362 citations · 2.5k across the 76 of their papers we have counts for
11 papers · 1 filter
Straggler-Resilient Federated Learning: Leveraging the Interplay Between Statistical Accuracy and System Heterogeneity
Amirhossein Reisizadeh, Isidoros Tziotis, Hamed Hassani +2
Federated Learning is a novel paradigm that involves learning from data samples distributed across a large network of clients while the data remains local. It is, however, known th…
How Does the Task Landscape Affect MAML Performance?
Liam Collins, Aryan Mokhtari, Sanjay Shakkottai
Model-Agnostic Meta-Learning (MAML) has become increasingly popular for training models that can quickly adapt to new tasks via one or few stochastic gradient descent steps. Howeve…
Submodular Meta-Learning
Arman Adibi, Aryan Mokhtari, Hamed Hassani
In this paper, we introduce a discrete variant of the meta-learning framework. Meta-learning aims at exploiting prior experience and data to improve performance on future tasks. By…
Federated Learning with Compression: Unified Analysis and Sharp Guarantees
Farzin Haddadpour, Mohammad Mahdi Kamani, Aryan Mokhtari +1
In federated learning, communication cost is often a critical bottleneck to scale up distributed optimization algorithms to collaboratively learn a model from millions of devices w…
Safe Learning under Uncertain Objectives and Constraints
Mohammad Fereydounian, Zebang Shen, Aryan Mokhtari +2
In this paper, we consider non-convex optimization problems under \textit{unknown} yet safety-critical constraints. Such problems naturally arise in a variety of domains including…
Hybrid Model for Anomaly Detection on Call Detail Records by Time Series Forecasting
Aryan Mokhtari, Leyla Sadighi, Behnam Bahrak +1
Mobile network operators store an enormous amount of information like log files that describe various events and users' activities. Analysis of these logs might be used in many cri…
Non-asymptotic Superlinear Convergence of Standard Quasi-Newton Methods
Qiujiang Jin, Aryan Mokhtari
In this paper, we study and prove the non-asymptotic superlinear convergence rate of the Broyden class of quasi-Newton algorithms which includes the Davidon--Fletcher--Powell (DFP)…
Quantized Decentralized Stochastic Learning over Directed Graphs
Hossein Taheri, Aryan Mokhtari, Hamed Hassani +1
We consider a decentralized stochastic learning problem where data points are distributed among computing nodes communicating over a directed graph. As the model size gets large, d…
Personalized Federated Learning: A Meta-Learning Approach
Alireza Fallah, Aryan Mokhtari, Asuman Ozdaglar
In Federated Learning, we aim to train models across multiple computing units (users), while users can only communicate with a common central server, without exchanging their data…
On the Convergence Theory of Debiased Model-Agnostic Meta-Reinforcement Learning
Alireza Fallah, Kristian Georgiev, Aryan Mokhtari +1
We consider Model-Agnostic Meta-Learning (MAML) methods for Reinforcement Learning (RL) problems, where the goal is to find a policy using data from several tasks represented by Ma…
Task-Robust Model-Agnostic Meta-Learning
Liam Collins, Aryan Mokhtari, Sanjay Shakkottai
Meta-learning methods have shown an impressive ability to train models that rapidly learn new tasks. However, these methods only aim to perform well in expectation over tasks comin…