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We analyze the optimized adaptive importance sampler (OAIS) for performing Monte Carlo integration with general proposals.
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2007
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2008
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Olivier Cappé, Randal Douc, Arnaud Guillin, Jean-Michel Marin, and Christian P Robert, Adaptive importance sampling in general mixture classes , Statistics and Computing 18
2008
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Mónica F Bugallo, Luca Martino, and Jukka Corander, Adaptive importance sampling in signal processing , Digital Signal Processing 47
2015
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Luca Martino, Victor Elvira, David Luengo, and Jukka Corander, An adaptive population importance sampler: Learning from uncertainty , IEEE Transactions on Signal Processing 63
2015
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2016
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Ernest K Ryu, Convex optimization for monte carlo: Stochastic optimization for importance sampling , Ph.D. thesis, Stanford University, 2016
2016
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2017
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Monica F Bugallo, Victor Elvira, Luca Martino, David Luengo, Joaquin Miguez, and Petar M Djuric, Adaptive Importance Sampling: The past, the present, and the future , IEEE Signal Processing Magazine 34
2017
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Adji Bousso Dieng, Dustin Tran, Rajesh Ranganath, John Paisley, and David Blei, Variational inference via χ \chi -upper bound minimization , Advances in Neural Information Processing Systems, 2017, pp. 2732–2741
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Víctor Elvira, Luca Martino, David Luengo, and Mónica F Bugallo, Improving population monte carlo: Alternative weighting and resampling schemes , Signal Processing 131
2017
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by same author, Acceleration on adaptive importance sampling with sample average approximation , SIAM Journal on Scientific Computing 39
2017
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Luca Martino, Victor Elvira, and David Luengo, Anti-tempered layered adaptive importance sampling , 2017 22nd International Conference on Digital Signal Processing (DSP), IEEE, 2017, pp. 1–5
2017
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by same author, Layered adaptive importance sampling , Statistics and Computing 27
2017
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Maxim Raginsky, Alexander Rakhlin, and Matus Telgarsky, Non-convex learning via stochastic gradient Langevin dynamics: a nonasymptotic analysis , Conference on Learning Theory, 2017, pp. 1674–1703
Difan Zou, Pan Xu, and Quanquan Gu, Stochastic gradient Hamiltonian Monte Carlo methods with recursive variance reduction , Advances in Neural Information Processing Systems 32
2019
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2020
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Romain Lopez, Pierre Boyeau, Nir Yosef, Michael Jordan, and Jeffrey Regier, Decision-making with auto-encoding variational bayes , Advances in Neural Information Processing Systems 33
2020
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Ömer Deniz Akyildiz and Joaquín Míguez, Convergence rates for optimised adaptive importance samplers , Statistics and Computing 31
2021
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2017
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Nicolas Brosse, Alain Durmus, and Eric Moulines, The promises and pitfalls of stochastic gradient langevin dynamics , Advances in Neural Information Processing Systems 31
2018
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Bernard Delyon and François Portier, Asymptotic optimality of adaptive importance sampling , Proceedings of the 32nd International Conference on Neural Information Processing Systems, 2018, pp. 3138–3148
2018
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2018
Cited alongside, same era.
Matteo Fasiolo, Flávio Eler de Melo, and Simon Maskell, Langevin incremental mixture importance sampling , Statistics and Computing 28
2018
Cited alongside, same era.
by same author, Optimizing adaptive importance sampling by stochastic approximation , SIAM Journal on Scientific Computing 40
2018
Cited alongside, same era.
Daniel Sanz-Alonso, Importance sampling and necessary sample size: an information theory approach , SIAM/ASA Journal on Uncertainty Quantification 6
2018
Cited alongside, same era.
Pan Xu, Jinghui Chen, Difan Zou, and Quanquan Gu, Global convergence of langevin dynamics based algorithms for nonconvex optimization , Advances in Neural Information Processing Systems (NeurIPS) (2018)
2018
Cited alongside, same era.
Ngoc Huy Chau, Éric Moulines, Miklos Rásonyi, Sotirios Sabanis, and Ying Zhang, On stochastic gradient langevin dynamics with dependent data streams: The fully nonconvex case , SIAM Journal on Mathematics of Data Science 3
2021
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by same author, Policy gradient importance sampling for bayesian inference , IEEE Transactions on Signal Processing 69
2021
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Víctor Elvira and Emilie Chouzenoux, Optimized population monte carlo , (2021)
2021
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Xuefeng Gao, Mert Gürbüzbalaban, and Lingjiong Zhu, Global convergence of stochastic gradient hamiltonian monte carlo for nonconvex stochastic optimization: Nonasymptotic performance bounds and momentum-based acceleration , Operations Research (2021)
2021
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Ghassen Jerfel, Serena Wang, Clara Wong-Fannjiang, Katherine A Heller, Yian Ma, and Michael I Jordan, Variational refinement for importance sampling using the forward kullback-leibler divergence , Uncertainty in Artificial Intelligence, PMLR, 2021, pp. 1819–1829
2021
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2021
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2021
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2021
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Ali Mousavi, Reza Monsefi, and Víctor Elvira, Hamiltonian adaptive importance sampling , IEEE Signal Processing Letters 28
2021
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Daniel Sanz-Alonso and Zijian Wang, Bayesian update with importance sampling: Required sample size , Entropy 23
2021
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by same author, Faster convergence of stochastic gradient langevin dynamics for non-log-concave sampling , Uncertainty in Artificial Intelligence, PMLR, 2021, pp. 1152–1162
2021
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2022
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Víctor Elvira, Luca Martino, and Christian P Robert, Rethinking the effective sample size , International Statistical Review 90
2022
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2023
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Ying Zhang, Ömer Deniz Akyildiz, Theodoros Damoulas, and Sotirios Sabanis, Nonasymptotic estimates for stochastic gradient langevin dynamics under local conditions in nonconvex optimization , Applied Mathematics & Optimization 87
2023
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