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Efficient Solution Algorithms for Factored MDPs
TLDR
This paper presents two approximate solution algorithms that exploit structure in factored MDPs by using an approximate value function represented as a linear combination of basis functions, where each basis function involves only a small subset of the domain variables. Expand
Identifying diverse usage behaviors of smartphone apps
TLDR
This paper presents results on app usage at a national level using anonymized network measurements from a tier-1 cellular carrier in the U.S. and identifies traffic from distinct marketplace apps based on HTTP signatures and presents aggregate results on their spatial and temporal prevalence, locality, and correlation. Expand
Detection of Interactive Stepping Stones: Algorithms and Confidence Bounds
TLDR
The results are the first to achieve provable (polynomial) upper bounds on the number of packets needed to confidently detect and identify encrypted stepping-stone streams with proven guarantees on the probability of falsely accusing non-attacking pairs. Expand
BitShred: feature hashing malware for scalable triage and semantic analysis
TLDR
The key idea behind BitShred is using feature hashing to dramatically reduce the high-dimensional feature spaces that are common in malware analysis, and to mine correlated features between malware families and samples using co-clustering techniques. Expand
New Streaming Algorithms for Fast Detection of Superspreaders
TLDR
This paper proposes several new streaming algorithms for detecting superspreaders, and proves guarantees on their accuracy and memory requirements, and provides several extensions to these algorithms that are applicable to any problem that can be formulated as follows. Expand
Context-specific multiagent coordination and planning with factored MDPs
TLDR
An algorithm for coordinated decision making in cooperative multiagent settings, where the agents' value function can be represented as a sum of context-specific value rules using an efficient linear programming algorithm is presented. Expand
Prometheus: toward quality-of-experience estimation for mobile apps from passive network measurements
TLDR
This paper uses machine learning to obtain a function that relates passive measurements to an app's QoE using passive network measurements, and shows with anonymous data that Prometheus can measure the QOE of real video-on-demand and VoIP apps with over 80% accuracy, which is close to or exceeds the accuracy of approaches suggested by domain experts. Expand
A first look at cellular network performance during crowded events
TLDR
This paper characterizes the operational performance of a tier-1 cellular network in the United States during two high-profile crowded events in 2012 and suggests two mechanisms that can improve performance without resorting to costly infrastructure changes: radio resource allocation tuning and opportunistic connection sharing. Expand
Modeling web quality-of-experience on cellular networks
TLDR
A machine-learning-based mechanism to infer web QoE metrics from network traces accurately is devised and a large-scale study characterizing the impact of network characteristics on web QOE is presented using a month-long anonymized dataset collected from a major cellular network provider. Expand
Characterizing data usage patterns in a large cellular network
TLDR
This paper investigates the usage patterns of mobile data users and provides a fine-grained categorization of data users based on their usage patterns and sheds light on the potential impact of different users on the cellular data network. Expand
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