Model reduction from partial observations

Learning from Partial Observations

We present a general machine learning framework for modelling the phenomenon of missing information in data. We propose a masking process model to capture the stochastic nature of information loss. Learning in this context is employed as a means to recover as much of the missing information as is recoverable. We extend the Probably Approximately Correct semantics to the case of learning from pa...

Influence-Driven Model for Time Series Prediction from Partial Observations

Applications in sustainability domains such as in energy, transportation, and natural resource and environment monitoring, increasingly use sensors for collecting data and sending it back to centrally located processing nodes. While data can usually be collected by the sensors at a very high speed, in many cases, it can not be sent back to central nodes at a frequency that is required for fast ...

Partial Order Reduction in Directed Model Checking

Partial order reduction is a very succesful technique for avoid ing the state explosion problem that is inherent to explicit state model checking of asynchronous concurrent systems It exploits the commu tativity of concurrently executed transitions in interleaved system runs in order to reduce the size of the explored state space Directed model checking on the other hand addresses the state exp...

Model Checking with the Partial Order Reduction

Learning Valuation Distributions from Partial Observations

Auction theory traditionally assumes that bidders’ valuation distributions are known to the auctioneer, such as in the celebrated, revenue-optimal Myerson auction (Myerson 1981). However, this theory does not describe how the auctioneer comes to possess this information. Recently work (Cole and Roughgarden 2014) showed that an approximation based on a finite sample of independent draws from eac...