A note on no-free-lunch theorem

The No-Free-Lunch theorem is an interesting and important theoretical result in machine learning. Based on philosophy of No-Free-Lunch theorem, we discuss extensively on the limitation of a data-driven approach in solving NP-hard problems.

THE LIVING QURAN: TRADISI FREE LUNCH SETELAH SHALAT JUMAT DI MASJID JOGOKARIYAN YOGYAKARTA

The study of living Qur'an is one of the studies in the form of scientific research on social events in the Muslim community by positioning the Qur'an as the main behavior and actions in daily life. From there, it will be seen how they react continuously and live up to the values ​​of the al-Qur'an in their community. The study of the Living Qur'an appears as a phenomenon of the Qur'an in everyday life, namely the function and meaning of the Qur'an is understood and practiced by the Muslim community in real terms in everyday life. Unlike the study of the Koran which only focuses on the al-Qur'an as an object of study that focuses on the textuality of the Qur'an it self, while the living Al-Qur'an is more focused on the way people position the Qur'an. In his daily life. One example of this living Qur'an practice is the tradition of Free Lunch after Friday prayers at the Jogokariyan Mosque. In this tradition, the Muslim community tries to live up to the value of sodaqoh contained in the Al-Qur'an surah al-Baqarah verse 261. This tradition emerged in the community as a form of social tradition with the aim of providing assistance in the form of free lunch (free lunch) to Friday congregation.

Still No Free Lunches: The Price to Pay for Tighter PAC-Bayes Bounds

“No free lunch” results state the impossibility of obtaining meaningful bounds on the error of a learning algorithm without prior assumptions and modelling, which is more or less realistic for a given problem. Some models are “expensive” (strong assumptions, such as sub-Gaussian tails), others are “cheap” (simply finite variance). As it is well known, the more you pay, the more you get: in other words, the most expensive models yield the more interesting bounds. Recent advances in robust statistics have investigated procedures to obtain tight bounds while keeping the cost of assumptions minimal. The present paper explores and exhibits what the limits are for obtaining tight probably approximately correct (PAC)-Bayes bounds in a robust setting for cheap models.

Correctional “Free Lunch”? Cost Neglect Increases Punishment in Prosecutors

Prosecutors can influence judges’ sentencing decisions by the sentencing recommendations they make—but prosecutors are insulated from the costs of those sentences, which critics have described as a correctional “free lunch.” In a nationally distributed survey experiment, we show that when a sample of (n=178) professional prosecutors were insulated from sentencing cost information, their prison sentence recommendations were nearly one-third lengthier than sentences rendered following exposure to direct cost information. Exposure to a fiscally equivalent benefit of incarceration did not impact sentencing recommendations, as predicted. This pattern suggests that prosecutors implicitly value incorporating sentencing costs but selectively neglect them unless they are made explicit. These findings highlight a likely but previously unrecognized contributor to mass incarceration and identify a potential way to remediate it.