51 citations · 116 across the 31 of their papers we have counts for
10 papers · 1 filter
Constrained Sampling with Primal-Dual Langevin Monte Carlo
Luiz F. O. Chamon, Mohammad Reza Karimi, Anna Korba
This work considers the problem of sampling from a probability distribution known up to a normalization constant while satisfying a set of statistical constraints specified by the…
Provable Convergence and Limitations of Geometric Tempering for Langevin Dynamics
Omar Chehab, Anna Korba, Austin Stromme +1
Geometric tempering is a popular approach to sampling from challenging multi-modal probability distributions by instead sampling from a sequence of distributions which interpolate,…
(De)-regularized Maximum Mean Discrepancy Gradient Flow
Zonghao Chen, Aratrika Mustafi, Pierre Glaser +3
We introduce a (de)-regularization of the Maximum Mean Discrepancy (DrMMD) and its Wasserstein gradient flow. Existing gradient flows that transport samples from source distributio…
Statistical and Geometrical properties of regularized Kernel Kullback-Leibler divergence
Clémentine Chazal, Anna Korba, Francis Bach
In this paper, we study the statistical and geometrical properties of the Kullback-Leibler divergence with kernel covariance operators (KKL) introduced by Bach [2022]. Unlike the c…
A Practical Diffusion Path for Sampling
Omar Chehab, Anna Korba
Diffusion models are state-of-the-art methods in generative modeling when samples from a target probability distribution are available, and can be efficiently sampled, using score…
Theoretical Guarantees for Variational Inference with Fixed-Variance Mixture of Gaussians
Tom Huix, Anna Korba, Alain Durmus +1
Variational inference (VI) is a popular approach in Bayesian inference, that looks for the best approximation of the posterior distribution within a parametric family, minimizing a…
Unified PAC-Bayesian Study of Pessimism for Offline Policy Learning with Regularized Importance Sampling
Imad Aouali, Victor-Emmanuel Brunel, David Rohde +1
Off-policy learning (OPL) often involves minimizing a risk estimator based on importance weighting to correct bias from the logging policy used to collect data. However, this metho…
Mirror and Preconditioned Gradient Descent in Wasserstein Space
Clément Bonet, Théo Uscidda, Adam David +2
As the problem of minimizing functionals on the Wasserstein space encompasses many applications in machine learning, different optimization algorithms on have receiv…
Bayesian Off-Policy Evaluation and Learning for Large Action Spaces
Imad Aouali, Victor-Emmanuel Brunel, David Rohde +1
In interactive systems, actions are often correlated, presenting an opportunity for more sample-efficient off-policy evaluation (OPE) and learning (OPL) in large action spaces. We…
Implicit Diffusion: Efficient Optimization through Stochastic Sampling
Pierre Marion, Anna Korba, Peter Bartlett +6
We present a new algorithm to optimize distributions defined implicitly by parameterized stochastic diffusions. Doing so allows us to modify the outcome distribution of sampling pr…