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Found 15 papers, 1 papers with code
On Convergence of the Alternating Directions SGHMC Algorithm
We study convergence rates of Hamiltonian Monte Carlo (HMC) algorithms with leapfrog integration under mild conditions on stochastic gradient oracle for the target distribution (SGHMC).
Obtaining Explainable Classification Models using Distributionally Robust Optimization
Model explainability is crucial for human users to be able to interpret how a proposed classifier assigns labels to data based on its feature values.
On Representations of Mean-Field Variational Inference
We present a framework to analyze MFVI algorithms, which is inspired by a similar development for general variational Bayesian formulations.
A Class of Geometric Structures in Transfer Learning: Minimax Bounds and Optimality
In contrast, our study first illustrates the benefits of incorporating a natural geometric structure within a linear regression model, which corresponds to the generalized eigenvalue problem formed by the Gram matrices of both domains.
Polynomial convergence of iterations of certain random operators in Hilbert space
We study the convergence of a random iterative sequence of a family of operators on infinite dimensional Hilbert spaces, inspired by the Stochastic Gradient Descent (SGD) algorithm in the case of the noiseless regression, as studied in [1].
Efficient Generalization with Distributionally Robust Learning
Distributionally robust learning (DRL) is increasingly seen as a viable method to train machine learning models for improved model generalization.
Hamiltonian Monte Carlo with Asymmetrical Momentum Distributions
Existing rigorous convergence guarantees for the Hamiltonian Monte Carlo (HMC) algorithm use Gaussian auxiliary momentum variables, which are crucially symmetrically distributed.
EventGraD: Event-Triggered Communication in Parallel Machine Learning
To relieve some of this overhead, in this paper, we present EventGraD - an algorithm with event-triggered communication for stochastic gradient descent in parallel machine learning.
HMC, an Algorithms in Data Mining, the Functional Analysis approach
The main purpose of this paper is to facilitate the communication between the Analytic, Probabilistic and Algorithmic communities.
On $L^q$ Convergence of the Hamiltonian Monte Carlo
We establish $L_q$ convergence for Hamiltonian Monte Carlo algorithms.
Unbiased Gradient Estimation for Distributionally Robust Learning
Seeking to improve model generalization, we consider a new approach based on distributionally robust learning (DRL) that applies stochastic gradient descent to the outer minimization problem.
Quantifying the Empirical Wasserstein Distance to a Set of Measures: Beating the Curse of Dimensionality
We consider the problem of estimating the Wasserstein distance between the empirical measure and a set of probability measures whose expectations over a class of functions (hypothesis class) are constrained.
Efficient Stochastic Gradient Descent for Learning with Distributionally Robust Optimization
Distributionally robust optimization (DRO) problems are increasingly seen as a viable method to train machine learning models for improved model generalization.
Slow and Stale Gradients Can Win the Race: Error-Runtime Trade-offs in Distributed SGD
Distributed Stochastic Gradient Descent (SGD) when run in a synchronous manner, suffers from delays in waiting for the slowest learners (stragglers).
Efficient Estimation in the Tails of Gaussian Copulas
The first of our proposed estimators $\estOpt$ is the "full-information" estimator that actively exploits such local structure to achieve bounded relative error in Gaussian settings.
