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Generalization of Model-Agnostic Meta-Learning Algorithms: Recurring and Unseen Tasks
In this paper, we study the generalization properties of Model-Agnostic ...
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Why Does MAML Outperform ERM? An Optimization Perspective
Model-Agnostic Meta-Learning (MAML) has demonstrated widespread success ...
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Federated Learning with Compression: Unified Analysis and Sharp Guarantees
In federated learning, communication cost is often a critical bottleneck...
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Hybrid Model for Anomaly Detection on Call Detail Records by Time Series Forecasting
Mobile network operators store an enormous amount of information like lo...
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Non-asymptotic Superlinear Convergence of Standard Quasi-Newton Methods
In this paper, we study the non-asymptotic superlinear convergence rate ...
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Quantized Push-sum for Gossip and Decentralized Optimization over Directed Graphs
We consider a decentralized stochastic learning problem where data point...
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Personalized Federated Learning: A Meta-Learning Approach
The goal of federated learning is to design algorithms in which several ...
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Provably Convergent Policy Gradient Methods for Model-Agnostic Meta-Reinforcement Learning
We consider Model-Agnostic Meta-Learning (MAML) methods for Reinforcemen...
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Distribution-Agnostic Model-Agnostic Meta-Learning
The Model-Agnostic Meta-Learning (MAML) algorithm <cit.> has been celebr...
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A Decentralized Proximal Point-type Method for Saddle Point Problems
In this paper, we focus on solving a class of constrained non-convex non...
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One Sample Stochastic Frank-Wolfe
One of the beauties of the projected gradient descent method lies in its...
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FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization
Federated learning is a new distributed machine learning approach, where...
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On the Convergence Theory of Gradient-Based Model-Agnostic Meta-Learning Algorithms
In this paper, we study the convergence theory of a class of gradient-ba...
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Robust and Communication-Efficient Collaborative Learning
We consider a decentralized learning problem, where a set of computing n...
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Proximal Point Approximations Achieving a Convergence Rate of O(1/k) for Smooth Convex-Concave Saddle Point Problems: Optimistic Gradient and Extra-gradient Methods
In this paper we analyze the iteration complexity of the optimistic grad...
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Stochastic Conditional Gradient++
In this paper, we develop Stochastic Continuous Greedy++ (SCG++), the fi...
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Quantized Frank-Wolfe: Communication-Efficient Distributed Optimization
How can we efficiently mitigate the overhead of gradient communications ...
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A Unified Analysis of Extra-gradient and Optimistic Gradient Methods for Saddle Point Problems: Proximal Point Approach
We consider solving convex-concave saddle point problems. We focus on tw...
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Efficient Distributed Hessian Free Algorithm for Large-scale Empirical Risk Minimization via Accumulating Sample Strategy
In this paper, we propose a Distributed Accumulated Newton Conjugate gra...
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Escaping Saddle Points in Constrained Optimization
In this paper, we focus on escaping from saddle points in smooth nonconv...
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Quantized Decentralized Consensus Optimization
We consider the problem of decentralized consensus optimization, where t...
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Towards More Efficient Stochastic Decentralized Learning: Faster Convergence and Sparse Communication
Recently, the decentralized optimization problem is attracting growing a...
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Direct Runge-Kutta Discretization Achieves Acceleration
We study gradient-based optimization methods obtained by directly discre...
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Stochastic Conditional Gradient Methods: From Convex Minimization to Submodular Maximization
This paper considers stochastic optimization problems for a large class ...
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Large Scale Empirical Risk Minimization via Truncated Adaptive Newton Method
We consider large scale empirical risk minimization (ERM) problems, wher...
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Stochastic Averaging for Constrained Optimization with Application to Online Resource Allocation
Existing approaches to resource allocation for nowadays stochastic netwo...
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A Class of Parallel Doubly Stochastic Algorithms for Large-Scale Learning
We consider learning problems over training sets in which both, the numb...
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RES: Regularized Stochastic BFGS Algorithm
RES, a regularized stochastic version of the Broyden-Fletcher-Goldfarb-S...
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