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Found 44 papers, 1 papers with code
On the Online Frank-Wolfe Algorithms for Convex and Non-convex Optimizations
With a strongly convex stochastic cost and when the optimal solution lies in the interior of the constraint set or the constraint set is a polytope, the regret bound and anytime optimality are shown to be ${\cal O}( \log^3 T / T )$ and ${\cal O}( \log^2 T / T)$, respectively, where $T$ is the number of rounds played.
Active Sensing of Social Networks
The model used for the regression is based on the steady state equation in the linear DeGroot model under the influence of stubborn agents, i. e., agents whose opinions are not influenced by their neighbors.
Decentralized Frank-Wolfe Algorithm for Convex and Non-convex Problems
The convergence of the proposed algorithm is studied by viewing the decentralized algorithm as an inexact FW algorithm.
RIDS: Robust Identification of Sparse Gene Regulatory Networks from Perturbation Experiments
To overcome this challenge, we develop the Robust IDentification of Sparse networks (RIDS) method that reconstructs the GRN from a small number of perturbation experiments.
Curvature-aided Incremental Aggregated Gradient Method
We propose a new algorithm for finite sum optimization which we call the curvature-aided incremental aggregated gradient (CIAG) method.
SUCAG: Stochastic Unbiased Curvature-aided Gradient Method for Distributed Optimization
We propose and analyze a new stochastic gradient method, which we call Stochastic Unbiased Curvature-aided Gradient (SUCAG), for finite sum optimization problems.
Accelerating Incremental Gradient Optimization with Curvature Information
This paper studies an acceleration technique for incremental aggregated gradient ({\sf IAG}) method through the use of \emph{curvature} information for solving strongly convex finite sum optimization problems.
Multi-Agent Reinforcement Learning via Double Averaging Primal-Dual Optimization
Despite the success of single-agent reinforcement learning, multi-agent reinforcement learning (MARL) remains challenging due to complex interactions between agents.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Blind Community Detection from Low-rank Excitations of a Graph Filter
The paper shows that communities can be detected by applying a spectral method to the covariance matrix of graph signals.
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.
Block-Randomized Stochastic Proximal Gradient for Low-Rank Tensor Factorization
In this work, we propose a stochastic optimization framework for large-scale CPD with constraints/regularizations.
Non-asymptotic Analysis of Biased Stochastic Approximation Scheme
We illustrate these settings with the online EM algorithm and the policy-gradient method for average reward maximization in reinforcement learning.
Spectral partitioning of time-varying networks with unobserved edges
We discuss a variant of `blind' community detection, in which we aim to partition an unobserved network from the observation of a (dynamical) graph signal defined on the network.
On the Global Convergence of (Fast) Incremental Expectation Maximization Methods
To alleviate this problem, Neal and Hinton have proposed an incremental version of the EM (iEM) in which at each iteration the conditional expectation of the latent data (E-step) is updated only for a mini-batch of observations.
Variance Reduced Policy Evaluation with Smooth Function Approximation
Policy evaluation with smooth and nonlinear function approximation has shown great potential for reinforcement learning.
Distributed Learning in the Non-Convex World: From Batch to Streaming Data, and Beyond
In particular, we {provide a selective review} about the recent techniques developed for optimizing non-convex models (i. e., problem classes), processing batch and streaming data (i. e., data types), over the networks in a distributed manner (i. e., communication and computation paradigm).
Finite Time Analysis of Linear Two-timescale Stochastic Approximation with Markovian Noise
Our bounds show that there is no discrepancy in the convergence rate between Markovian and martingale noise, only the constants are affected by the mixing time of the Markov chain.
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.
A Two-Timescale Framework for Bilevel Optimization: Complexity Analysis and Application to Actor-Critic
Bilevel optimization is a class of problems which exhibit a two-level structure, and its goal is to minimize an outer objective function with variables which are constrained to be the optimal solution to an (inner) optimization problem.
A User Guide to Low-Pass Graph Signal Processing and its Applications
The notion of graph filters can be used to define generative models for graph data.
On the Convergence of Consensus Algorithms with Markovian Noise and Gradient Bias
This rate is comparable to the best known performances of SA in a centralized setting with a non-convex potential function.
Geom-SPIDER-EM: Faster Variance Reduced Stochastic Expectation Maximization for Nonconvex Finite-Sum Optimization
The Expectation Maximization (EM) algorithm is a key reference for inference in latent variable models; unfortunately, its computational cost is prohibitive in the large scale learning setting.
A Stochastic Path-Integrated Differential EstimatoR Expectation Maximization Algorithm
The Expectation Maximization (EM) algorithm is of key importance for inference in latent variable models including mixture of regressors and experts, missing observations.
A Stochastic Path Integral Differential EstimatoR Expectation Maximization Algorithm
The Expectation Maximization (EM) algorithm is of key importance for inference in latent variable models including mixture of regressors and experts, missing observations.
Provably Efficient Neural GTD for Off-Policy Learning
This paper studies a gradient temporal difference (GTD) algorithm using neural network (NN) function approximators to minimize the mean squared Bellman error (MSBE).
Identifying First-order Lowpass Graph Signals using Perron Frobenius Theorem
Utilizing this observation, we develop a simple detector that answers if a given data set is produced by a first-order lowpass graph filter.
On the Stability of Random Matrix Product with Markovian Noise: Application to Linear Stochastic Approximation and TD Learning
This paper studies the exponential stability of random matrix products driven by a general (possibly unbounded) state space Markov chain.
A Near-Optimal Algorithm for Stochastic Bilevel Optimization via Double-Momentum
We focus on bilevel problems where the lower level subproblem is strongly-convex and the upper level objective function is smooth.
Tight High Probability Bounds for Linear Stochastic Approximation with Fixed Stepsize
This family of methods arises in many machine learning tasks and is used to obtain approximate solutions of a linear system $\bar{A}\theta = \bar{b}$ for which $\bar{A}$ and $\bar{b}$ can only be accessed through random estimates $\{({\bf A}_n, {\bf b}_n): n \in \mathbb{N}^*\}$.
Robust Distributed Optimization With Randomly Corrupted Gradients
In this paper, we propose a first-order distributed optimization algorithm that is provably robust to Byzantine failures-arbitrary and potentially adversarial behavior, where all the participating agents are prone to failure.
Detecting Central Nodes from Low-rank Excited Graph Signals via Structured Factor Analysis
For general low pass graph filter, we show that the graph signals can be described by a structured factor model featuring the product between a low-rank plus sparse factor and an unstructured factor.
Inducing Equilibria via Incentives: Simultaneous Design-and-Play Ensures Global Convergence
To regulate a social system comprised of self-interested agents, economic incentives are often required to induce a desirable outcome.
An Empirical Study on Compressed Decentralized Stochastic Gradient Algorithms with Overparameterized Models
In this work, we present an empirical analysis on the performance of compressed decentralized stochastic gradient (DSG) algorithms with overparameterized NNs.
On the Stability of Low Pass Graph Filter With a Large Number of Edge Rewires
The stability of a graph filter characterizes the effect of topology perturbation on the output of a graph filter, a fundamental building block for GCNs.
DoCoM: Compressed Decentralized Optimization with Near-Optimal Sample Complexity
This paper proposes the Doubly Compressed Momentum-assisted stochastic gradient tracking algorithm $\texttt{DoCoM}$ for communication-efficient decentralized optimization.
Online Inference for Mixture Model of Streaming Graph Signals with Non-White Excitation
We study a mixture model of filtered low pass graph signals with possibly non-white and low-rank excitation.
Product Graph Learning from Multi-attribute Graph Signals with Inter-layer Coupling
This paper considers learning a product graph from multi-attribute graph signals.
Stochastic Approximation Beyond Gradient for Signal Processing and Machine Learning
Stochastic Approximation (SA) is a classical algorithm that has had since the early days a huge impact on signal processing, and nowadays on machine learning, due to the necessity to deal with a large amount of data observed with uncertainties.
Clustering of Time-Varying Graphs Based on Temporal Label Smoothness
In this paper, we formulate a node clustering of time-varying graphs as an optimization problem based on spectral clustering, with a smoothness constraint of the node labels.
Detecting Low Pass Graph Signals via Spectral Pattern: Sampling Complexity and Applications
This paper proposes a blind detection problem for low pass graph signals.
Blind Graph Matching Using Graph Signals
Our analysis shows that the blind matching outcome converges to the result obtained with known graph topologies when the signal sampling size is large and the signal noise is small.
Linear Speedup of Incremental Aggregated Gradient Methods on Streaming Data
This paper considers a type of incremental aggregated gradient (IAG) method for large-scale distributed optimization.
EMC$^2$: Efficient MCMC Negative Sampling for Contrastive Learning with Global Convergence
We follow the global contrastive learning loss as introduced in SogCLR, and propose EMC$^2$ which utilizes an adaptive Metropolis-Hastings subroutine to generate hardness-aware negative samples in an online fashion during the optimization.
Clipped SGD Algorithms for Privacy Preserving Performative Prediction: Bias Amplification and Remedies
This paper studies the convergence properties of these algorithms in a performative prediction setting, where the data distribution may shift due to the deployed prediction model.
