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Probabilistic Coherence and Proper Scoring Rules
This paper provides self-contained proof of a theorem relating probabilistic coherence of forecasts to their non-domination by rival forecasts with respect to any proper scoring rule. The theoremExpand
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  • Open Access
Insiders Behaving Badly
Often, we worry about outsiders attacking our systems and networks, breaking through the perimeter defenses we've established to keep bad actors out. However, we must also worry about "insiderExpand
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A Collaborative Training Algorithm for Distributed Learning
In this paper, an algorithm is developed for collaboratively training networks of kernel-linear least-squares regression estimators. The algorithm is shown to distributively solve a relaxation of theExpand
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  • 4
  • Open Access
Insiders Behaving Badly: Addressing Bad Actors and Their Actions
We present a framework for describing insiders and their actions based on the organization, the environment, the system, and the individual. Using several real examples of unwelcome insider actionExpand
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Consistency in models for distributed learning under communication constraints
Motivated by sensor networks and other distributed settings, several models for distributed learning are presented. The models differ from classical works in statistical pattern recognition byExpand
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  • Open Access
Distributed learning in wireless sensor networks
This paper discusses nonparametric distributed learning. After reviewing the classical learning model and highlighting the success of machine learning in centralized settings, the challenges thatExpand
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  • Open Access
Distributed Kernel Regression: An Algorithm for Training Collaboratively
This paper addresses the problem of distributed learning under communication constraints, motivated by distributed signal processing in wireless sensor networks and data mining with distributedExpand
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  • 2
  • Open Access
Aggregating Forecasts of Chance from Incoherent and Abstaining Experts
Linear averaging is a popular method for combining forecasts of chance, but it is of limited use in the context of incoherent or abstaining judges. Recently proposed, the coherent approximationExpand
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  • 1
  • Open Access
Aggregating Probabilistic Forecasts from Incoherent and Abstaining Experts
Decision makers often rely on expert opinion when making forecasts under uncertainty. In doing so, they confront two methodological challenges: the elicitation problem, which requires them to extractExpand
  • 60
  • Open Access
Regression in sensor networks: training distributively with alternating projections
Wireless sensor networks (WSNs) have attracted considerable attention in recent years and motivate a host of new challenges for distributed signal processing. The problem of distributed orExpand
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  • Open Access