885 citations · 1.7k across the 71 of their papers we have counts for
12 papers · 1 filter
Edge Computing in the Dark: Leveraging Contextual-Combinatorial Bandit and Coded Computing
Chien-Sheng Yang, Ramtin Pedarsani, A. Salman Avestimehr
With recent advancements in edge computing capabilities, there has been a significant increase in utilizing the edge cloud for event-driven and time-sensitive computations. However…
Optimal Tolling for Heterogeneous Traffic Networks with Mixed Autonomy
Daniel A. Lazar, Samuel Coogan, Ramtin Pedarsani
When people pick routes to minimize their travel time, the total experienced delay, or social cost, may be significantly greater than if people followed routes assigned to them by…
FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization
Amirhossein Reisizadeh, Aryan Mokhtari, Hamed Hassani +2
Federated learning is a distributed framework according to which a model is trained over a set of devices, while keeping data localized. This framework faces several systems-orient…
Dynamic Pricing and Fleet Management for Electric Autonomous Mobility on Demand Systems
Berkay Turan, Ramtin Pedarsani, Mahnoosh Alizadeh
The proliferation of ride sharing systems is a major drive in the advancement of autonomous and electric vehicle technologies. This paper considers the joint routing, battery charg…
Learning How to Dynamically Route Autonomous Vehicles on Shared Roads
Daniel A. Lazar, Erdem Bıyık, Dorsa Sadigh +1
Road congestion induces significant costs across the world, and road network disturbances, such as traffic accidents, can cause highly congested traffic patterns. If a planner had…
Mixed Autonomy in Ride-Sharing Networks
Qinshuang Wei, Ramtin Pedarsani, Samuel Coogan
We consider ride-sharing networks served by human-driven vehicles (HVs) and autonomous vehicles (AVs). We propose a model for ride-sharing in this mixed autonomy setting for a mult…
Sharp Guarantees for Solving Random Equations with One-Bit Information
Hossein Taheri, Ramtin Pedarsani, Christos Thrampoulidis
We study the performance of a wide class of convex optimization-based estimators for recovering a signal from corrupted one-bit measurements in high-dimensions. Our general result…
Robust and Communication-Efficient Collaborative Learning
Amirhossein Reisizadeh, Hossein Taheri, Aryan Mokhtari +2
We consider a decentralized learning problem, where a set of computing nodes aim at solving a non-convex optimization problem collaboratively. It is well-known that decentralized o…
Timely-Throughput Optimal Coded Computing over Cloud Networks
Chien-Sheng Yang, Ramtin Pedarsani, A. Salman Avestimehr
In modern distributed computing systems, unpredictable and unreliable infrastructures result in high variability of computing resources. Meanwhile, there is significantly increasin…
The Green Choice: Learning and Influencing Human Decisions on Shared Roads
Erdem Bıyık, Daniel A. Lazar, Dorsa Sadigh +1
Autonomous vehicles have the potential to increase the capacity of roads via platooning, even when human drivers and autonomous vehicles share roads. However, when users of a road…
Ride-Sharing Networks with Mixed Autonomy
Qinshuang Wei, Jorge Alberto Rodriguez, Ramtin Pedarsani +1
We consider ride-sharing networks served byhuman-driven vehicles and autonomous vehicles. First, wepropose a novel model for ride-sharing in this mixed autonomysetting for a multi-…
CodedReduce: A Fast and Robust Framework for Gradient Aggregation in Distributed Learning
Amirhossein Reisizadeh, Saurav Prakash, Ramtin Pedarsani +1
We focus on the commonly used synchronous Gradient Descent paradigm for large-scale distributed learning, for which there has been a growing interest to develop efficient and robus…