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Found 21 papers, 3 papers with code
Natural Policy Gradient for Average Reward Non-Stationary RL
Policy-based methods despite their flexibility in practice are not theoretically well understood in non-stationary RL.
Initialization Matters: Unraveling the Impact of Pre-Training on Federated Learning
Several recent works explore the benefits of pre-trained initialization in a federated learning (FL) setting, where the downstream training is performed at the edge clients with heterogeneous data distribution.
Federated Communication-Efficient Multi-Objective Optimization
We study a federated version of multi-objective optimization (MOO), where a single model is trained to optimize multiple objective functions.
Nonlinear Stochastic Gradient Descent and Heavy-tailed Noise: A Unified Framework and High-probability Guarantees
For symmetric noise and non-convex costs we establish convergence of gradient norm-squared, at a rate $\widetilde{\mathcal{O}}(t^{-1/4})$, while for the last iterate of strongly convex costs we establish convergence to the population optima, at a rate $\mathcal{O}(t^{-\zeta})$, where $\zeta \in (0, 1)$ depends on noise and problem parameters.
Debiasing Federated Learning with Correlated Client Participation
In cross-device federated learning (FL) with millions of mobile clients, only a small subset of clients participate in training in every communication round, and Federated Averaging (FedAvg) is the most popular algorithm in practice.
FedAST: Federated Asynchronous Simultaneous Training
Much of the existing work in FL focuses on efficiently learning a model for a single task.
High-probability Convergence Bounds for Nonlinear Stochastic Gradient Descent Under Heavy-tailed Noise
First, for non-convex costs and component-wise nonlinearities, we establish a convergence rate arbitrarily close to $\mathcal{O}\left(t^{-\frac{1}{4}}\right)$, whose exponent is independent of noise and problem parameters.
Federated Multi-Sequence Stochastic Approximation with Local Hypergradient Estimation
Stochastic approximation with multiple coupled sequences (MSA) has found broad applications in machine learning as it encompasses a rich class of problems including bilevel optimization (BLO), multi-level compositional optimization (MCO), and reinforcement learning (specifically, actor-critic methods).
Model Sparsity Can Simplify Machine Unlearning
We show in both theory and practice that model sparsity can boost the multi-criteria unlearning performance of an approximate unlearner, closing the approximation gap, while continuing to be efficient.
What Is Missing in IRM Training and Evaluation? Challenges and Solutions
We propose a new IRM variant to address this limitation based on a novel viewpoint of ensemble IRM games as consensus-constrained bi-level optimization.
Federated Minimax Optimization with Client Heterogeneity
We analyze the convergence of the proposed algorithm for classes of nonconvex-concave and nonconvex-nonconcave functions and characterize the impact of heterogeneous client data, partial client participation, and heterogeneous local computations.
On the Convergence of Federated Averaging with Cyclic Client Participation
Federated Averaging (FedAvg) and its variants are the most popular optimization algorithms in federated learning (FL).
FedVARP: Tackling the Variance Due to Partial Client Participation in Federated Learning
To remedy this, we propose FedVARP, a novel variance reduction algorithm applied at the server that eliminates error due to partial client participation.
Federated Stochastic Approximation under Markov Noise and Heterogeneity: Applications in Reinforcement Learning
Federated reinforcement learning is a framework in which $N$ agents collaboratively learn a global model, without sharing their individual data and policies.
Distributed Estimation in Large Scale Wireless Sensor Networks via a Two Step Group-based Approach
To achieve greater sensor transmission and estimation efficiency, we propose a two step group-based collaborative distributed estimation scheme, where in the first step, sensors form dependence driven groups such that sensors in the same group are highly dependent, while sensors from different groups are independent, and perform a copula-based maximum a posteriori probability (MAP) estimation via intragroup collaboration.
Federated Minimax Optimization: Improved Convergence Analyses and Algorithms
In this paper, we consider nonconvex minimax optimization, which is gaining prominence in many modern machine learning applications such as GANs.
STEM: A Stochastic Two-Sided Momentum Algorithm Achieving Near-Optimal Sample and Communication Complexities for Federated Learning
Despite extensive research, for a generic non-convex FL problem, it is not clear, how to choose the WNs' and the server's update directions, the minibatch sizes, and the local update frequency, so that the WNs use the minimum number of samples and communication rounds to achieve the desired solution.
Zeroth-Order Hybrid Gradient Descent: Towards A Principled Black-Box Optimization Framework
In this work, we focus on the study of stochastic zeroth-order (ZO) optimization which does not require first-order gradient information and uses only function evaluations.
Distributed Stochastic Non-Convex Optimization: Momentum-Based Variance Reduction
In this work, we propose a distributed algorithm for stochastic non-convex optimization.
Optimization and Control Distributed, Parallel, and Cluster Computing
Parallel Restarted SPIDER -- Communication Efficient Distributed Nonconvex Optimization with Optimal Computation Complexity
For online problems ($n$ unknown or infinite), we achieve the optimal IFO complexity $O(\epsilon^{-3/2})$.
Why Interpretability in Machine Learning? An Answer Using Distributed Detection and Data Fusion Theory
Such arguments, however, fail to acknowledge that the overall decision-making system is composed of two entities: the learned model and a human who fuses together model outputs with his or her own information.
