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01/30/2023
The Fair Value of Data Under Heterogeneous Privacy Constraints
Modern data aggregation often takes the form of a platform collecting da...
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11/22/2022
Learning-based social coordination to improve safety and robustness of cooperative autonomous vehicles in mixed traffic
It is expected that autonomous vehicles(AVs) and heterogeneous human-dri...
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10/13/2022
Equal Improvability: A New Fairness Notion Considering the Long-term Impact
Devising a fair classifier that does not discriminate against different ...
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02/02/2022
Imitation Learning by Estimating Expertise of Demonstrators
Many existing imitation learning datasets are collected from multiple de...
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02/02/2022
Robustness and Adaptability of Reinforcement Learning based Cooperative Autonomous Driving in Mixed-autonomy Traffic
Building autonomous vehicles (AVs) is a complex problem, but enabling th...
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12/04/2021
Generalized Likelihood Ratio Test for Adversarially Robust Hypothesis Testing
Machine learning models are known to be susceptible to adversarial attac...
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11/05/2021
Towards Learning Generalizable Driving Policies from Restricted Latent Representations
Training intelligent agents that can drive autonomously in various urban...
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07/12/2021
Altruistic Maneuver Planning for Cooperative Autonomous Vehicles Using Multi-agent Advantage Actor-Critic
With the adoption of autonomous vehicles on our roads, we will witness a...
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07/02/2021
Cooperative Autonomous Vehicles that Sympathize with Human Drivers
Widespread adoption of autonomous vehicles will not become a reality unt...
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07/01/2021
Social Coordination and Altruism in Autonomous Driving
Despite the advances in the autonomous driving domain, autonomous vehicl...
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05/13/2021
Emergent Prosociality in Multi-Agent Games Through Gifting
Coordination is often critical to forming prosocial behaviors – behavior...
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05/06/2021
Incentivizing Efficient Equilibria in Traffic Networks with Mixed Autonomy
Traffic congestion has large economic and social costs. The introduction...
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12/28/2020
Incentivizing Routing Choices for Safe and Efficient Transportation in the Face of the COVID-19 Pandemic
The COVID-19 pandemic has severely affected many aspects of people's dai...
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11/16/2020
Adversarially Robust Classification based on GLRT
Machine learning models are vulnerable to adversarial attacks that can o...
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10/26/2020
Asymptotic Behavior of Adversarial Training in Binary Classification
It is widely known that several machine learning models are susceptible ...
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09/01/2020
Optimal Tolling for Multitype Mixed Autonomous Traffic Networks
When selfish users share a road network and minimize their individual tr...
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06/16/2020
Fundamental Limits of Ridge-Regularized Empirical Risk Minimization in High Dimensions
Empirical Risk Minimization (ERM) algorithms are widely used in a variet...
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06/16/2020
Robust Federated Learning: The Case of Affine Distribution Shifts
Federated learning is a distributed paradigm that aims at training model...
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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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02/22/2020
Polarizing Front Ends for Robust CNNs
The vulnerability of deep neural networks to small, adversarially design...
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02/17/2020
Sharp Asymptotics and Optimal Performance for Inference in Binary Models
We study convex empirical risk minimization for high-dimensional inferen...
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12/19/2019
Edge Computing in the Dark: Leveraging Contextual-Combinatorial Bandit and Coded Computing
With recent advancements in edge computing capabilities, there has been ...
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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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09/09/2019
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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08/12/2019
Sharp Guarantees for Solving Random Equations with One-Bit Information
We study the performance of a wide class of convex optimization-based es...
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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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04/11/2019
Timely-Throughput Optimal Coded Computing over Cloud Networks
In modern distributed computing systems, unpredictable and unreliable in...
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04/03/2019
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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02/06/2019
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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10/29/2018
Altruistic Autonomy: Beating Congestion on Shared Roads
Traffic congestion has large economic and social costs. The introduction...
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10/24/2018
Toward Robust Neural Networks via Sparsification
It is by now well-known that small adversarial perturbations can induce ...
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09/05/2018
Routing for Traffic Networks with Mixed Autonomy
In this work we propose a macroscopic model for studying routing on netw...
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07/12/2018
Maximizing Road Capacity Using Cars that Influence People
The emerging technology enabling autonomy in vehicles has led to a varie...
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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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04/17/2018
Communication-Aware Scheduling of Serial Tasks for Dispersed Computing
There is a growing interest in development of in-network dispersed compu...
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03/11/2018
Combating Adversarial Attacks Using Sparse Representations
It is by now well-known that small adversarial perturbations can induce ...
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01/17/2018
Coded Computing for Distributed Graph Analytics
Many distributed graph computing systems have been developed recently fo...
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01/15/2018