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Transport maps can ease the sampling of distributions with non-trivial geometries by transforming them into distributions that are easier to handle.
NeuTra-lizing Bad Geometry in Hamiltonian Monte Carlo Using Neural Transport
Hoffman, M. D., Sountsov, P., Dillon, J. V., Langmore, I., Tran, D., and Vasudevan, S · 1903
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Normalizing Constant Estimation with Gaussianized Bridge Sampling, December 2019
Jia, H. and Seljak, U · 1912
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Sampling for bayesian mixture models: Mcmc with polynomial-time mixing, 2019
Mou, W., Ho, N., Wainwright, M. J., Bartlett, P. L., and Jordan, M. I · 1912
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Variational Inference with Normalizing Flows
Rezende, D. and Mohamed, S · 1938
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DeepBAR: A Fast and Exact Method for Binding Free Energy Computation
Ding, X. and Zhang, B · 1948
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Hybrid Monte Carlo
Duane, S., Kennedy, A. D., Pendleton, B. J., and Roweth, D · 1987
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Polar factorization and monotone rearrangement of vector-valued functions
Brenier, Y · 1991
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Inference from Iterative Simulation Using Multiple Sequences
Gelman, A. and Rubin, D. B · 1992
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Random walks in a convex body and an improved volume algorithm
Lovász, L. and Simonovits, M · 1993
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Exponential convergence of langevin distributions and their discrete approximations
Roberts, G. O. and Tweedie, R. L · 1996
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Isoperimetric and Analytic Inequalities for Log-Concave Probability Measures
Bobkov, S. G · 1999
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Hit-and-run mixes fast
Lovász, L · 1999
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The multiple-try method and local optimization in metropolis sampling
Liu, J. S., Liang, F., and Wong, W. H · 2000
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Boosting Monte Carlo simulations of spin glasses using autoregressive neural networks
McNaughton, B., Milošević, M. V., Perali, A., and Pilati, S · 2002
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Using all metropolis–hastings proposals to estimate mean values
Tjelmeland, H · 2004
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Triangular transformations of measures
Bogachev, V. I., Kolesnikov, A. V., and Medvedev, K. V · 2005
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Metastability in reversible diffusion processes ii. precise asymptotics for small eigenvalues
Bovier, A., Klein, M., and Gayrard, V · 2005
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Monte Carlo Statistical Methods (Springer Texts in Statistics)
Robert, C. P. and Casella, G · 2005
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Geometric random walks: a survey
Vempala, S · 2005
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Convergence rate of multiple-try metropolis independent sampler, 2021
Yang, X. and Liu, J. S · 2005
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Sequential Monte Carlo samplers
Del Moral, P., Doucet, A., and Jasra, A · 2006
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Acceleration of the multiple-try metropolis algorithm using antithetic and stratified sampling
Craiu, R. V. and Lemieux, C · 2007
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The geometry of logconcave functions and sampling algorithms
Lovász, L. and Vempala, S · 2007
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Handling sparsity via the horseshoe
Carvalho, C. M., Polson, N. G., and Scott, J. G · 2009
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Convergence of gibbs sampling: Coordinate hit-and-run mixes fast, 2020
Laddha, A. and Vempala, S · 2009
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Trivializing Maps, the Wilson Flow and the HMC Algorithm
Lüscher, M · 2009
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Particle Markov chain Monte Carlo methods
Andrieu, C., Doucet, A., and Holenstein, R · 2010
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Elliptical slice sampling
Murray, I., Adams, R., and MacKay, D · 2010
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Density estimation by dual ascent of the log-likelihood
Tabak, E. G. and Vanden-Eijnden, E · 2010
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Importance sampling: a review
Tokdar, S. T. and Kass, R. E · 2010
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Riemann manifold langevin and hamiltonian monte carlo methods
Girolami, M. and Calderhead, B · 2011
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Mcmc using hamiltonian dynamics
Neal, R. M. et al · 2011
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Quantitative non-geometric convergence bounds for independence samplers
Normalizing flows: An introduction and review of current methods
Kobyzev, I., Prince, S. J., and Brubaker, M. A · 2020
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Targeted free energy estimation via learned mappings
Wirnsberger, P., Ballard, A. J., Papamakarios, G., Abercrombie, S., Racanière, S., Pritzel, A., Jimenez Rezende, D., and Blundell, C · 2020
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Annealed Flow Transport Monte Carlo
Arbel, M., Matthews, A., and Doucet, A · 2021
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Exact convergence analysis for metropolis-hastings independence samplers in wasserstein distances, 2021
Brown, A. and Jones, G. L · 2021
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Optimal dimension dependence of the metropolis-adjusted langevin algorithm
Chewi, S., Lu, C., Ahn, K., Cheng, X., Gouic, T. L., and Rigollet, P · 2021
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Roberts, G. O. and Rosenthal, J. S · 2011
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Adam: A method for stochastic optimization, 2014
Kingma, D. P. and Ba, J · 2014
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Density estimation using real nvp, 2016
Dinh, L., Sohl-Dickstein, J., and Bengio, S · 2016
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Importance sampling: Intrinsic dimension and computational cost
Agapiou, S., Papaspiliopoulos, O., Sanz-Alonso, D., and Stuart, A. M · 2017
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UCI machine learning repository, 2017
Dua, D. and Graff, C · 2017
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Masked autoregressive flow for density estimation
Papamakarios, G., Pavlakou, T., and Murray, I · 2017
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Del Debbio, L., Rossney, J. M., and Wilson, M · 2021
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Flow-based sampling for multimodal distributions in lattice field theory, July 2021
Hackett, D. C., Hsieh, C.-C., Albergo, M. S., Boyda, D., Chen, J.-W., Chen, K.-F., Cranmer, K., Kanwar, G., and Shanahan, P. E · 2021
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Variational refinement for importance sampling using the forward kullback-leibler divergence
Jerfel, G., Wang, S., Wong-Fannjiang, C., Heller, K. A., Ma, Y., and Jordan, M. I · 2021
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Geometric convergence of elliptical slice sampling
Natarovskii, V., Rudolf, D., and Sprungk, B · 2021
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Mcmc should mix: Learning energy-based model with neural transport latent space mcmc
Nijkamp, E., Gao, R., Sountsov, P., Vasudevan, S., Pang, B., Zhu, S.-C., and Wu, Y. N · 2021
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Normalizing flows for probabilistic modeling and inference
Papamakarios, G., Nalisnick, E., Rezende, D. J., Mohamed, S., and Lakshminarayanan, B · 2021
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Transport elliptical slice sampling, 2022
Cabezas, A. and Nemeth, C · 2022
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Adaptive monte carlo augmented with normalizing flows
Gabrié, M., Rotskoff, G. M., and Vanden-Eijnden, E · 2022
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Deterministic langevin monte carlo with normalizing flows for bayesian inference, 2022
Grumitt, R. D. P., Dai, B., and Seljak, U · 2022
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Skipping the Replica Exchange Ladder with Normalizing Flows
Invernizzi, M., Krämer, A., Clementi, C., and Noé, F · 2022
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Accelerating astronomical and cosmological inference with Preconditioned Monte Carlo
Karamanis, M., Beutler, F., Peacock, J. A., Nabergoj, D., and Seljak, U · 2022
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Accurate Sampling of Macromolecular Conformations Using Adaptive Deep Learning and Coarse-Grained Representation
Mahmoud, A. H., Masters, M., Lee, S. J., and Lill, M. A · 2022
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Flow annealed importance sampling bootstrap, 2022
Midgley, L. I., Stimper, V., Simm, G. N. C., Schölkopf, B., and Hernández-Lobato, J. M · 2022
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On the mixing time of coordinate hit-and-run
Narayanan, H. and Srivastava, P · 2022
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Machine Learning of Thermodynamic Observables in the Presence of Mode Collapse
Nicoli, K. A., Anders, C., Funcke, L., Hartung, T., Jansen, K., Kessel, P., Nakajima, S., and Stornati, P · 2022
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Local-global mcmc kernels: the best of both worlds
Samsonov, S., Lagutin, E., Gabrié, M., Durmus, A., Naumov, A., and Moulines, E · 2022
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Alanine dipeptide in an implicit solvent at 300k, August 2022
Stimper, V., Midgley, L. I., Simm, G. N. C., Schölkopf, B., and Hernández-Lobato, J. M · 2022
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Exact convergence analysis of the independent Metropolis-Hastings algorithms, 2022
Wang, G · 2022
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flowMC: Normalizing-flow enhanced sampling package for probabilistic inference in Jax, November 2022
Wong, K. W. K., Gabrié, M., and Foreman-Mackey, D · 2022
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Minimax mixing time of the metropolis-adjusted langevin algorithm for log-concave sampling
Wu, K., Schmidler, S., and Chen, Y · 2022
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