
Incentivizing Routing Choices for Safe and Efficient Transportation in the Face of the COVID19 Pandemic
The COVID19 pandemic has severely affected many aspects of people's dai...
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Adversarially Robust Classification based on GLRT
Machine learning models are vulnerable to adversarial attacks that can o...
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Asymptotic Behavior of Adversarial Training in Binary Classification
It is widely known that several machine learning models are susceptible ...
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Optimal Tolling for Multitype Mixed Autonomous Traffic Networks
When selfish users share a road network and minimize their individual tr...
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Fundamental Limits of RidgeRegularized Empirical Risk Minimization in High Dimensions
Empirical Risk Minimization (ERM) algorithms are widely used in a variet...
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Robust Federated Learning: The Case of Affine Distribution Shifts
Federated learning is a distributed paradigm that aims at training model...
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Quantized Pushsum for Gossip and Decentralized Optimization over Directed Graphs
We consider a decentralized stochastic learning problem where data point...
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Polarizing Front Ends for Robust CNNs
The vulnerability of deep neural networks to small, adversarially design...
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Sharp Asymptotics and Optimal Performance for Inference in Binary Models
We study convex empirical risk minimization for highdimensional inferen...
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Edge Computing in the Dark: Leveraging ContextualCombinatorial Bandit and Coded Computing
With recent advancements in edge computing capabilities, there has been ...
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FedPAQ: A CommunicationEfficient Federated Learning Method with Periodic Averaging and Quantization
Federated learning is a new distributed machine learning approach, where...
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Learning How to Dynamically Route Autonomous Vehicles on Shared Roads
Road congestion induces significant costs across the world, and road net...
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Sharp Guarantees for Solving Random Equations with OneBit Information
We study the performance of a wide class of convex optimizationbased es...
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Robust and CommunicationEfficient Collaborative Learning
We consider a decentralized learning problem, where a set of computing n...
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TimelyThroughput Optimal Coded Computing over Cloud Networks
In modern distributed computing systems, unpredictable and unreliable in...
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The Green Choice: Learning and Influencing Human Decisions on Shared Roads
Autonomous vehicles have the potential to increase the capacity of roads...
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CodedReduce: A Fast and Robust Framework for Gradient Aggregation in Distributed Learning
We focus on the commonly used synchronous Gradient Descent paradigm for ...
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Altruistic Autonomy: Beating Congestion on Shared Roads
Traffic congestion has large economic and social costs. The introduction...
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Toward Robust Neural Networks via Sparsification
It is by now wellknown that small adversarial perturbations can induce ...
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Routing for Traffic Networks with Mixed Autonomy
In this work we propose a macroscopic model for studying routing on netw...
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Maximizing Road Capacity Using Cars that Influence People
The emerging technology enabling autonomy in vehicles has led to a varie...
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Quantized Decentralized Consensus Optimization
We consider the problem of decentralized consensus optimization, where t...
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CommunicationAware Scheduling of Serial Tasks for Dispersed Computing
There is a growing interest in development of innetwork dispersed compu...
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Combating Adversarial Attacks Using Sparse Representations
It is by now wellknown that small adversarial perturbations can induce ...
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Coded Computing for Distributed Graph Analytics
Many distributed graph computing systems have been developed recently fo...
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Sparsitybased Defense against Adversarial Attacks on Linear Classifiers
Deep neural networks represent the state of the art in machine learning ...
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Ramtin Pedarsani
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