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Found 16 papers, 7 papers with code
Kernel Stein Discrepancy Descent
We investigate the properties of its Wasserstein gradient flow to approximate a target probability distribution $\pi$ on $\mathbb{R}^d$, known up to a normalization constant.
A Structured Prediction Approach for Label Ranking
We propose to solve a label ranking problem as a structured output regression task.
Maximum Mean Discrepancy Gradient Flow
We construct a Wasserstein gradient flow of the maximum mean discrepancy (MMD) and study its convergence properties.
Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction
In particular, we provide a unifying view of two-stage and moment restriction approaches for solving this problem in a nonlinear setting.
Sampling with Mollified Interaction Energy Descent
These energies rely on mollifier functions -- smooth approximations of the Dirac delta originated from PDE theory.
Variational Inference of overparameterized Bayesian Neural Networks: a theoretical and empirical study
This paper studies the Variational Inference (VI) used for training Bayesian Neural Networks (BNN) in the overparameterized regime, i. e., when the number of neurons tends to infinity.
Ranking Median Regression: Learning to Order through Local Consensus
In the probabilistic formulation of the 'Learning to Order' problem we propose, which extends the framework for statistical Kemeny ranking aggregation developped in \citet{CKS17}, this boils down to recovering conditional Kemeny medians of $\Sigma$ given $X$ from i. i. d.
Dimensionality Reduction and (Bucket) Ranking: a Mass Transportation Approach
Whereas most dimensionality reduction techniques (e. g. PCA, ICA, NMF) for multivariate data essentially rely on linear algebra to a certain extent, summarizing ranking data, viewed as realizations of a random permutation $\Sigma$ on a set of items indexed by $i\in \{1,\ldots,\; n\}$, is a great statistical challenge, due to the absence of vector space structure for the set of permutations $\mathfrak{S}_n$.
The Wasserstein Proximal Gradient Algorithm
Using techniques from convex optimization and optimal transport, we analyze the FB scheme as a minimization algorithm on the Wasserstein space.
A Non-Asymptotic Analysis for Stein Variational Gradient Descent
We study the Stein Variational Gradient Descent (SVGD) algorithm, which optimises a set of particles to approximate a target probability distribution $\pi\propto e^{-V}$ on $\mathbb{R}^d$.
Adaptive Importance Sampling meets Mirror Descent: a Bias-variance tradeoff
Adaptive importance sampling is a widely spread Monte Carlo technique that uses a re-weighting strategy to iteratively estimate the so-called target distribution.
Mirror Descent with Relative Smoothness in Measure Spaces, with application to Sinkhorn and EM
We also show that Expectation Maximization (EM) can always formally be written as a mirror descent.
Exponential Smoothing for Off-Policy Learning
In particular, it is also valid for standard IPS without making the assumption that the importance weights are bounded.
A connection between Tempering and Entropic Mirror Descent
We establish that tempering SMC corresponds to entropic mirror descent applied to the reverse Kullback-Leibler (KL) divergence and obtain convergence rates for the tempering iterates.
Implicit Diffusion: Efficient Optimization through Stochastic Sampling
We present a new algorithm to optimize distributions defined implicitly by parameterized stochastic diffusions.
Bayesian Off-Policy Evaluation and Learning for Large Action Spaces
In this framework, we propose sDM, a generic Bayesian approach designed for OPE and OPL, grounded in both algorithmic and theoretical foundations.
