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Found 24 papers, 0 papers with code
Policy Optimization via Stochastic Recursive Gradient Algorithm
In this paper, we propose the StochAstic Recursive grAdient Policy Optimization (SARAPO) algorithm which is a novel variance reduction method on Trust Region Policy Optimization (TRPO).
A General Continuous-Time Formulation of Stochastic ADMM and Its Variants
Stochastic versions of the alternating direction method of multiplier (ADMM) and its variants play a key role in many modern large-scale machine learning problems.
Accelerating Inexact HyperGradient Descent for Bilevel Optimization
We present a method for solving general nonconvex-strongly-convex bilevel optimization problems.
Nesterov Meets Optimism: Rate-Optimal Separable Minimax Optimization
AG-OG is the first single-call algorithm with optimal convergence rates in both deterministic and stochastic settings for bilinearly coupled minimax optimization problems.
A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning
With the increasing need for handling large state and action spaces, general function approximation has become a key technique in reinforcement learning (RL).
Learning Two-Player Mixture Markov Games: Kernel Function Approximation and Correlated Equilibrium
We consider learning Nash equilibria in two-player zero-sum Markov Games with nonlinear function approximation, where the action-value function is approximated by a function in a Reproducing Kernel Hilbert Space (RKHS).
Optimal Extragradient-Based Bilinearly-Coupled Saddle-Point Optimization
We consider the smooth convex-concave bilinearly-coupled saddle-point problem, $\min_{\mathbf{x}}\max_{\mathbf{y}}~F(\mathbf{x}) + H(\mathbf{x},\mathbf{y}) - G(\mathbf{y})$, where one has access to stochastic first-order oracles for $F$, $G$ as well as the bilinear coupling function $H$.
Nonconvex Stochastic Scaled-Gradient Descent and Generalized Eigenvector Problems
Motivated by the problem of online canonical correlation analysis, we propose the \emph{Stochastic Scaled-Gradient Descent} (SSGD) algorithm for minimizing the expectation of a stochastic function over a generic Riemannian manifold.
On the Convergence of Stochastic Extragradient for Bilinear Games using Restarted Iteration Averaging
We study the stochastic bilinear minimax optimization problem, presenting an analysis of the same-sample Stochastic ExtraGradient (SEG) method with constant step size, and presenting variations of the method that yield favorable convergence.
Stochastic Approximation for Online Tensorial Independent Component Analysis
For estimating one component, we provide a dynamics-based analysis to prove that our online tensorial ICA algorithm with a specific choice of stepsize achieves a sharp finite-sample error bound.
ROOT-SGD: Sharp Nonasymptotics and Asymptotic Efficiency in a Single Algorithm
We study the problem of solving strongly convex and smooth unconstrained optimization problems using stochastic first-order algorithms.
On Linear Stochastic Approximation: Fine-grained Polyak-Ruppert and Non-Asymptotic Concentration
When the matrix $\bar{A}$ is Hurwitz, we prove a central limit theorem (CLT) for the averaged iterates with fixed step size and number of iterations going to infinity.
Stochastic Modified Equations for Continuous Limit of Stochastic ADMM
In this work, we put different variants of stochastic ADMM into a unified form, which includes standard, linearized and gradient-based ADMM with relaxation, and study their dynamics via a continuous-time model approach.
Efficient Smooth Non-Convex Stochastic Compositional Optimization via Stochastic Recursive Gradient Descent
Stochastic compositional optimization arises in many important machine learning tasks such as reinforcement learning and portfolio management.
Hessian-Aware Zeroth-Order Optimization for Black-Box Adversarial Attack
Zeroth-order optimization is an important research topic in machine learning.
SPIDER: Near-Optimal Non-Convex Optimization via Stochastic Path-Integrated Differential Estimator
Specially, we prove that the SPIDER-SFO algorithm achieves a gradient computation cost of $\mathcal{O}\left( \min( n^{1/2} \epsilon^{-2}, \epsilon^{-3} ) \right)$ to find an $\epsilon$-approximate first-order stationary point.
A note on concentration inequality for vector-valued martingales with weak exponential-type tails
We present novel martingale concentration inequalities for martingale differences with finite Orlicz-$\psi_\alpha$ norms.
Diffusion Approximations for Online Principal Component Estimation and Global Convergence
In this paper, we propose to adopt the diffusion approximation tools to study the dynamics of Oja's iteration which is an online stochastic gradient descent method for the principal component analysis.
Online ICA: Understanding Global Dynamics of Nonconvex Optimization via Diffusion Processes
Despite the empirical success of nonconvex statistical optimization methods, their global dynamics, especially convergence to the desirable local minima, remain less well understood in theory.
SPIDER: Near-Optimal Non-Convex Optimization via Stochastic Path Integrated Differential Estimator
For stochastic first-order method, combining SPIDER with normalized gradient descent, we propose two new algorithms, namely SPIDER-SFO and SPIDER-SFO\textsuperscript{+}, that solve non-convex stochastic optimization problems using stochastic gradients only.
A convergence analysis of the perturbed compositional gradient flow: averaging principle and normal deviations
By introducing a separation of fast and slow scales of the two equations, we show that the limit of the slow motion is given by an averaged ordinary differential equation.
Online Partial Least Square Optimization: Dropping Convexity for Better Efficiency and Scalability
Multiview representation learning is popular for latent factor analysis.
On the diffusion approximation of nonconvex stochastic gradient descent
In addition, we discuss the effects of batch size for the deep neural networks, and we find that small batch size is helpful for SGD algorithms to escape unstable stationary points and sharp minimizers.
Near-Optimal Stochastic Approximation for Online Principal Component Estimation
We prove for the first time a nearly optimal finite-sample error bound for the online PCA algorithm.
