General Laws of Adaptation to Environmental Factors: From Ecological Stress to Financial Crisis

We study ensembles of similar systems under load of environmental factors. The phenomenon of adaptation has similar properties for systems of different nature. Typically, when the load increases above some threshold, then the adapting systems become more different (variance increases), but the correlation increases too. If the stress continues to increase then the second threshold appears: the correlation achieves maximal value, and start to decrease, but the variance continue to increase. In many applications this second threshold is a signal of approaching of fatal outcome.
This effect is supported by many experiments and observation of groups of humans, mice, trees, grassy plants, and on financial time series. A general approach to explanation of the effect through dynamics of adaptation is developed. H. Selye introduced “adaptation energy" for explanation of adaptation phenomena. We formalize this approach in factors – resource models and develop hierarchy of models of adaptation. Different organization of interaction between factors (Liebig's versus synergistic systems) lead to different adaptation dynamics. This gives an explanation to qualitatively different dynamics of correlation under different types of load and to some deviation from the typical reaction to stress.
In addition to the “quasistatic" optimization factor – resource models, dynamical models of adaptation are developed, and a simple model (three variables) for adaptation to one factor load is formulated explicitly

Weakly Supervised Learners for Correction of AI Errors with Provable Performance Guarantees

We  present  a  new  methodology  for  handling  AIerrors by introducing weakly supervised AI error correctors witha prioriperformance guarantees. These AI correctors are auxiliarymaps  whose  role  is  to  moderate  the  decisions  of  some  previouslyconstructed underlying classifier by either approving or rejectingits  decisions.  The  rejection  of  a  decision  can  be  used  as  a  signalto  suggest  abstaining  from  making  a  decision.  A  key  technicalfocus  of  the  work  is  in  providing  performance  guarantees  forthese new AI correctors through bounds on the probabilities ofincorrect decisions. These bounds are distribution agnostic anddo not rely on assumptions on the data dimension. Our empiricalexample illustrates how the framework can be applied to improvethe performance of an image classifier in a challenging real-worldtask  where  training  data  are  scarce.</p

Weakly Supervised Learners for Correction of AI Errors with Provable Performance Guarantees

We  present  a  new  methodology  for  handling  AIerrors by introducing weakly supervised AI error correctors witha prioriperformance guarantees. These AI correctors are auxiliarymaps  whose  role  is  to  moderate  the  decisions  of  some  previouslyconstructed underlying classifier by either approving or rejectingits  decisions.  The  rejection  of  a  decision  can  be  used  as  a  signalto  suggest  abstaining  from  making  a  decision.  A  key  technicalfocus  of  the  work  is  in  providing  performance  guarantees  forthese new AI correctors through bounds on the probabilities ofincorrect decisions. These bounds are distribution agnostic anddo not rely on assumptions on the data dimension. Our empiricalexample illustrates how the framework can be applied to improvethe performance of an image classifier in a challenging real-worldtask  where  training  data  are  scarce.</p