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- Publications
- Influence

Probabilistic Coherence and Proper Scoring Rules

- Joel B. Predd, R. Seiringer, E. Lieb, D. Osherson, H. Poor, S. Kulkarni
- Mathematics, Computer Science
- IEEE Transactions on Information Theory
- 16 October 2007

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 theorem… Expand

Insiders Behaving Badly

- Joel B. Predd, S. Pfleeger, Jeffrey Hunker, Carla Bulford
- Computer Science
- IEEE Security & Privacy
- 1 July 2008

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 "insider… Expand

A Collaborative Training Algorithm for Distributed Learning

- Joel B. Predd, S. Kulkarni, H. Poor
- Computer Science
- IEEE Transactions on Information Theory
- 1 April 2009

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 the… Expand

Insiders Behaving Badly: Addressing Bad Actors and Their Actions

- S. Pfleeger, Joel B. Predd, Jeffrey Hunker, Carla Bulford
- Computer Science
- IEEE Transactions on Information Forensics and…
- 1 March 2010

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 action… Expand

Consistency in models for distributed learning under communication constraints

- Joel B. Predd, S. Kulkarni, H. Poor
- Computer Science, Mathematics
- IEEE Transactions on Information Theory
- 25 March 2005

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 by… Expand

Distributed learning in wireless sensor networks

- Joel B. Predd, S. Kulkarni, H. Poor
- Computer Science, Mathematics
- IEEE Signal Processing Magazine
- 25 March 2005

This paper discusses nonparametric distributed learning. After reviewing the classical learning model and highlighting the success of machine learning in centralized settings, the challenges that… Expand

Distributed Kernel Regression: An Algorithm for Training Collaboratively

- Joel B. Predd, S. Kulkarni, H. Poor
- Computer Science, Mathematics
- IEEE Information Theory Workshop - ITW '06 Punta…
- 20 January 2006

This paper addresses the problem of distributed learning under communication constraints, motivated by distributed signal processing in wireless sensor networks and data mining with distributed… Expand

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 approximation… Expand

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- Open Access

Aggregating Probabilistic Forecasts from Incoherent and Abstaining Experts

- Joel B. Predd, D. Osherson, S. Kulkarni, H. Poor
- Computer Science
- Decis. Anal.
- 31 July 2008

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 extract… Expand

Regression in sensor networks: training distributively with alternating projections

- Joel B. Predd, S. Kulkarni, H. Poor
- Physics, Engineering
- SPIE Optics + Photonics
- 17 July 2005

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 or… Expand