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Found 8 papers, 1 papers with code
Sampling in Constrained Domains with Orthogonal-Space Variational Gradient Descent
Sampling methods, as important inference and learning techniques, are typically designed for unconstrained domains.
Can We Do Better Than Random Start? The Power of Data Outsourcing
Based on three different scenarios, we propose simulation-based algorithms that can utilize a small amount of outsourced data to find good initial points accordingly.
Stochastic Gradient Descent with Dependent Data for Offline Reinforcement Learning
The policy evaluation algorithm is then combined with the policy iteration algorithm to learn the optimal policy.
Consistency analysis of bilevel data-driven learning in inverse problems
One fundamental problem when solving inverse problems is how to find regularization parameters.
Dimension Independent Generalization Error by Stochastic Gradient Descent
One classical canon of statistics is that large models are prone to overfitting, and model selection procedures are necessary for high dimensional data.
Replica Exchange for Non-Convex Optimization
Gradient descent (GD) is known to converge quickly for convex objective functions, but it can be trapped at local minima.
On Stationary-Point Hitting Time and Ergodicity of Stochastic Gradient Langevin Dynamics
In this paper, using intuitions from stochastic differential equations, we provide a direct analysis for the hitting times of SGLD to the first and second order stationary points.
Statistical Inference for Model Parameters in Stochastic Gradient Descent
Second, for high-dimensional linear regression, using a variant of the SGD algorithm, we construct a debiased estimator of each regression coefficient that is asymptotically normal.
