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Conditional diffusion models serve as the foundation of modern image synthesis and find extensive application in fields like computational biology and reinforcement learning.
Information-theoretic determination of minimax rates of convergence
Yuhong Yang and Andrew Barron · 1999
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A distribution-free theory of nonparametric regression
László Györfi, Michael Kohler, Adam Krzyzak, and Harro Walk · 2006
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All of nonparametric statistics
Larry Wasserman · 2006
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Finite-time bounds for fitted value iteration
Rémi Munos and Csaba Szepesvári · 2008
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Introduction to Nonparametric Estimation
Alexandre B. Tsybakov · 2008
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Pure exploration in finitely-armed and continuous-armed bandits
Sébastien Bubeck, Rémi Munos, and Gilles Stoltz · 2011
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Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2011
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A connection between score matching and denoising autoencoders
Pascal Vincent · 2011
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Minimax theory for high-dimensional gaussian mixtures with sparse mean separation
Martin Azizyan, Aarti Singh, and Larry Wasserman · 2013
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Thin shell implies spectral gap up to polylog via a stochastic localization scheme
Ronen Eldan · 2013
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Openai gym, 2016
Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
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Breaking the curse of horizon: Infinite-horizon off-policy estimation
Qiang Liu, Lihong Li, Ziyang Tang, and Dengyong Zhou · 2018
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Information-theoretic considerations in batch reinforcement learning
Jinglin Chen and Nan Jiang · 2019
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Plug and play language models: A simple approach to controlled text generation
Sumanth Dathathri, Andrea Madotto, Janice Lan, Jane Hung, Eric Frank, Piero Molino, Jason Yosinski, and Rosanne Liu · 2019
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Introduction to multi-armed bandits
Aleksandrs Slivkins et al · 2019
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Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
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Generative modeling with denoising auto-encoders and langevin sampling
Adam Block, Youssef Mroueh, and Alexander Rakhlin · 2020
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Wavegrad: Estimating gradients for waveform generation
Nanxin Chen, Yu Zhang, Heiga Zen, Ron J Weiss, Mohammad Norouzi, and William Chan · 2020
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A theoretical analysis of deep Q-learning
Jianqing Fan, Zhaoran Wang, Yuchen Xie, and Zhuoran Yang · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Diffwave: A versatile diffusion model for audio synthesis
Zhifeng Kong, Wei Ping, Jiaji Huang, Kexin Zhao, and Bryan Catanzaro · 2020
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Adaptive approximation and generalization of deep neural network with intrinsic dimensionality
Ryumei Nakada and Masaaki Imaizumi · 2020
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Improved techniques for training score-based generative models
Yang Song and Stefano Ermon · 2020
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Diffusion schrödinger bridge with applications to score-based generative modeling
Valentin De Bortoli, James Thornton, Jeremy Heng, and Arnaud Doucet · 2021
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Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
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Diff-tts: A denoising diffusion model for text-to-speech
Myeonghun Jeong, Hyeongju Kim, Sung Jun Cheon, Byoung Jin Choi, and Nam Soo Kim · 2021
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Symbolic music generation with diffusion models
Gautam Mittal, Jesse Engel, Curtis Hawthorne, and Ian Simon · 2021
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Improved denoising diffusion probabilistic models
Alexander Quinn Nichol and Prafulla Dhariwal · 2021
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Solving inverse problems in medical imaging with score-based generative models
Yang Song, Liyue Shen, Lei Xing, and Stefano Ermon · 2021
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CSDI: Conditional score-based diffusion models for probabilistic time series imputation
Yusuke Tashiro, Jiaming Song, Yang Song, and Stefano Ermon · 2021
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Score-based generative modeling in latent space
Arash Vahdat, Karsten Kreis, and Jan Kautz · 2021
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Is conditional generative modeling all you need for decision-making?
Anurag Ajay, Yilun Du, Abhi Gupta, Joshua Tenenbaum, Tommi Jaakkola, and Pulkit Agrawal · 2022
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Diffusion-based time series imputation and forecasting with structured state space models
Juan Miguel Lopez Alcaraz and Nils Strodthoff · 2022
Stochastic interpolants: A unifying framework for flows and diffusions
Michael S Albergo, Nicholas M Boffi, and Eric Vanden-Eijnden · 2023
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Universal guidance for diffusion models
Arpit Bansal, Hong-Min Chu, Avi Schwarzschild, Soumyadip Sengupta, Micah Goldblum, Jonas Geiping, and Tom Goldstein · 2023
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Linear convergence bounds for diffusion models via stochastic localization
Joe Benton, Valentin De Bortoli, Arnaud Doucet, and George Deligiannidis · 2023
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A short note on an inequality between kl and tv, 2023
Clément L. Canonne · 2023
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Diffusion Policy: Visuomotor policy learning via action diffusion
Cheng Chi, Siyuan Feng, Yilun Du, Zhenjia Xu, Eric Cousineau, Benjamin Burchfiel, and Shuran Song · 2023
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Protein structure and sequence generation with equivariant denoising diffusion probabilistic models
Namrata Anand and Tudor Achim · 2022
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Blended diffusion for text-driven editing of natural images
Omri Avrahami, Dani Lischinski, and Ohad Fried · 2022
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High-frequency space diffusion models for accelerated mri
Chentao Cao, Zhuo-Xu Cui, Shaonan Liu, Dong Liang, and Yanjie Zhu · 2022
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Localization schemes: A framework for proving mixing bounds for markov chains
Yuansi Chen and Ronen Eldan · 2022
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Score-based diffusion models for accelerated MRI
Hyungjin Chung and Jong Chul Ye · 2022
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Convergence of denoising diffusion models under the manifold hypothesis
Valentin De Bortoli · 2022
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An information-theoretic view of stochastic localization
Ahmed El Alaoui and Andrea Montanari · 2022
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Sampling from mean-field gibbs measures via diffusion processes
Ahmed El Alaoui, Andrea Montanari, and Mark Sellke · 2023
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Protein design with guided discrete diffusion
Nate Gruver, Samuel Stanton, Nathan C Frey, Tim GJ Rudner, Isidro Hotzel, Julien Lafrance-Vanasse, Arvind Rajpal, Kyunghyun Cho, and Andrew Gordon Wilson · 2023
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Adaptive diffusion priors for accelerated MRI reconstruction
Alper Güngör, Salman UH Dar, Şaban Öztürk, Yilmaz Korkmaz, Hasan A Bedel, Gokberk Elmas, Muzaffer Ozbey, and Tolga Çukur · 2023
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IDQL: Implicit Q-learning as an actor-critic method with diffusion policies
Philippe Hansen-Estruch, Ilya Kostrikov, Michael Janner, Jakub Grudzien Kuba, and Sergey Levine · 2023
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Classifier-free guidance makes image captioning models more descriptive
Simon Kornblith, Lala Li, Zirui Wang, and Thao Nguyen · 2023
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Diffusion models for image restoration and enhancement–a comprehensive survey
Xin Li, Yulin Ren, Xin Jin, Cuiling Lan, Xingrui Wang, Wenjun Zeng, Xinchao Wang, and Zhibo Chen · 2023
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Song Mei and Yuchen Wu · 2023
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On distillation of guided diffusion models
Chenlin Meng, Robin Rombach, Ruiqi Gao, Diederik Kingma, Stefano Ermon, Jonathan Ho, and Tim Salimans · 2023
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Sampling, diffusions, and stochastic localization
Andrea Montanari · 2023
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Posterior sampling from the spiked models via diffusion processes
Andrea Montanari and Yuchen Wu · 2023
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Diffusion models are minimax optimal distribution estimators
Kazusato Oko, Shunta Akiyama, and Taiji Suzuki · 2023
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Imitating human behaviour with diffusion models
Tim Pearce, Tabish Rashid, Anssi Kanervisto, Dave Bignell, Mingfei Sun, Raluca Georgescu, Sergio Valcarcel Macua, Shan Zheng Tan, Ida Momennejad, Katja Hofmann, and Sam Devlin · 2023
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Goal-conditioned imitation learning using score-based diffusion policies
Moritz Reuss, Maximilian Li, Xiaogang Jia, and Rudolf Lioutikov · 2023
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Learning mixtures of gaussians using the ddpm objective
Kulin Shah, Sitan Chen, and Adam Klivans · 2023
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Solving inverse problems with latent diffusion models via hard data consistency
Bowen Song, Soo Min Kwon, Zecheng Zhang, Xinyu Hu, Qing Qu, and Liyue Shen · 2023
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Fast and reliable generation of ehr time series via diffusion models
Muhang Tian, Bernie Chen, Allan Guo, Shiyi Jiang, and Anru R Zhang · 2023
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De novo design of protein structure and function with rfdiffusion
Joseph L Watson, David Juergens, Nathaniel R Bennett, Brian L Trippe, Jason Yim, Helen E Eisenach, Woody Ahern, Andrew J Borst, Robert J Ragotte, Lukas F Milles, et al · 2023
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Guided diffusion for inverse molecular design
Tomer Weiss, Eduardo Mayo Yanes, Sabyasachi Chakraborty, Luca Cosmo, Alex M Bronstein, and Renana Gershoni-Poranne · 2023
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Graph denoising diffusion for inverse protein folding
Kai Yi, Bingxin Zhou, Yiqing Shen, Pietro Liò, and Yu Guang Wang · 2023
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Distribution shift inversion for out-of-distribution prediction
Runpeng Yu, Songhua Liu, Xingyi Yang, and Xinchao Wang · 2023
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Reward-directed conditional diffusion: Provable distribution estimation and reward improvement
Hui Yuan, Kaixuan Huang, Chengzhuo Ni, Minshuo Chen, and Mengdi Wang · 2023
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Optimal score estimation via empirical bayes smoothing
Andre Wibisono, Yihong Wu, and Kaylee Yingxi Yang · 2024
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Protein structure generation via folding diffusion
Kevin E Wu, Kevin K Yang, Rianne van den Berg, Sarah Alamdari, James Y Zou, Alex X Lu, and Ava P Amini · 2024
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