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2020 – today
- 2023
[c122]Eugenio Clerico, George Deligiannidis, Arnaud Doucet:
Wide stochastic networks: Gaussian limit and PAC-Bayesian training. ALT 2023: 447-470- [c121]Francisco Vargas, Will Sussman Grathwohl, Arnaud Doucet:
Denoising Diffusion Samplers. ICLR 2023 - [c120]Yilun Du, Conor Durkan, Robin Strudel, Joshua B. Tenenbaum, Sander Dieleman, Rob Fergus, Jascha Sohl-Dickstein, Arnaud Doucet, Will Sussman Grathwohl:
Reduce, Reuse, Recycle: Compositional Generation with Energy-Based Diffusion Models and MCMC. ICML 2023: 8489-8510 - [c119]Jason Yim, Brian L. Trippe, Valentin De Bortoli, Emile Mathieu, Arnaud Doucet, Regina Barzilay, Tommi S. Jaakkola:
SE(3) diffusion model with application to protein backbone generation. ICML 2023: 40001-40039 - [i77]Rob Cornish, Muhammad Faaiz Taufiq, Arnaud Doucet, Chris C. Holmes:
Causal Falsification of Digital Twins. CoRR abs/2301.07210 (2023) - [i76]Fabian Falck, Christopher Williams, Dominic Danks, George Deligiannidis, Christopher Yau, Chris C. Holmes, Arnaud Doucet, Matthew Willetts:
A Multi-Resolution Framework for U-Nets with Applications to Hierarchical VAEs. CoRR abs/2301.08187 (2023) - [i75]Jason Yim, Brian L. Trippe, Valentin De Bortoli, Emile Mathieu, Arnaud Doucet, Regina Barzilay, Tommi S. Jaakkola:
SE(3) diffusion model with application to protein backbone generation. CoRR abs/2302.02277 (2023) - [i74]Yilun Du, Conor Durkan, Robin Strudel, Joshua B. Tenenbaum, Sander Dieleman, Rob Fergus, Jascha Sohl-Dickstein, Arnaud Doucet, Will Grathwohl:
Reduce, Reuse, Recycle: Compositional Generation with Energy-Based Diffusion Models and MCMC. CoRR abs/2302.11552 (2023) - [i73]Francisco Vargas, Will Grathwohl, Arnaud Doucet:
Denoising Diffusion Samplers. CoRR abs/2302.13834 (2023) - [i72]Yuyang Shi, Valentin De Bortoli, Andrew Campbell, Arnaud Doucet:
Diffusion Schrödinger Bridge Matching. CoRR abs/2303.16852 (2023) - [i71]Andrew Campbell, William Harvey, Christian Weilbach, Valentin De Bortoli, Tom Rainforth, Arnaud Doucet:
Trans-Dimensional Generative Modeling via Jump Diffusion Models. CoRR abs/2305.16261 (2023) - [i70]Maxence Noble, Valentin De Bortoli, Arnaud Doucet, Alain Durmus:
Tree-Based Diffusion Schrödinger Bridge with Applications to Wasserstein Barycenters. CoRR abs/2305.16557 (2023) - [i69]Joe Benton, George Deligiannidis, Arnaud Doucet:
Error Bounds for Flow Matching Methods. CoRR abs/2305.16860 (2023) - [i68]Christopher Williams, Fabian Falck, George Deligiannidis, Chris C. Holmes, Arnaud Doucet, Saifuddin Syed:
A Unified Framework for U-Net Design and Analysis. CoRR abs/2305.19638 (2023) - [i67]David Stutz, Ali Taylan Cemgil, Abhijit Guha Roy, Tatiana Matejovicova, Melih Barsbey, Patricia Strachan, Mike Schaekermann, Jan Freyberg, Rajeev Rikhye, Beverly Freeman, Javier Perez Matos, Umesh Telang, Dale R. Webster, Yuan Liu, Gregory S. Corrado, Yossi Matias, Pushmeet Kohli, Yun Liu, Arnaud Doucet, Alan Karthikesalingam:
Evaluating AI systems under uncertain ground truth: a case study in dermatology. CoRR abs/2307.02191 (2023) - [i66]David Stutz, Abhijit Guha Roy, Tatiana Matejovicova, Patricia Strachan, Ali Taylan Cemgil, Arnaud Doucet:
Conformal prediction under ambiguous ground truth. CoRR abs/2307.09302 (2023) - [i65]Joe Benton, Valentin De Bortoli, Arnaud Doucet, George Deligiannidis:
Linear Convergence Bounds for Diffusion Models via Stochastic Localization. CoRR abs/2308.03686 (2023) - [i64]Çaglar Gülçehre, Tom Le Paine, Srivatsan Srinivasan, Ksenia Konyushkova, Lotte Weerts, Abhishek Sharma, Aditya Siddhant, Alex Ahern, Miaosen Wang, Chenjie Gu, Wolfgang Macherey, Arnaud Doucet, Orhan Firat, Nando de Freitas:
Reinforced Self-Training (ReST) for Language Modeling. CoRR abs/2308.08998 (2023) - [i63]Marin Vlastelica, Tatiana Lopez-Guevara, Kelsey R. Allen, Peter W. Battaglia, Arnaud Doucet, Kimberly L. Stachenfeld:
Diffusion Generative Inverse Design. CoRR abs/2309.02040 (2023) - 2022
- [j66]Maxime Vono, Daniel Paulin, Arnaud Doucet:
Efficient MCMC Sampling with Dimension-Free Convergence Rate using ADMM-type Splitting. J. Mach. Learn. Res. 23: 25:1-25:69 (2022) - [j65]Yutian Chen, Liyuan Xu, Çaglar Gülçehre, Tom Le Paine, Arthur Gretton, Nando de Freitas, Arnaud Doucet:
On Instrumental Variable Regression for Deep Offline Policy Evaluation. J. Mach. Learn. Res. 23: 302:1-302:40 (2022) - [j64]Emilien Dupont, Hrushikesh Loya, Milad Alizadeh, Adam Golinski, Yee Whye Teh, Arnaud Doucet:
COIN++: Neural Compression Across Modalities. Trans. Mach. Learn. Res. 2022 (2022) - [j63]Çaglar Gülçehre, Srivatsan Srinivasan, Jakub Sygnowski, Georg Ostrovski, Mehrdad Farajtabar, Matthew Hoffman, Razvan Pascanu, Arnaud Doucet:
An empirical study of implicit regularization in deep offline RL. Trans. Mach. Learn. Res. 2022 (2022) - [c118]Eugenio Clerico, George Deligiannidis, Arnaud Doucet:
Conditionally Gaussian PAC-Bayes. AISTATS 2022: 2311-2329 - [c117]Emilien Dupont, Yee Whye Teh, Arnaud Doucet:
Generative Models as Distributions of Functions. AISTATS 2022: 2989-3015 - [c116]Badr-Eddine Chérief-Abdellatif, Yuyang Shi, Arnaud Doucet, Benjamin Guedj:
On PAC-Bayesian reconstruction guarantees for VAEs. AISTATS 2022: 3066-3079 - [c115]Eugenio Clerico, Amitis Shidani, George Deligiannidis, Arnaud Doucet:
Chained generalisation bounds. COLT 2022: 4212-4257 - [c114]Angad Singh, Omar Makhlouf, Maximilian Igl, João Messias, Arnaud Doucet, Shimon Whiteson:
Particle-Based Score Estimation for State Space Model Learning in Autonomous Driving. CoRL 2022: 1168-1177 - [c113]David Stutz
, Krishnamurthy Dvijotham, Ali Taylan Cemgil, Arnaud Doucet:
Learning Optimal Conformal Classifiers. ICLR 2022 - [c112]Alexander G. de G. Matthews, Michael Arbel, Danilo Jimenez Rezende, Arnaud Doucet:
Continual Repeated Annealed Flow Transport Monte Carlo. ICML 2022: 15196-15219 - [c111]Liyuan Xu, Yutian Chen, Arnaud Doucet, Arthur Gretton:
Importance Weighted Kernel Bayes' Rule. ICML 2022: 24524-24538 - [c110]Valentin De Bortoli, Emile Mathieu, Michael J. Hutchinson, James Thornton, Yee Whye Teh, Arnaud Doucet:
Riemannian Score-Based Generative Modelling. NeurIPS 2022 - [c109]Andrew Campbell, Joe Benton, Valentin De Bortoli, Thomas Rainforth, George Deligiannidis, Arnaud Doucet:
A Continuous Time Framework for Discrete Denoising Models. NeurIPS 2022 - [c108]Yutian Chen, Xingyou Song, Chansoo Lee, Zi Wang, Richard Zhang, David Dohan, Kazuya Kawakami, Greg Kochanski, Arnaud Doucet, Marc'Aurelio Ranzato, Sagi Perel, Nando de Freitas:
Towards Learning Universal Hyperparameter Optimizers with Transformers. NeurIPS 2022 - [c107]Arnaud Doucet, Will Grathwohl, Alexander G. de G. Matthews, Heiko Strathmann:
Score-Based Diffusion meets Annealed Importance Sampling. NeurIPS 2022 - [c106]Fabian Falck, Christopher Williams, Dominic Danks, George Deligiannidis, Christopher Yau, Chris C. Holmes, Arnaud Doucet, Matthew Willetts:
A Multi-Resolution Framework for U-Nets with Applications to Hierarchical VAEs. NeurIPS 2022 - [c105]Muhammad Faaiz Taufiq, Jean-Francois Ton, Rob Cornish, Yee Whye Teh, Arnaud Doucet:
Conformal Off-Policy Prediction in Contextual Bandits. NeurIPS 2022 - [c104]Sahra Ghalebikesabi, Harry Wilde, Jack Jewson, Arnaud Doucet, Sebastian J. Vollmer, Chris C. Holmes:
Mitigating statistical bias within differentially private synthetic data. UAI 2022: 696-705 - [c103]Yuyang Shi, Valentin De Bortoli, George Deligiannidis, Arnaud Doucet:
Conditional simulation using diffusion Schrödinger bridges. UAI 2022: 1792-1802 - [i62]Emilien Dupont, Hrushikesh Loya, Milad Alizadeh, Adam Golinski, Yee Whye Teh, Arnaud Doucet:
COIN++: Data Agnostic Neural Compression. CoRR abs/2201.12904 (2022) - [i61]Alexander G. de G. Matthews, Michael Arbel, Danilo J. Rezende, Arnaud Doucet:
Continual Repeated Annealed Flow Transport Monte Carlo. CoRR abs/2201.13117 (2022) - [i60]Liyuan Xu, Yutian Chen, Arnaud Doucet, Arthur Gretton:
Importance Weighting Approach in Kernel Bayes' Rule. CoRR abs/2202.02474 (2022) - [i59]Valentin De Bortoli, Emile Mathieu, Michael J. Hutchinson, James Thornton, Yee Whye Teh, Arnaud Doucet:
Riemannian Score-Based Generative Modeling. CoRR abs/2202.02763 (2022) - [i58]Badr-Eddine Chérief-Abdellatif, Yuyang Shi, Arnaud Doucet, Benjamin Guedj:
On PAC-Bayesian reconstruction guarantees for VAEs. CoRR abs/2202.11455 (2022) - [i57]Yuyang Shi, Valentin De Bortoli, George Deligiannidis, Arnaud Doucet:
Conditional Simulation Using Diffusion Schrödinger Bridges. CoRR abs/2202.13460 (2022) - [i56]Eugenio Clerico, Amitis Shidani, George Deligiannidis, Arnaud Doucet:
Chained Generalisation Bounds. CoRR abs/2203.00977 (2022) - [i55]Yutian Chen, Xingyou Song, Chansoo Lee, Zi Wang, Qiuyi Zhang, David Dohan, Kazuya Kawakami, Greg Kochanski, Arnaud Doucet, Marc'Aurelio Ranzato, Sagi Perel, Nando de Freitas:
Towards Learning Universal Hyperparameter Optimizers with Transformers. CoRR abs/2205.13320 (2022) - [i54]Andrew Campbell, Joe Benton, Valentin De Bortoli, Tom Rainforth, George Deligiannidis, Arnaud Doucet:
A Continuous Time Framework for Discrete Denoising Models. CoRR abs/2205.14987 (2022) - [i53]Muhammad Faaiz Taufiq, Jean-Francois Ton, Robert Cornish, Yee Whye Teh, Arnaud Doucet:
Conformal Off-Policy Prediction in Contextual Bandits. CoRR abs/2206.04405 (2022) - [i52]Amitis Shidani, George Deligiannidis, Arnaud Doucet:
Ranking in Contextual Multi-Armed Bandits. CoRR abs/2207.00109 (2022) - [i51]Çaglar Gülçehre, Srivatsan Srinivasan, Jakub Sygnowski, Georg Ostrovski, Mehrdad Farajtabar, Matt Hoffman, Razvan Pascanu, Arnaud Doucet:
An Empirical Study of Implicit Regularization in Deep Offline RL. CoRR abs/2207.02099 (2022) - [i50]James Thornton, Michael J. Hutchinson, Emile Mathieu, Valentin De Bortoli, Yee Whye Teh, Arnaud Doucet:
Riemannian Diffusion Schrödinger Bridge. CoRR abs/2207.03024 (2022) - [i49]Arnaud Doucet, Will Grathwohl, Alexander G. de G. Matthews, Heiko Strathmann:
Score-Based Diffusion meets Annealed Importance Sampling. CoRR abs/2208.07698 (2022) - [i48]Eugenio Clerico, George Deligiannidis, Benjamin Guedj, Arnaud Doucet:
A PAC-Bayes bound for deterministic classifiers. CoRR abs/2209.02525 (2022) - [i47]Francesca R. Crucinio, Valentin De Bortoli, Arnaud Doucet, Adam M. Johansen:
Solving Fredholm Integral Equations of the First Kind via Wasserstein Gradient Flows. CoRR abs/2209.09936 (2022) - [i46]Angus Phillips, Thomas Seror, Michael J. Hutchinson, Valentin De Bortoli, Arnaud Doucet, Emile Mathieu:
Spectral Diffusion Processes. CoRR abs/2209.14125 (2022) - [i45]Kamélia Daudel, Joe Benton, Yuyang Shi, Arnaud Doucet:
Alpha-divergence Variational Inference Meets Importance Weighted Auto-Encoders: Methodology and Asymptotics. CoRR abs/2210.06226 (2022) - [i44]Pierre Glaser, Michael Arbel, Arnaud Doucet, Arthur Gretton:
Maximum Likelihood Learning of Energy-Based Models for Simulation-Based Inference. CoRR abs/2210.14756 (2022) - [i43]Pierre H. Richemond, Sander Dieleman, Arnaud Doucet:
Categorical SDEs with Simplex Diffusion. CoRR abs/2210.14784 (2022) - [i42]Joe Benton, Yuyang Shi, Valentin De Bortoli, George Deligiannidis, Arnaud Doucet:
From Denoising Diffusions to Denoising Markov Models. CoRR abs/2211.03595 (2022) - [i41]Sander Dieleman, Laurent Sartran, Arman Roshannai, Nikolay Savinov, Yaroslav Ganin, Pierre H. Richemond, Arnaud Doucet, Robin Strudel, Chris Dyer, Conor Durkan, Curtis Hawthorne, Rémi Leblond, Will Grathwohl, Jonas Adler:
Continuous diffusion for categorical data. CoRR abs/2211.15089 (2022) - [i40]Angad Singh, Omar Makhlouf, Maximilian Igl, João V. Messias, Arnaud Doucet, Shimon Whiteson:
Particle-Based Score Estimation for State Space Model Learning in Autonomous Driving. CoRR abs/2212.06968 (2022) - 2021
- [j62]Philippe Gagnon, Arnaud Doucet:
Nonreversible Jump Algorithms for Bayesian Nested Model Selection. J. Comput. Graph. Stat. 30(2): 312-323 (2021) - [j61]Vladislav Z. B. Tadic, Arnaud Doucet:
Bias of Particle Approximations to Optimal Filter Derivative. SIAM J. Control. Optim. 59(1): 727-748 (2021) - [j60]Adrian N. Bishop, Arnaud Doucet:
Network Consensus in the Wasserstein Metric Space of Probability Measures. SIAM J. Control. Optim. 59(5): 3261-3277 (2021) - [j59]Chris J. Maddison, Daniel Paulin, Yee Whye Teh, Arnaud Doucet:
Dual Space Preconditioning for Gradient Descent. SIAM J. Optim. 31(1): 991-1016 (2021) - [j58]Vladislav Z. B. Tadic, Arnaud Doucet:
Asymptotic Properties of Recursive Particle Maximum Likelihood Estimation. IEEE Trans. Inf. Theory 67(3): 1825-1848 (2021) - [c102]Soufiane Hayou, Eugenio Clerico, Bobby He, George Deligiannidis, Arnaud Doucet, Judith Rousseau:
Stable ResNet. AISTATS 2021: 1324-1332 - [c101]Soufiane Hayou, Arnaud Doucet, Judith Rousseau:
The Curse of Depth in Kernel Regime. ICBINB@NeurIPS 2021: 41-47 - [c100]Soufiane Hayou, Jean-Francois Ton, Arnaud Doucet, Yee Whye Teh:
Robust Pruning at Initialization. ICLR 2021 - [c99]Liyuan Xu, Yutian Chen, Siddarth Srinivasan, Nando de Freitas, Arnaud Doucet, Arthur Gretton:
Learning Deep Features in Instrumental Variable Regression. ICLR 2021 - [c98]Michael Arbel, Alexander G. de G. Matthews, Arnaud Doucet:
Annealed Flow Transport Monte Carlo. ICML 2021: 318-330 - [c97]Adrien Corenflos, James Thornton, George Deligiannidis, Arnaud Doucet:
Differentiable Particle Filtering via Entropy-Regularized Optimal Transport. ICML 2021: 2100-2111 - [c96]Yangjun Ruan, Karen Ullrich, Daniel Severo, James Townsend, Ashish Khisti, Arnaud Doucet, Alireza Makhzani, Chris J. Maddison:
Improving Lossless Compression Rates via Monte Carlo Bits-Back Coding. ICML 2021: 9136-9147 - [c95]Achille Thin, Nikita Kotelevskii, Arnaud Doucet, Alain Durmus, Eric Moulines, Maxim Panov:
Monte Carlo Variational Auto-Encoders. ICML 2021: 10247-10257 - [c94]Achille Thin, Yazid Janati El Idrissi, Sylvain Le Corff, Charles Ollion, Eric Moulines, Arnaud Doucet, Alain Durmus, Christian X. Robert:
NEO: Non Equilibrium Sampling on the Orbits of a Deterministic Transform. NeurIPS 2021: 17060-17071 - [c93]Valentin De Bortoli, James Thornton, Jeremy Heng, Arnaud Doucet:
Diffusion Schrödinger Bridge with Applications to Score-Based Generative Modeling. NeurIPS 2021: 17695-17709 - [c92]Andrew Campbell, Yuyang Shi, Thomas Rainforth, Arnaud Doucet:
Online Variational Filtering and Parameter Learning. NeurIPS 2021: 18633-18645 - [c91]Anthony L. Caterini, Robert Cornish, Dino Sejdinovic, Arnaud Doucet:
Variational inference with continuously-indexed normalizing flows. UAI 2021: 44-53 - [c90]Francisco J. R. Ruiz, Michalis K. Titsias, A. Taylan Cemgil, Arnaud Doucet:
Unbiased gradient estimation for variational auto-encoders using coupled Markov chains. UAI 2021: 707-717 - [i39]Emilien Dupont, Yee Whye Teh, Arnaud Doucet:
Generative Models as Distributions of Functions. CoRR abs/2102.04776 (2021) - [i38]Michael Arbel, Alexander G. de G. Matthews, Arnaud Doucet:
Annealed Flow Transport Monte Carlo. CoRR abs/2102.07501 (2021) - [i37]Adrien Corenflos, James Thornton, Arnaud Doucet, George Deligiannidis:
Differentiable Particle Filtering via Entropy-Regularized Optimal Transport. CoRR abs/2102.07850 (2021) - [i36]Yangjun Ruan, Karen Ullrich, Daniel Severo, James Townsend, Ashish Khisti, Arnaud Doucet, Alireza Makhzani, Chris J. Maddison:
Improving Lossless Compression Rates via Monte Carlo Bits-Back Coding. CoRR abs/2102.11086 (2021) - [i35]Emilien Dupont, Adam Golinski, Milad Alizadeh, Yee Whye Teh, Arnaud Doucet:
COIN: COmpression with Implicit Neural representations. CoRR abs/2103.03123 (2021) - [i34]Yutian Chen, Liyuan Xu, Çaglar Gülçehre, Tom Le Paine, Arthur Gretton, Nando de Freitas, Arnaud Doucet:
On Instrumental Variable Regression for Deep Offline Policy Evaluation. CoRR abs/2105.10148 (2021) - [i33]Valentin De Bortoli, James Thornton, Jeremy Heng, Arnaud Doucet:
Diffusion Schrödinger Bridge with Applications to Score-Based Generative Modeling. CoRR abs/2106.01357 (2021) - [i32]Eugenio Clerico, George Deligiannidis, Arnaud Doucet:
Wide stochastic networks: Gaussian limit and PAC-Bayesian training. CoRR abs/2106.09798 (2021) - [i31]Achille Thin, Nikita Kotelevskii, Arnaud Doucet, Alain Durmus, Eric Moulines, Maxim Panov:
Monte Carlo Variational Auto-Encoders. CoRR abs/2106.15921 (2021) - [i30]George Deligiannidis, Valentin De Bortoli, Arnaud Doucet:
Quantitative Uniform Stability of the Iterative Proportional Fitting Procedure. CoRR abs/2108.08129 (2021) - [i29]Sahra Ghalebikesabi, Harrison Wilde, Jack Jewson, Arnaud Doucet, Sebastian J. Vollmer, Chris C. Holmes:
Bias Mitigated Learning from Differentially Private Synthetic Data: A Cautionary Tale. CoRR abs/2108.10934 (2021) - [i28]David Stutz, Krishnamurthy Dvijotham, Ali Taylan Cemgil, Arnaud Doucet:
Learning Optimal Conformal Classifiers. CoRR abs/2110.09192 (2021) - [i27]Eugenio Clerico, George Deligiannidis, Arnaud Doucet:
Conditional Gaussian PAC-Bayes. CoRR abs/2110.11886 (2021) - [i26]Andrew Campbell, Yuyang Shi, Tom Rainforth, Arnaud Doucet:
Online Variational Filtering and Parameter Learning. CoRR abs/2110.13549 (2021) - [i25]Valentin De Bortoli, Arnaud Doucet, Jeremy Heng, James Thornton:
Simulating Diffusion Bridges with Score Matching. CoRR abs/2111.07243 (2021) - 2020
- [c89]Robert Cornish, Anthony L. Caterini, George Deligiannidis, Arnaud Doucet:
Relaxing Bijectivity Constraints with Continuously Indexed Normalising Flows. ICML 2020: 2133-2143 - [c88]Yutian Chen, Abram L. Friesen, Feryal M. P. Behbahani, Arnaud Doucet, David Budden, Matthew Hoffman, Nando de Freitas:
Modular Meta-Learning with Shrinkage. NeurIPS 2020 - [i24]Soufiane Hayou, Jean-Francois Ton, Arnaud Doucet, Yee Whye Teh:
Pruning untrained neural networks: Principles and Analysis. CoRR abs/2002.08797 (2020) - [i23]Anthony L. Caterini, Robert Cornish, Dino Sejdinovic, Arnaud Doucet:
Variational Inference with Continuously-Indexed Normalizing Flows. CoRR abs/2007.05426 (2020) - [i22]Francisco J. R. Ruiz, Michalis K. Titsias, A. Taylan Cemgil, Arnaud Doucet:
Unbiased Gradient Estimation for Variational Auto-Encoders using Coupled Markov Chains. CoRR abs/2010.01845 (2020) - [i21]Liyuan Xu, Yutian Chen, Siddarth Srinivasan, Nando de Freitas, Arnaud Doucet, Arthur Gretton:
Learning Deep Features in Instrumental Variable Regression. CoRR abs/2010.07154 (2020) - [i20]Soufiane Hayou, Eugenio Clerico, Bobby He, George Deligiannidis, Arnaud Doucet, Judith Rousseau:
Stable ResNet. CoRR abs/2010.12859 (2020)
2010 – 2019
- 2019
- [j57]Vladislav Z. B. Tadic, Arnaud Doucet:
Analyticity of Entropy Rates of Continuous-State Hidden Markov Models. IEEE Trans. Inf. Theory 65(12): 7950-7975 (2019) - [c87]Sebastian M. Schmon, Arnaud Doucet, George Deligiannidis:
Bernoulli Race Particle Filters. AISTATS 2019: 2350-2358 - [c86]Lawrece Middleton, George Deligiannidis, Arnaud Doucet, Pierre E. Jacob:
Unbiased Smoothing using Particle Independent Metropolis-Hastings. AISTATS 2019: 2378-2387 - [c85]Vladislav Z. B. Tadic, Arnaud Doucet:
Stability of Optimal Filter Higher-Order Derivatives. CDC 2019: 1644-1649 - [c84]Robert Cornish, Paul Vanetti, Alexandre Bouchard-Côté, George Deligiannidis, Arnaud Doucet:
Scalable Metropolis-Hastings for Exact Bayesian Inference with Large Datasets. ICML 2019: 1351-1360 - [c83]Soufiane Hayou, Arnaud Doucet, Judith Rousseau:
On the Impact of the Activation function on Deep Neural Networks Training. ICML 2019: 2672-2680 - [c82]Alex Shestopaloff, Arnaud Doucet:
Replica Conditional Sequential Monte Carlo. ICML 2019: 5749-5757 - [c81]Vladislav Z. B. Tadic, Arnaud Doucet:
Asymptotic Properties of Recursive Particle Maximum Likelihood Estimation. ISIT 2019: 887-891 - [c80]Emilien Dupont, Arnaud Doucet, Yee Whye Teh:
Augmented Neural ODEs. NeurIPS 2019: 3134-3144 - [i19]Robert Cornish, Paul Vanetti, Alexandre Bouchard-Côté, George Deligiannidis, Arnaud Doucet:
Scalable Metropolis-Hastings for Exact Bayesian Inference with Large Datasets. CoRR abs/1901.09881 (2019) - [i18]Soufiane Hayou, Arnaud Doucet, Judith Rousseau:
On the Impact of the Activation Function on Deep Neural Networks Training. CoRR abs/1902.06853 (2019) - [i17]Emilien Dupont, Arnaud Doucet, Yee Whye Teh:
Augmented Neural ODEs. CoRR abs/1904.01681 (2019) - [i16]Soufiane Hayou, Arnaud Doucet, Judith Rousseau:
Training Dynamics of Deep Networks using Stochastic Gradient Descent via Neural Tangent Kernel. CoRR abs/1905.13654 (2019) - [i15]Yutian Chen, Abram L. Friesen, Feryal M. P. Behbahani, David Budden, Matthew W. Hoffman, Arnaud Doucet, Nando de Freitas:
Modular Meta-Learning with Shrinkage. CoRR abs/1909.05557 (2019) - [i14]Robert Cornish, Anthony L. Caterini, George Deligiannidis, Arnaud Doucet:
Localised Generative Flows. CoRR abs/1909.13833 (2019) - 2018
- [c79]Anthony L. Caterini, Arnaud Doucet, Dino Sejdinovic:
Hamiltonian Variational Auto-Encoder. NeurIPS 2018: 8178-8188 - [i13]Soufiane Hayou, Arnaud Doucet, Judith Rousseau:
On the Selection of Initialization and Activation Function for Deep Neural Networks. CoRR abs/1805.08266 (2018) - [i12]Anthony L. Caterini, Arnaud Doucet, Dino Sejdinovic:
Hamiltonian Variational Auto-Encoder. CoRR abs/1805.11328 (2018) - [i11]Vladislav Z. B. Tadic, Arnaud Doucet:
Analyticity of Entropy Rates of Continuous-State Hidden Markov Models. CoRR abs/1806.09589 (2018) - [i10]Chris J. Maddison, Daniel Paulin, Yee Whye Teh, Brendan O'Donoghue, Arnaud Doucet:
Hamiltonian Descent Methods. CoRR abs/1809.05042 (2018) - 2017
- [j56]François Caron, Willie Neiswanger, Frank D. Wood, Arnaud Doucet, Manuel Davy:
Generalized Pólya Urn for Time-Varying Pitman-Yor Processes. J. Mach. Learn. Res. 18: 27:1-27:32 (2017) - [j55]Alexandre Bouchard-Côté, Arnaud Doucet, Andrew Roth:
Particle Gibbs Split-Merge Sampling for Bayesian Inference in Mixture Models. J. Mach. Learn. Res. 18: 28:1-28:39 (2017) - [j54]Rémi Bardenet, Arnaud Doucet, Christopher C. Holmes:
On Markov chain Monte Carlo methods for tall data. J. Mach. Learn. Res. 18: 47:1-47:43 (2017) - [c78]Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Arnaud Doucet, Andriy Mnih, Yee Whye Teh:
Particle Value Functions. ICLR (Workshop) 2017 - [c77]Andrei-Cristian Barbos, Francois Caron,