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Found 9 papers, 0 papers with code
Parallelized Midpoint Randomization for Langevin Monte Carlo
We explore the sampling problem within the framework where parallel evaluations of the gradient of the log-density are feasible.
Guaranteed Optimal Generative Modeling with Maximum Deviation from the Empirical Distribution
Generative modeling is a widely-used machine learning method with various applications in scientific and industrial fields.
Langevin Monte Carlo for strongly log-concave distributions: Randomized midpoint revisited
To provide a more thorough explanation of our method for establishing the computable upper bound, we conduct an analysis of the midpoint discretization for the vanilla Langevin process.
Matching Map Recovery with an Unknown Number of Outliers
If $n$ and $m$ are the sizes of these two sets, we assume that the matching map that should be recovered is defined on a subset of unknown cardinality $k^*\le \min(n, m)$.
Optimal detection of the feature matching map in presence of noise and outliers
The matching map is then an injection, which can be consistently estimated only if the vectors of the second set are well separated.
Statistical guarantees for generative models without domination
In this paper, we introduce a convenient framework for studying (adversarial) generative models from a statistical perspective.
Outlier-robust estimation of a sparse linear model using \ell_1-penalized Huber's M-estimator
We study the problem of estimating a $p$-dimensional $s$-sparse vector in a linear model with Gaussian design.
Fused sparsity and robust estimation for linear models with unknown variance
In this paper, we develop a novel approach to the problem of learning sparse representations in the context of fused sparsity and unknown noise level.
L_1-Penalized Robust Estimation for a Class of Inverse Problems Arising in Multiview Geometry
We propose a new approach to the problem of robust estimation in multiview geometry.
