380 citations · 628 across the 6 of their papers we have counts for
6 papers
Probabilistic coherence and proper scoring rules
Joel Predd, Robert Seiringer, Elliott H. Lieb +3
We provide 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 t…
Distributed Kernel Regression: An Algorithm for Training Collaboratively
Joel B. Predd, Sanjeev R. Kulkarni, H. Vincent Poor
This paper addresses the problem of distributed learning under communication constraints, motivated by distributed signal processing in wireless sensor networks and data mining wit…
Scalable Algorithms for Aggregating Disparate Forecasts of Probability
Joel B. Predd, Sanjeev R. Kulkarni, Daniel N. Osherson +1
In this paper, computational aspects of the panel aggregation problem are addressed. Motivated primarily by applications of risk assessment, an algorithm is developed for aggregati…
Distributed Regression in Sensor Networks: Training Distributively with Alternating Projections
Joel B. Predd, Sanjeev R. Kulkarni, H. Vincent Poor
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 distri…
Distributed Learning in Wireless Sensor Networks
Joel B. Predd, Sanjeev R. Kulkarni, H. Vincent Poor
The problem of distributed or decentralized detection and estimation in applications such as wireless sensor networks has often been considered in the framework of parametric model…
Consistency in Models for Distributed Learning under Communication Constraints
Joel B. Predd, Sanjeev R. Kulkarni, H. Vincent Poor
Motivated by sensor networks and other distributed settings, several models for distributed learning are presented. The models differ from classical works in statistical pattern re…