This semester, I—as a teacher—came across two cases of heavily reliance on AI by master students, mostly for coding purposes, to which I had rather surprisingly not been exposed before. (Except for this plagiarised thesis two years ago that essentially rewrote existing papers with synonyms and for which we had to get to the disciplinary committee!) One project made a massive advance within two days, with hundreds of lines of beautiful python code, and reasonable output, but with my student unable to explain the code or the method behind… And anther case homeworks involving coding came back with extremely clean codes as well. Meaning they could not be graded and we had to switch to another type of evaluation. Oh well, welcome ol’me into the new age (just for a few years!)
Archive for master project
fAIrst contAIct
Posted in Books, Kids, pictures, Statistics, University life with tags ChatGPT, coding, debugging, discipline, master project, plagiarism, Python, R, teaching, Université Paris Dauphine, University of Warwick on November 12, 2025 by xi'anpositive response to negative mixtures
Posted in pictures, Running with tags academic publisher, accept-reject algorithm, acceptance, acceptance probability, article, COVID-19, inverse cdf, master project, PSL Research University, publication, signed mixture, simulation, Statistics and Computing, Université Paris Dauphine, ziggurat algorithm on December 17, 2024 by xi'an
Hurray, our signed mixture simulation paper has been accepted by Statistics & Computing! If Og’s readers remember my earlier post about this problem, things get surprisingly more complicated when the mixture weights can take negative values. For instance, the naïve solution consisting in first simulating from the associated mixture of positive weight components and then using an accept-reject step may prove highly inefficient since the overall probability of acceptance can get arbitrarily close to zero. Substituting to this naïve version, we construct an alternative accept-reject scheme based on pairing positive and negative components as efficiently as possible, partitioning the real line, and finding tighter upper and lower bounds on positive and negative components, respectively, towards yielding a higher acceptance rate on average. In retrospect, the problem was beyond the reach of the undergraduate students we supervised (pre-COVID) on a research internship!
