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Found 10 papers, 2 papers with code
Long-range Event-level Prediction and Response Simulation for Urban Crime and Global Terrorism with Granger Networks
Large-scale trends in urban crime and global terrorism are well-predicted by socio-economic drivers, but focused, event-level predictions have had limited success.
Data Smashing 2.0: Sequence Likelihood (SL) Divergence For Fast Time Series Comparison
Recognizing subtle historical patterns is central to modeling and forecasting problems in time series analysis.
A Tamper-Free Semi-Universal Communication System for Deletion Channels
We investigate the problem of reliable communication between two legitimate parties over deletion channels under an active eavesdropping (aka jamming) adversarial model.
Autostacker: A Compositional Evolutionary Learning System
We introduce an automatic machine learning (AutoML) modeling architecture called Autostacker, which combines an innovative hierarchical stacking architecture and an Evolutionary Algorithm (EA) to perform efficient parameter search.
A Hilbert Space of Stationary Ergodic Processes
Identifying meaningful signal buried in noise is a problem of interest arising in diverse scenarios of data-driven modeling.
Algorithmic Bio-surveillance For Precise Spatio-temporal Prediction of Zoonotic Emergence
Viral zoonoses have emerged as the key drivers of recent pandemics.
Autostacker: an Automatic Evolutionary Hierarchical Machine Learning System
We significantly reduce the time of AutoML with a naturally inspired algorithm - Parallel Hill Climbing (PHC).
Causality Networks
While correlation measures are used to discern statistical relationships between observed variables in almost all branches of data-driven scientific inquiry, what we are really interested in is the existence of causal dependence.
Computing Entropy Rate Of Symbol Sources & A Distribution-free Limit Theorem
Entropy rate of sequential data-streams naturally quantifies the complexity of the generative process.
Data Smashing
Here, we propose a universal solution to this problem: we delineate a principle for quantifying similarity between sources of arbitrary data streams, without a priori knowledge, features or training.
