weiqiao Han, Chenlin Meng, Christopher D. Manning, Stefano Ermon:
DistillKac: Few-Step Image Generation via Damped Wave Equations. CoRR abs/2509.21513 (2025) - [i274]Peng Luo, Xiayin Lou, Yu Zheng, Zhuo Zheng, Stefano Ermon:
GeoEvolve: Automating Geospatial Model Discovery via Multi-Agent Large Language Models. CoRR abs/2509.21593 (2025) - [i273]Zheyuan Hu, Chieh-Hsin Lai, Yuki Mitsufuji, Stefano Ermon:
CMT: Mid-Training for Efficient Learning of Consistency, Mean Flow, and Flow Map Models. CoRR abs/2509.24526 (2025) - [i272]Tianlang Chen, Minkai Xu, Jure Leskovec, Stefano Ermon:
RFG: Test-Time Scaling for Diffusion Large Language Model Reasoning with Reward-Free Guidance. CoRR abs/2509.25604 (2025) - [i271]Qiyuan He, Yicong Li, Haotian Ye, Jinghao Wang, Xinyao Liao, Pheng-Ann Heng, Stefano Ermon, James Zou, Angela Yao:
REAR: Rethinking Visual Autoregressive Models via Generator-Tokenizer Consistency Regularization. CoRR abs/2510.04450 (2025) - [i270]Chieh-Hsin Lai, Yang Song, Dongjun Kim, Yuki Mitsufuji, Stefano Ermon:
The Principles of Diffusion Models. CoRR abs/2510.21890 (2025) - [i269]Xiang Li, Till Jahnke, Rebecca Boll, Jiaqi Han, Minkai Xu, Michael Meyer, Maria Novella Piancastelli, Daniel Rolles, Artem Rudenko, Florian Trinter, Thomas J. A. Wolf, Jana B. Thayer, James P. Cryan, Stefano Ermon, Phay J. Ho:
Generative Modeling Enables Molecular Structure Retrieval from Coulomb Explosion Imaging. CoRR abs/2511.00179 (2025) - [i268]Zheyuan Hu, Chieh-Hsin Lai, Ge Wu, Yuki Mitsufuji, Stefano Ermon:
MeanFlow Transformers with Representation Autoencoders. CoRR abs/2511.13019 (2025) - [i267]Aniketh Iyengar, Jiaqi Han, Boris Ruf, Vincent Grari, Marcin Detyniecki, Stefano Ermon:
Energy Scaling Laws for Diffusion Models: Quantifying Compute and Carbon Emissions in Image Generation. CoRR abs/2511.17031 (2025) - [i266]Jingyang Ou, Jiaqi Han, Minkai Xu, Shaoxuan Xu, Jianwen Xie, Stefano Ermon, Yi Wu, Chongxuan Li:
Principled RL for Diffusion LLMs Emerges from a Sequence-Level Perspective. CoRR abs/2512.03759 (2025) - [i265]Haotian Ye, Kaiwen Zheng, Jiashu Xu, Puheng Li, Huayu Chen, Jiaqi Han, Sheng Liu, Qinsheng Zhang, Hanzi Mao, Zekun Hao, Prithvijit Chattopadhyay, Dinghao Yang, Liang Feng, Maosheng Liao, Junjie Bai, Ming-Yu Liu, James Zou, Stefano Ermon:
Data-regularized Reinforcement Learning for Diffusion Models at Scale. CoRR abs/2512.04332 (2025) - [i264]Haotian Ye, Qiyuan He, Jiaqi Han, Puheng Li, Jiaojiao Fan, Zekun Hao, Fitsum Reda, Yogesh Balaji, Huayu Chen, Sheng Liu, Angela Yao, James Zou, Stefano Ermon, Haoxiang Wang, Ming-Yu Liu:
InfoTok: Adaptive Discrete Video Tokenizer via Information-Theoretic Compression. CoRR abs/2512.16975 (2025) - 2024
- [j12]Sara A. Miskovich, Willie Neiswanger, William Colocho, Claudio Emma, Jacqueline Garrahan, Timothy Maxwell, Christopher Mayes, Stefano Ermon, Auralee Edelen, Daniel Ratner:
Multipoint-BAX: a new approach for efficiently tuning particle accelerator emittance via virtual objectives. Mach. Learn. Sci. Technol. 5(1): 15004 (2024) - [c269]Tailin Wu, Willie Neiswanger, Hongtao Zheng, Stefano Ermon, Jure Leskovec:
Uncertainty Quantification for Forward and Inverse Problems of PDEs via Latent Global Evolution. AAAI 2024: 320-328 - [c268]Jonathan Xu, Amna Elmustafa, Liya Weldegebriel, Emnet Negash, Richard Lee, Chenlin Meng, Stefano Ermon, David B. Lobell:
HarvestNet: A Dataset for Detecting Smallholder Farming Activity Using Harvest Piles and Remote Sensing. AAAI 2024: 22438-22446 - [c267]Ryan Park, Rafael Rafailov, Stefano Ermon, Chelsea Finn:
Disentangling Length from Quality in Direct Preference Optimization. ACL (Findings) 2024: 4998-5017 - [c266]Chris Cundy, Rishi Desai, Stefano Ermon:
Privacy-Constrained Policies via Mutual Information Regularized Policy Gradients. AISTATS 2024: 2809-2817 - [c265]Linqi Zhou, Andy Shih, Chenlin Meng, Stefano Ermon:
DreamPropeller: Supercharge Text-to-3D Generation with Parallel Sampling. CVPR 2024: 4610-4619 - [c264]Jonathan Xu, Amna Elmustafa, Liya Weldegebriel, Emnet Negash, Richard Lee, Chenlin Meng, Stefano Ermon, David B. Lobell:
HarvestNet: A Dataset for Detecting Smallholder Farming Activity Using Harvest Piles and Remote Sensing. CVPR Workshops 2024: 5366-5374 - [c263]Bram Wallace, Meihua Dang, Rafael Rafailov, Linqi Zhou, Aaron Lou, Senthil Purushwalkam, Stefano Ermon, Caiming Xiong, Shafiq Joty, Nikhil Naik:
Diffusion Model Alignment Using Direct Preference Optimization. CVPR 2024: 8228-8238 - [c262]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:
HIVE: Harnessing Human Feedback for Instructional Visual Editing. CVPR 2024: 9026-9036 - [c261]Hao Li, Yang Zou, Ying Wang, Orchid Majumder, Yusheng Xie, R. Manmatha, Ashwin Swaminathan, Zhuowen Tu, Stefano Ermon, Stefano Soatto:
On the Scalability of Diffusion-based Text-to-Image Generation. CVPR 2024: 9400-9409 - [c260]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, Akshay Nambi, Ni Lao, Gengchen Mai:
TorchSpatial: A Python Package for Spatial Representation Learning and Geo-Aware Model Development. GeoIndstry@SIGSPATIAL 2024: 39-42 - [c259]Ling Yang, Zhilong Zhang, Zhaochen Yu, Jingwei Liu, Minkai Xu, Stefano Ermon, Bin Cui:
Cross-Modal Contextualized Diffusion Models for Text-Guided Visual Generation and Editing. ICLR 2024 - [c258]Chris Cundy, Stefano Ermon:
SequenceMatch: Imitation Learning for Autoregressive Sequence Modelling with Backtracking. ICLR 2024 - [c257]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:
Manifold Preserving Guided Diffusion. ICLR 2024 - [c256]Samar Khanna, Patrick Liu, Linqi Zhou, Chenlin Meng, Robin Rombach, Marshall Burke, David B. Lobell, Stefano Ermon:
DiffusionSat: A Generative Foundation Model for Satellite Imagery. ICLR 2024 - [c255]Dongjun Kim, Chieh-Hsin Lai, Wei-Hsiang Liao, Naoki Murata, Yuhta Takida, Toshimitsu Uesaka, Yutong He, Yuki Mitsufuji, Stefano Ermon:
Consistency Trajectory Models: Learning Probability Flow ODE Trajectory of Diffusion. ICLR 2024 - [c254]Rohin Manvi, Samar Khanna, Gengchen Mai, Marshall Burke, David B. Lobell, Stefano Ermon:
GeoLLM: Extracting Geospatial Knowledge from Large Language Models. ICLR 2024 - [c253]Charlotte Nicks, Eric Mitchell, Rafael Rafailov, Archit Sharma, Christopher D. Manning, Chelsea Finn, Stefano Ermon:
Language Model Detectors Are Easily Optimized Against. ICLR 2024 - [c252]Linqi Zhou, Aaron Lou, Samar Khanna, Stefano Ermon:
Denoising Diffusion Bridge Models. ICLR 2024 - [c251]Ling Yang, Zhaochen Yu, Chenlin Meng, Minkai Xu, Stefano Ermon, Bin Cui:
Mastering Text-to-Image Diffusion: Recaptioning, Planning, and Generating with Multimodal LLMs. ICML 2024 - [c250]Aaron Lou, Chenlin Meng, Stefano Ermon:
Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution. ICML 2024 - [c249]Rohin Manvi, Samar Khanna, Marshall Burke, David B. Lobell, Stefano Ermon:
Large Language Models are Geographically Biased. ICML 2024 - [c248]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, Atsushi Yamashita:
State-Free Inference of State-Space Models: The *Transfer Function* Approach. ICML 2024 - [c247]Michael Poli, Armin W. Thomas, Eric Nguyen, Pragaash Ponnusamy, Björn Deiseroth, Kristian Kersting, Taiji Suzuki, Brian L. Hie, Stefano Ermon, Christopher Ré, Ce Zhang, Stefano Massaroli:
Mechanistic Design and Scaling of Hybrid Architectures. ICML 2024 - [c246]Fahim Tajwar, Anikait Singh, Archit Sharma, Rafael Rafailov, Jeff Schneider, Tengyang Xie, Stefano Ermon, Chelsea Finn, Aviral Kumar:
Preference Fine-Tuning of LLMs Should Leverage Suboptimal, On-Policy Data. ICML 2024 - [c245]Minkai Xu, Jiaqi Han, Aaron Lou, Jean Kossaifi, Arvind Ramanathan, Kamyar Azizzadenesheli, Jure Leskovec, Stefano Ermon, Anima Anandkumar:
Equivariant Graph Neural Operator for Modeling 3D Dynamics. ICML 2024 - [c244]Syrine Belakaria, Ben Letham, Jana Doppa, Barbara Engelhardt, Stefano Ermon, Eytan Bakshy:
Active Learning for Derivative-Based Global Sensitivity Analysis with Gaussian Processes. NeurIPS 2024 - [c243]Siyi Gu, Minkai Xu, Alexander S. Powers, Weili Nie, Tomas Geffner, Karsten Kreis, Jure Leskovec, Arash Vahdat, Stefano Ermon:
Aligning Target-Aware Molecule Diffusion Models with Exact Energy Optimization. NeurIPS 2024 - [c242]Jiaqi Han, Minkai Xu, Aaron Lou, Haotian Ye, Stefano Ermon:
Geometric Trajectory Diffusion Models. NeurIPS 2024 - [c241]Dongjun Kim, Chieh-Hsin Lai, Wei-Hsiang Liao, Yuhta Takida, Naoki Murata, Toshimitsu Uesaka, Yuki Mitsufuji, Stefano Ermon:
PaGoDA: Progressive Growing of a One-Step Generator from a Low-Resolution Diffusion Teacher. NeurIPS 2024 - [c240]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, Luis Oala, Wojciech Samek:
Generative Fractional Diffusion Models. NeurIPS 2024 - [c239]Felix Petersen, Christian Borgelt, Stefano Ermon:
TrAct: Making First-layer Pre-Activations Trainable. NeurIPS 2024 - [c238]Felix Petersen, Christian Borgelt, Tobias Sutter, Hilde Kuehne, Oliver Deussen, Stefano Ermon:
Newton Losses: Using Curvature Information for Learning with Differentiable Algorithms. NeurIPS 2024 - [c237]Felix Petersen, Hilde Kuehne, Christian Borgelt, Julian Welzel, Stefano Ermon:
Convolutional Differentiable Logic Gate Networks. NeurIPS 2024 - [c236]Nikil Roashan Selvam, Amil Merchant, Stefano Ermon:
Self-Refining Diffusion Samplers: Enabling Parallelization via Parareal Iterations. NeurIPS 2024 - [c235]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, Akshay Nambi, Ni Lao, Gengchen Mai:
TorchSpatial: A Location Encoding Framework and Benchmark for Spatial Representation Learning. NeurIPS 2024 - [c234]Haotian Ye, Haowei Lin, Jiaqi Han, Minkai Xu, Sheng Liu, Yitao Liang, Jianzhu Ma, James Y. Zou, Stefano Ermon:
TFG: Unified Training-Free Guidance for Diffusion Models. NeurIPS 2024 - [c233]Zhuo Zheng, Yanfei Zhong, Liangpei Zhang, Stefano Ermon:
Segment Any Change. NeurIPS 2024 - [c232]Zhengbang Zhu, Minghuan Liu, Liyuan Mao, Bingyi Kang, Minkai Xu, Yong Yu, Stefano Ermon, Weinan Zhang:
MADiff: Offline Multi-agent Learning with Diffusion Models. NeurIPS 2024 - [i263]Minkai Xu, Jiaqi Han, Aaron Lou, Jean Kossaifi, Arvind Ramanathan, Kamyar Azizzadenesheli, Jure Leskovec, Stefano Ermon, Anima Anandkumar:
Equivariant Graph Neural Operator for Modeling 3D Dynamics. CoRR abs/2401.11037 (2024) - [i262]Ling Yang, Zhaochen Yu, Chenlin Meng, Minkai Xu, Stefano Ermon, Bin Cui:
Mastering Text-to-Image Diffusion: Recaptioning, Planning, and Generating with Multimodal LLMs. CoRR abs/2401.11708 (2024) - [i261]Zhuo Zheng, Yanfei Zhong, Liangpei Zhang, Stefano Ermon:
Segment Any Change. CoRR abs/2402.01188 (2024) - [i260]Rohin Manvi, Samar Khanna, Marshall Burke, David B. Lobell, Stefano Ermon:
Large Language Models are Geographically Biased. CoRR abs/2402.02680 (2024) - [i259]Tailin Wu, Willie Neiswanger, Hongtao Zheng, Stefano Ermon, Jure Leskovec:
Uncertainty Quantification for Forward and Inverse Problems of PDEs via Latent Global Evolution. CoRR abs/2402.08383 (2024) - [i258]Ling Yang, Zhilong Zhang, Zhaochen Yu, Jingwei Liu, Minkai Xu, Stefano Ermon, Bin Cui:
Cross-Modal Contextualized Diffusion Models for Text-Guided Visual Generation and Editing. CoRR abs/2402.16627 (2024) - [i257]Michael Poli, Armin W. Thomas, Eric Nguyen, Pragaash Ponnusamy, Björn Deiseroth, Kristian Kersting, Taiji Suzuki, Brian L. Hie, Stefano Ermon, Christopher Ré, Ce Zhang, Stefano Massaroli:
Mechanistic Design and Scaling of Hybrid Architectures. CoRR abs/2403.17844 (2024) - [i256]Ryan Park, Rafael Rafailov, Stefano Ermon, Chelsea Finn:
Disentangling Length from Quality in Direct Preference Optimization. CoRR abs/2403.19159 (2024) - [i255]Hao Li, Yang Zou, Ying Wang, Orchid Majumder, Yusheng Xie, R. Manmatha, Ashwin Swaminathan, Zhuowen Tu, Stefano Ermon, Stefano Soatto:
On the Scalability of Diffusion-based Text-to-Image Generation. CoRR abs/2404.02883 (2024) - [i254]Fahim Tajwar, Anikait Singh, Archit Sharma, Rafael Rafailov, Jeff Schneider, Tengyang Xie, Stefano Ermon, Chelsea Finn, Aviral Kumar:
Preference Fine-Tuning of LLMs Should Leverage Suboptimal, On-Policy Data. CoRR abs/2404.14367 (2024) - [i253]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, Atsushi Yamashita, Michael Poli:
State-Free Inference of State-Space Models: The Transfer Function Approach. CoRR abs/2405.06147 (2024) - [i252]Dongjun Kim, Chieh-Hsin Lai, Wei-Hsiang Liao, Yuhta Takida, Naoki Murata, Toshimitsu Uesaka, Yuki Mitsufuji, Stefano Ermon:
PaGoDA: Progressive Growing of a One-Step Generator from a Low-Resolution Diffusion Teacher. CoRR abs/2405.14822 (2024) - [i251]Samar Khanna, Medhanie Irgau, David B. Lobell, Stefano Ermon:
ExPLoRA: Parameter-Efficient Extended Pre-Training to Adapt Vision Transformers under Domain Shifts. CoRR abs/2406.10973 (2024) - [i250]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, Akshay Nambi, Ni Lao, Gengchen Mai:
TorchSpatial: A Location Encoding Framework and Benchmark for Spatial Representation Learning. CoRR abs/2406.15658 (2024) - [i249]Zhuo Zheng, Stefano Ermon, Dongjun Kim, Liangpei Zhang, Yanfei Zhong:
Changen2: Multi-Temporal Remote Sensing Generative Change Foundation Model. CoRR abs/2406.17998 (2024) - [i248]Siyi Gu, Minkai Xu, Alexander S. Powers, Weili Nie, Tomas Geffner, Karsten Kreis, Jure Leskovec, Arash Vahdat, Stefano Ermon:
Aligning Target-Aware Molecule Diffusion Models with Exact Energy Optimization. CoRR abs/2407.01648 (2024) - [i247]Ling Yang, Zixiang Zhang, Zhilong Zhang, Xingchao Liu, Minkai Xu, Wentao Zhang, Chenlin Meng, Stefano Ermon, Bin Cui:
Consistency Flow Matching: Defining Straight Flows with Velocity Consistency. CoRR abs/2407.02398 (2024) - [i246]Eunwoo Kim, Un Yang, Cheol Lae Roh, Stefano Ermon:
Unsupervised Anomaly Detection Using Diffusion Trend Analysis. CoRR abs/2407.09578 (2024) - [i245]Syrine Belakaria, Benjamin Letham, Janardhan Rao Doppa, Barbara Engelhardt, Stefano Ermon, Eytan Bakshy:
Active Learning for Derivative-Based Global Sensitivity Analysis with Gaussian Processes. CoRR abs/2407.09739 (2024) - [i244]Joshua Kazdan, Hao Sun, Jiaqi Han, Felix Petersen, Stefano Ermon:
CPSample: Classifier Protected Sampling for Guarding Training Data During Diffusion. CoRR abs/2409.07025 (2024) - [i243]Haotian Ye, Haowei Lin, Jiaqi Han, Minkai Xu, Sheng Liu, Yitao Liang, Jianzhu Ma, James Zou, Stefano Ermon:
TFG: Unified Training-Free Guidance for Diffusion Models. CoRR abs/2409.15761 (2024) - [i242]Charles Marx, Volodymyr Kuleshov, Stefano Ermon:
Calibrated Probabilistic Forecasts for Arbitrary Sequences. CoRR abs/2409.19157 (2024) - [i241]Yangming Li, Chieh-Hsin Lai, Carola-Bibiane Schönlieb, Yuki Mitsufuji, Stefano Ermon:
Bellman Diffusion: Generative Modeling as Learning a Linear Operator in the Distribution Space. CoRR abs/2410.01796 (2024) - [i240]Rohin Manvi, Anikait Singh, Stefano Ermon:
Adaptive Inference-Time Compute: LLMs Can Predict if They Can Do Better, Even Mid-Generation. CoRR abs/2410.02725 (2024) - [i239]Jeremy Andrew Irvin, Emily Ruoyu Liu, Joyce Chuyi Chen, Ines Dormoy, Jinyoung Kim, Samar Khanna, Zhuo Zheng, Stefano Ermon:
TEOChat: A Large Vision-Language Assistant for Temporal Earth Observation Data. CoRR abs/2410.06234 (2024) - [i238]Bohan Zeng, Ling Yang, Siyu Li, Jiaming Liu, Zixiang Zhang, Juanxi Tian, Kaixin Zhu, Yongzhen Guo, Fu-Yun Wang, Minkai Xu, Stefano Ermon, Wentao Zhang:
Trans4D: Realistic Geometry-Aware Transition for Compositional Text-to-4D Synthesis. CoRR abs/2410.07155 (2024) - [i237]Felix Petersen, Christian Borgelt, Aashwin Ananda Mishra, Stefano Ermon:
Generalizing Stochastic Smoothing for Differentiation and Gradient Estimation. CoRR abs/2410.08125 (2024) - [i236]Jiaqi Han, Minkai Xu, Aaron Lou, Haotian Ye, Stefano Ermon:
Geometric Trajectory Diffusion Models. CoRR abs/2410.13027 (2024) - [i235]Naoki Murata, Chieh-Hsin Lai, Yuhta Takida, Toshimitsu Uesaka, Bac Nguyen, Stefano Ermon, Yuki Mitsufuji:
G2D2: Gradient-guided Discrete Diffusion for image inverse problem solving. CoRR abs/2410.14710 (2024) - [i234]Bac Nguyen, Chieh-Hsin Lai, Yuhta Takida, Naoki Murata, Toshimitsu Uesaka, Stefano Ermon, Yuki Mitsufuji:
Mitigating Embedding Collapse in Diffusion Models for Categorical Data. CoRR abs/2410.14758 (2024) - [i233]Felix Petersen, Christian Borgelt, Tobias Sutter, Hilde Kuehne, Oliver Deussen, Stefano Ermon:
Newton Losses: Using Curvature Information for Learning with Differentiable Algorithms. CoRR abs/2410.19055 (2024) - [i232]Juntong Shi, Minkai Xu, Harper Hua, Hengrui Zhang, Stefano Ermon, Jure Leskovec:
TabDiff: a Multi-Modal Diffusion Model for Tabular Data Generation. CoRR abs/2410.20626 (2024) - [i231]Minkai Xu, Tomas Geffner, Karsten Kreis, Weili Nie, Yilun Xu, Jure Leskovec, Stefano Ermon, Arash Vahdat:
Energy-Based Diffusion Language Models for Text Generation. CoRR abs/2410.21357 (2024) - [i230]Jiaqi Han, Mingjian Jiang, Yuxuan Song, Jure Leskovec, Stefano Ermon, Minkai Xu:
f-PO: Generalizing Preference Optimization with f-divergence Minimization. CoRR abs/2410.21662 (2024) - [i229]Felix Petersen, Christian Borgelt, Stefano Ermon:
TrAct: Making First-layer Pre-Activations Trainable. CoRR abs/2410.23970 (2024) - [i228]Felix Petersen, Hilde Kuehne, Christian Borgelt, Julian Welzel, Stefano Ermon:
Convolutional Differentiable Logic Gate Networks. CoRR abs/2411.04732 (2024) - [i227]Syrine Belakaria, Alaleh Ahmadianshalchi, Barbara Engelhardt, Stefano Ermon, Janardhan Rao Doppa:
Non-Myopic Multi-Objective Bayesian Optimization. CoRR abs/2412.08085 (2024) - [i226]Nikil Roashan Selvam, Amil Merchant, Stefano Ermon:
Self-Refining Diffusion Samplers: Enabling Parallelization via Parareal Iterations. CoRR abs/2412.08292 (2024) - [i225]Hao Li, Shamit Lal, Zhiheng Li, Yusheng Xie, Ying Wang, Yang Zou, Orchid Majumder, R. Manmatha, Zhuowen Tu, Stefano Ermon, Stefano Soatto, Ashwin Swaminathan:
Efficient Scaling of Diffusion Transformers for Text-to-Image Generation. CoRR abs/2412.12391 (2024) - 2023
- [j11]Gengchen Mai, Chiyu Jiang, Weiwei Sun, Rui Zhu, Yao Xuan, Ling Cai, Krzysztof Janowicz, Stefano Ermon, Ni Lao:
Towards general-purpose representation learning of polygonal geometries. GeoInformatica 27(2): 289-340 (2023) - [j10]Muyang Li, Ji Lin, Chenlin Meng, Stefano Ermon, Song Han, Jun-Yan Zhu:
Efficient Spatially Sparse Inference for Conditional GANs and Diffusion Models. IEEE Trans. Pattern Anal. Mach. Intell. 45(12): 14465-14480 (2023) - [j9]Berivan Isik, Kristy Choi, Xin Zheng, Tsachy Weissman, Stefano Ermon, H.-S. Philip Wong, Armin Alaghi:
Neural Network Compression for Noisy Storage Devices. ACM Trans. Embed. Comput. Syst. 22(3): 58:1-58:29 (2023) - [j8]Arundhati Banerjee, Soham R. Phade, Stefano Ermon, Stephan Zheng:
MERMAIDE: Learning to Align Learners using Model-Based Meta-Learning. Trans. Mach. Learn. Res. 2023 (2023) - [j7]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, Akshat Agarwal, Alethea Power, Alex Ray, Alex Warstadt, Alexander W. Kocurek, Ali Safaya, Ali Tazarv, Alice Xiang, Alicia Parrish, Allen Nie, Aman Hussain, Amanda Askell, Amanda Dsouza, Ambrose Slone, Ameet Rahane, Anantharaman S. Iyer, Anders Andreassen, Andrea Madotto, Andrea Santilli, Andreas Stuhlmüller, Andrew M. Dai, Andrew La, Andrew K. Lampinen, Andy Zou, Angela Jiang, Angelica Chen, Anh Vuong, Animesh Gupta, Anna Gottardi, Antonio Norelli, Anu Venkatesh, Arash Gholamidavoodi, Arfa Tabassum, Arul Menezes, Arun Kirubarajan, Asher Mullokandov, Ashish Sabharwal, Austin Herrick, Avia Efrat, Aykut Erdem, Ayla Karakas, B. Ryan Roberts, Bao Sheng Loe, Barret Zoph, Bartlomiej Bojanowski, Batuhan Özyurt, Behnam Hedayatnia, Behnam Neyshabur, Benjamin Inden, Benno Stein, Berk Ekmekci, Bill Yuchen Lin, Blake Howald, Bryan Orinion, Cameron Diao, Cameron Dour, Catherine Stinson, Cedrick Argueta, Cèsar Ferri Ramírez, Chandan Singh, Charles Rathkopf, Chenlin Meng, Chitta Baral, Chiyu Wu, Chris Callison-Burch,