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01/03/2022
Execute Order 66: Targeted Data Poisoning for Reinforcement Learning
Data poisoning for reinforcement learning has historically focused on ge...
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01/20/2021
LowKey: Leveraging Adversarial Attacks to Protect Social Media Users from Facial Recognition
Facial recognition systems are increasingly deployed by private corporat...
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10/08/2020
Information-Driven Adaptive Sensing Based on Deep Reinforcement Learning
In order to make better use of deep reinforcement learning in the creati...
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09/04/2020
Witches' Brew: Industrial Scale Data Poisoning via Gradient Matching
Data Poisoning attacks involve an attacker modifying training data to ma...
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04/01/2020
MetaPoison: Practical General-purpose Clean-label Data Poisoning
Data poisoning–the process by which an attacker takes control of a model...
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05/15/2019
Transferable Clean-Label Poisoning Attacks on Deep Neural Nets
In this paper, we explore clean-label poisoning attacks on deep convolut...
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05/10/2019
Autonomous Management of Energy-Harvesting IoT Nodes Using Deep Reinforcement Learning
Reinforcement learning (RL) is capable of managing wireless, energy-harv...
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04/29/2019
Adversarial Training for Free!
Adversarial training, in which a network is trained on adversarial examp...
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12/28/2017
Visualizing the Loss Landscape of Neural Nets
Neural network training relies on our ability to find "good" minimizers ...
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06/09/2017
Adaptive Consensus ADMM for Distributed Optimization
The alternating direction method of multipliers (ADMM) is commonly used ...
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12/05/2015
Variance Reduction for Distributed Stochastic Gradient Descent
Variance reduction (VR) methods boost the performance of stochastic grad...
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10/15/2015
Layer-Specific Adaptive Learning Rates for Deep Networks
The increasing complexity of deep learning architectures is resulting in...
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04/16/2014
An Analysis of State-Relevance Weights and Sampling Distributions on L1-Regularized Approximate Linear Programming Approximation Accuracy
Recent interest in the use of L_1 regularization in the use of value fun...
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10/16/2012
Value Function Approximation in Noisy Environments Using Locally Smoothed Regularized Approximate Linear Programs
Recently, Petrik et al. demonstrated that L1Regularized Approximate Line...
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05/11/2010
