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08/15/2023
Projection-Free Methods for Stochastic Simple Bilevel Optimization with Convex Lower-level Problem
In this paper, we study a class of stochastic bilevel optimization probl...
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06/27/2023
Limited-Memory Greedy Quasi-Newton Method with Non-asymptotic Superlinear Convergence Rate
Non-asymptotic convergence analysis of quasi-Newton methods has gained a...
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06/03/2023
Accelerated Quasi-Newton Proximal Extragradient: Faster Rate for Smooth Convex Optimization
In this paper, we propose an accelerated quasi-Newton proximal extragrad...
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03/20/2023
Greedy Pruning with Group Lasso Provably Generalizes for Matrix Sensing and Neural Networks with Quadratic Activations
Pruning schemes have been widely used in practice to reduce the complexi...
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02/16/2023
Online Learning Guided Curvature Approximation: A Quasi-Newton Method with Global Non-Asymptotic Superlinear Convergence
Quasi-Newton algorithms are among the most popular iterative methods for...
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02/15/2023
InfoNCE Loss Provably Learns Cluster-Preserving Representations
The goal of contrasting learning is to learn a representation that prese...
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01/11/2023
Network Adaptive Federated Learning: Congestion and Lossy Compression
In order to achieve the dual goals of privacy and learning across distri...
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09/02/2022
Future Gradient Descent for Adapting the Temporal Shifting Data Distribution in Online Recommendation Systems
One of the key challenges of learning an online recommendation model is ...
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06/17/2022
Generalized Frank-Wolfe Algorithm for Bilevel Optimization
In this paper, we study a class of bilevel optimization problems, also k...
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02/19/2022
Generalized Optimistic Methods for Convex-Concave Saddle Point Problems
The optimistic gradient method has seen increasing popularity as an effi...
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02/11/2022
The Power of Adaptivity in SGD: Self-Tuning Step Sizes with Unbounded Gradients and Affine Variance
We study convergence rates of AdaGrad-Norm as an exemplar of adaptive st...
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02/07/2022
MAML and ANIL Provably Learn Representations
Recent empirical evidence has driven conventional wisdom to believe that...
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06/10/2021
Exploiting Local Convergence of Quasi-Newton Methods Globally: Adaptive Sample Size Approach
In this paper, we study the application of quasi-Newton methods for solv...
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02/07/2021
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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10/27/2020
Why Does MAML Outperform ERM? An Optimization Perspective
Model-Agnostic Meta-Learning (MAML) has demonstrated widespread success ...
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07/02/2020
Federated Learning with Compression: Unified Analysis and Sharp Guarantees
In federated learning, communication cost is often a critical bottleneck...
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06/07/2020
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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03/30/2020
Non-asymptotic Superlinear Convergence of Standard Quasi-Newton Methods
In this paper, we study the non-asymptotic superlinear convergence rate ...
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02/23/2020
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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02/19/2020
Personalized Federated Learning: A Meta-Learning Approach
The goal of federated learning is to design algorithms in which several ...
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02/12/2020
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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02/12/2020
Distribution-Agnostic Model-Agnostic Meta-Learning
The Model-Agnostic Meta-Learning (MAML) algorithm <cit.> has been celebr...
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10/31/2019
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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10/10/2019
One Sample Stochastic Frank-Wolfe
One of the beauties of the projected gradient descent method lies in its...
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09/28/2019
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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08/27/2019
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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07/24/2019
Robust and Communication-Efficient Collaborative Learning
We consider a decentralized learning problem, where a set of computing n...
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06/03/2019
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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02/19/2019
Stochastic Conditional Gradient++
In this paper, we develop Stochastic Continuous Greedy++ (SCG++), the fi...
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02/17/2019
Quantized Frank-Wolfe: Communication-Efficient Distributed Optimization
How can we efficiently mitigate the overhead of gradient communications ...
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01/24/2019
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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10/26/2018
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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09/06/2018
Escaping Saddle Points in Constrained Optimization
In this paper, we focus on escaping from saddle points in smooth nonconv...
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06/29/2018
Quantized Decentralized Consensus Optimization
We consider the problem of decentralized consensus optimization, where t...
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05/25/2018
Towards More Efficient Stochastic Decentralized Learning: Faster Convergence and Sparse Communication
Recently, the decentralized optimization problem is attracting growing a...
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05/01/2018
Direct Runge-Kutta Discretization Achieves Acceleration
We study gradient-based optimization methods obtained by directly discre...
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04/24/2018
Stochastic Conditional Gradient Methods: From Convex Minimization to Submodular Maximization
This paper considers stochastic optimization problems for a large class ...
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05/22/2017
Large Scale Empirical Risk Minimization via Truncated Adaptive Newton Method
We consider large scale empirical risk minimization (ERM) problems, wher...
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10/07/2016
Stochastic Averaging for Constrained Optimization with Application to Online Resource Allocation
Existing approaches to resource allocation for nowadays stochastic netwo...
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06/15/2016
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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01/29/2014
