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Found 10 papers, 2 papers with code
On Last-Layer Algorithms for Classification: Decoupling Representation from Uncertainty Estimation
Uncertainty quantification for deep learning is a challenging open problem.
On Sampling with Approximate Transport Maps
Transport maps can ease the sampling of distributions with non-trivial geometries by transforming them into distributions that are easier to handle.
Low-rank Interaction with Sparse Additive Effects Model for Large Data Frames
In this paper, we introduce a low-rank interaction and sparse additive effects (LORIS) model which combines matrix regression on a dictionary and low-rank design, to estimate main effects and interactions simultaneously.
On stochastic gradient Langevin dynamics with dependent data streams: the fully non-convex case
We consider the problem of sampling from a target distribution, which is \emph {not necessarily logconcave}, in the context of empirical risk minimization and stochastic optimization as presented in Raginsky et al. (2017).
Unifying mirror descent and dual averaging
We introduce and analyze a new family of first-order optimization algorithms which generalizes and unifies both mirror descent and dual averaging.
Convergence Analysis of Riemannian Stochastic Approximation Schemes
This paper analyzes the convergence for a large class of Riemannian stochastic approximation (SA) schemes, which aim at tackling stochastic optimization problems.
Counterfactual Credit Assignment in Model-Free Reinforcement Learning
Credit assignment in reinforcement learning is the problem of measuring an action's influence on future rewards.
On Riemannian Stochastic Approximation Schemes with Fixed Step-Size
This result gives rise to a family of stationary distributions indexed by the step-size, which is further shown to converge to a Dirac measure, concentrated at the solution of the problem at hand, as the step-size goes to 0.
Balanced Training of Energy-Based Models with Adaptive Flow Sampling
Energy-based models (EBMs) are versatile density estimation models that directly parameterize an unnormalized log density.
Law of Large Numbers for Bayesian two-layer Neural Network trained with Variational Inference
We provide a rigorous analysis of training by variational inference (VI) of Bayesian neural networks in the two-layer and infinite-width case.
