Fetching the paper…
Reading the bibliography…
We consider the optimization problem of minimizing a functional defined over a family of probability distributions, where the objective functional is assumed to possess a variational form.
Is there an analog of Nesterov acceleration for MCMC?
Ma, Y.-A · 1902
Earlier work this paper cites.
A selective overview of deep learning
Fan, J · 1904
Earlier work this paper cites.
Linear convergence of accelerated conditional gradient algorithms in spaces of measures
Pieper, K · 1904
Earlier work this paper cites.
An introduction to variational autoencoders
Kingma, D. P · 1906
Earlier work this paper cites.
Improved bounds for discretization of Langevin diffusions: Near-optimal rates without convexity
Mou, W · 1907
Earlier work this paper cites.
Distributionally robust optimization: A review
Rahimian, H · 1908
Earlier work this paper cites.
Projection-free nonconvex stochastic optimization on Riemannian manifolds
Weber, M · 1910
Earlier work this paper cites.
Proximal Langevin algorithm: Rapid convergence under isoperimetry
Wibisono, A · 1911
Earlier work this paper cites.
On the geometry of Stein variational gradient descent
Duncan, A · 1912
Earlier work this paper cites.
The theory of probabilities
Bernstein, S · 1946
Earlier work this paper cites.
Gradient methods for minimizing functionals
Polyak, B. T · 1963
Earlier work this paper cites.
Information-type measures of difference of probability distributions and indirect observation
Csiszár, I · 1967
Earlier work this paper cites.
Logarithmic Sobolev inequalities
Gross, L · 1975
Earlier work this paper cites.
Principles of mathematical analysis
Rudin, W · 1976
Earlier work this paper cites.
Axiomatic derivation of the principle of maximum entropy and the principle of minimum cross-entropy
Shore, J · 1980
Earlier work this paper cites.
Infinite-dimensional optimization and convexity
Ekeland, I · 1983
Earlier work this paper cites.
The principle of maximum entropy
Guiasu, S · 1985
Earlier work this paper cites.
Linearization methods for optimization of functionals which depend on probability measures
Gaivoronski, A · 1986
Earlier work this paper cites.
Asymptotics of Varadhan-type and the Gibbs variational principle
Petz, D · 1989
Earlier work this paper cites.
Asymptotic analysis of penalized likelihood and related estimators
Cox, D. D · 1990
Earlier work this paper cites.
Riemannian geometry
do Carmo, M. P · 1992
Earlier work this paper cites.
Convex functions and optimization methods on Riemannian manifolds
Udriste, C · 1994
Earlier work this paper cites.
Markov chain Monte Carlo in practice
Gilks, W. R · 1995
Earlier work this paper cites.
Integral probability metrics and their generating classes of functions
Müller, A · 1997
Earlier work this paper cites.
The variational formulation of the Fokker–Planck equation
Jordan, R · 1998
Earlier work this paper cites.
Lectures on differential geometry
Chern, S.-S · 1999
Earlier work this paper cites.
Infinite dimensional optimization and control theory
Fattorini, H. O · 1999
Earlier work this paper cites.
Variational calculus in the space of measures and optimal design
Molchanov, I · 2000
Earlier work this paper cites.
Generalization of an inequality by Talagrand and links with the logarithmic Sobolev inequality
Otto, F · 2000
Earlier work this paper cites.
Structured semidefinite programs and semialgebraic geometry methods in robustness and optimization
Parrilo, P. A · 2000
Earlier work this paper cites.
Policy gradient methods for reinforcement learning with function approximation
Sutton, R. S · 2000
Earlier work this paper cites.
Course in metric geometry
Burago, D · 2001
Earlier work this paper cites.
The elements of statistical learning
Friedman, J · 2001
Earlier work this paper cites.
Global optimization with polynomials and the problem of moments
Lasserre, J. B · 2001
Earlier work this paper cites.
Polar factorization of maps on Riemannian manifolds
McCann, R. J · 2001
Earlier work this paper cites.
The geometry of dissipative evolution equations: the porous medium equation
Otto, F · 2001
Earlier work this paper cites.
Finite-time analysis of the multi-armed bandit problem
Auer, P · 2002
Earlier work this paper cites.
Proximal point algorithm on riemannian manifolds
Ferreira, O · 2002
Earlier work this paper cites.
Steepest descent algorithms in a space of measures
Molchanov, I · 2002
Earlier work this paper cites.
Stein self-repulsive dynamics: Benefits from past samples
Ye, M · 2002
Earlier work this paper cites.
Constrained steepest descent in the 2-Wasserstein metric
Carlen, E. A · 2003
Earlier work this paper cites.
Estimating the approximation error in learning theory
Smale, S · 2003
Earlier work this paper cites.
Topics in optimal transportation
Villani, C · 2003
Earlier work this paper cites.
Optimisation in space of measures and optimal design
Molchanov, I · 2004
Earlier work this paper cites.
Kernel methods for pattern analysis
Shawe-Taylor, J · 2004
Earlier work this paper cites.
A new geometric condition for Fenchel’s duality in infinite dimensional spaces
Burachik, R. S · 2005
Earlier work this paper cites.
Exploration-exploitation tradeoffs for experts algorithms in reactive environments
Farias, D. D · 2005
Earlier work this paper cites.
SVGD as a kernelized Wasserstein gradient flow of the chi-squared divergence
Chewi, S · 2006
Earlier work this paper cites.
A non-asymptotic analysis for Stein variational gradient descent
Korba, A · 2006
Earlier work this paper cites.
Riemannian geometry
Petersen, P · 2006
Cited alongside, same era.
A semidefinite programming approach to the generalized problem of moments
Lasserre, J. B · 2008
Cited alongside, same era.
Support vector machines
Steinwart, I · 2008
Cited alongside, same era.
Optimal transport: old and new
Villani, C · 2008
Cited alongside, same era.
Graphical models, exponential families, and variational inference
Wainwright, M. J · 2008
Cited alongside, same era.
Optimization algorithms on matrix manifolds
Absil, P.-A · 2009
Cited alongside, same era.
Approximate volume and integration for basic semialgebraic sets
Henrion, D · 2009
Further and stronger analogy between sampling and optimization: Langevin Monte Carlo and gradient descent
Dalalyan, A · 2017
Later among the works it cites.
Sobolev norm learning rates for regularized least-squares algorithm
Fischer, S · 2017
Later among the works it cites.
MMD GAN: Towards deeper understanding of moment matching network
Li, C.-L · 2017
Later among the works it cites.
Stein variational gradient descent as gradient flow
Liu, Q · 2017
Later among the works it cites.
Accelerated first-order methods for geodesically convex optimization on Riemannian manifolds
Liu, Y · 2017
Later among the works it cites.
Fisher GAN
Mroueh, Y · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Moments and sums of squares for polynomial optimization and related problems
Lasserre, J. B · 2009
Cited alongside, same era.
A unique solution to a nonlinear elliptic equation
Denny, D · 2010
Cited alongside, same era.
Partial differential equations
Evans, L · 2010
Cited alongside, same era.
Moments, positive polynomials and their applications
Lasserre, J.-B · 2010
Cited alongside, same era.
Estimating divergence functionals and the likelihood ratio by convex risk minimization
Nguyen, X · 2010
Cited alongside, same era.
Non-convex learning via stochastic gradient Langevin dynamics: a nonasymptotic analysis
Raginsky, M · 2017
Later among the works it cites.
{ \{ Euclidean, metric, and Wasserstein } \} gradient flows: an overview
Santambrogio, F · 2017
Later among the works it cites.
Equivalence between policy gradients and soft Q-learning
Schulman, J · 2017
Later among the works it cites.
Adaptive regularization with cubics on manifolds
Agarwal, N · 2018
Later among the works it cites.
Riemannian adaptive optimization methods
Bécigneul, G · 2018
Later among the works it cites.
Langevin Monte Carlo and JKO splitting
Bernton, E · 2018
Later among the works it cites.
Global rates of convergence for nonconvex optimization on manifolds
Boumal, N · 2018
Later among the works it cites.
The promises and pitfalls of stochastic gradient Langevin dynamics
Brosse, N · 2018
Later among the works it cites.
On the theory of variance reduction for stochastic gradient Monte Carlo
Chatterji, N · 2018
Later among the works it cites.
A unified particle-optimization framework for scalable Bayesian sampling
Chen, C · 2018
Later among the works it cites.
Convergence of Langevin MCMC in KL-divergence
Cheng, X · 2018
Later among the works it cites.
A Stein variational Newton method
Detommaso, G · 2018
Later among the works it cites.
Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Haarnoja, T · 2018
Later among the works it cites.
Stein variational gradient descent without gradient
Han, J · 2018
Later among the works it cites.
Moment-sum-of-squares approach for fast risk estimation in uncertain environments
Jasour, A · 2018
Later among the works it cites.
Sobolev GAN
Mroueh, Y · 2018
Later among the works it cites.
Reproducing kernels of Sobolev spaces on ℝ d \mathbb{R}^{d} and applications to embedding constants and tractability
Novak, E · 2018
Later among the works it cites.
Reinforcement learning: An introduction
Sutton, R. S · 2018
Later among the works it cites.
Averaging stochastic gradient descent on Riemannian manifolds
Tripuraneni, N · 2018
Later among the works it cites.
Sampling as optimization in the space of measures: The Langevin dynamics as a composite optimization problem
Wibisono, A · 2018
Later among the works it cites.
Global convergence of Langevin dynamics based algorithms for nonconvex optimization
Xu, P · 2018
Later among the works it cites.
An estimate sequence for geodesically convex optimization
Zhang, H · 2018
Later among the works it cites.
R-SPIDER: A fast Riemannian stochastic optimization algorithm with curvature independent rate
Zhang, J · 2018
Later among the works it cites.
Maximum mean discrepancy gradient flow
Arbel, M · 2019
Later among the works it cites.
Control variates for stochastic gradient MCMC
Baker, J · 2019
Later among the works it cites.
Probability functional descent: A unifying perspective on GANs, variational inference, and reinforcement learning
Chu, C · 2019
Later among the works it cites.
Modified log-Sobolev inequalities for strongly log-concave distributions
Cryan, M · 2019
Later among the works it cites.
User-friendly guarantees for the Langevin Monte Carlo with inaccurate gradient
Dalalyan, A. S · 2019
Later among the works it cites.
High-dimensional Bayesian inference via the unadjusted Langevin algorithm
Durmus, A · 2019
Later among the works it cites.
Bayesian posterior approximation via greedy particle optimization
Futami, F · 2019
Later among the works it cites.
Quantile Stein variational gradient descent for batch Bayesian optimization
Gong, C · 2019
Later among the works it cites.
Understanding and accelerating particle-based variational inference
Liu, C · 2019
Later among the works it cites.
Stochastic proximal Langevin algorithm: Potential splitting and nonasymptotic rates
Salim, A · 2019
Later among the works it cites.
Riemannian stochastic variance reduced gradient algorithm with retraction and vector transport
Sato, H · 2019
Later among the works it cites.
Nonparametric density estimation & convergence rates for GANs under Besov IPM losses
Uppal, A · 2019
Later among the works it cites.
Rapid convergence of the unadjusted Langevin algorithm: Isoperimetry suffices
Vempala, S · 2019
Later among the works it cites.
Stein variational gradient descent with matrix-valued kernels
Wang, D · 2019
Later among the works it cites.
Faster first-order methods for stochastic non-convex optimization on Riemannian manifolds
Zhou, P · 2019
Later among the works it cites.
First order methods for optimization on Riemannian manifolds
Ferreira, O. P · 2020
Closest in time.
Approximate inference with Wasserstein gradient flows
Frogner, C · 2020
Closest in time.
Recent advances in stochastic Riemannian optimization
Hosseini, R · 2020
Closest in time.
Stochastic particle-optimization sampling and the non-asymptotic convergence theory
Zhang, J · 2020
Closest in time.