Search Results for author:
Found 9 papers, 0 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).
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.
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].
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.
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.
A Family of Robust Stochastic Operators for Reinforcement Learning
We consider a new family of stochastic operators for reinforcement learning with the goal of alleviating negative effects and becoming more robust to approximation or estimation errors.
A General Markov Decision Process Framework for Directly Learning Optimal Control Policies
We consider a new form of reinforcement learning (RL) that is based on opportunities to directly learn the optimal control policy and a general Markov decision process (MDP) framework devised to support these opportunities.
A General Family of Robust Stochastic Operators for Reinforcement Learning
We consider a new family of operators for reinforcement learning with the goal of alleviating the negative effects and becoming more robust to approximation or estimation errors.
