Regardless of whether you plan to use them in applications, everyone should learn about Gaussian processes, and Bayesian methods. They provide a foundation for reasoning about model construction and all sorts of deep learning behaviour that would otherwise appear mysterious.
Daniel García Rasines, profesor del Departamento de Métodos Cuantitativos de CUNEF Universidad, ha sido galardonado con el Premio SEIO – Fundación BBVA a la mejor contribución metodológica en Estadística.
El jurado ha reconocido su artículo “Splitting strategies for post-selection inference”, publicado en Biometrika, junto al profesor G. Alastair Young, catedrático de Estadística en el Imperial College de Londres.
La investigación facilita el análisis de datos cuando hay un gran número de variables. Los autores proponen un esquema para añadir ruido aleatorio a un conjunto de datos que permite identificar las variables más relevantes y estimar sus efectos de manera rigurosa.
¡Enhorabuena!
¿Por qué @bicimad tiene tantas estaciones vacías y @bicing no, si los dos sistemas tienen 7000 bicis?
No, no son los riders, sino dos fallos que han cometido en Madrid que tienen solución. Hilo va->
Our paper, “Poisoning Bayesian Inference via Data Deletion and Replication,” has been accepted to #AISTATS25.
In it, we propose “posterior attraction problems”, a new family of challenges in Bayesian robustness.
Stay tuned for more.
If you are Canadian, European, Asian, Latin American, African or wherever, it's time to leave X, join millions of others who have now migrated to #BlueSky
Find us here today: https://t.co/OEjOmkGsKw
Industrial Revolution brought environmental pollution, and Artificial Intelligence brings information contamination. It is getting increasingly serious. Pollution destroys the environment, but contaminated information destroys civilizations.
People have too inflated sense of what it means to "ask an AI" about something. The AI are language models trained basically by imitation on data from human labelers. Instead of the mysticism of "asking an AI", think of it more as "asking the average data labeler" on the internet.
Few caveats apply because e.g. in many domains (e.g. code, math, creative writing) the companies hire skilled data labelers (so think of it as asking them instead), and this is not 100% true when reinforcement learning is involved, though I have an earlier rant on how RLHF is just barely RL, and "actual RL" is still too early and/or constrained to domains that offer easy reward functions (math etc.).
But roughly speaking (and today), you're not asking some magical AI. You're asking a human data labeler. Whose average essence was lossily distilled into statistical token tumblers that are LLMs. This can still be super useful ofc ourse. Post triggered by someone suggesting we ask an AI how to run the government etc. TLDR you're not asking an AI, you're asking some mashup spirit of its average data labeler.
Geoff and John are a truly inspired choice for the Nobel Prize in Physics. Not only because they have done groundbreaking work for machine learning research, but also since this choice reflects an understanding that machine learning methods are changing how science is done (1/2)
Two Bayesian postdocs in Adversarial Machine Learning and Collective Behaviour at ICMAT supervised by me. Applications until December 31st through https://t.co/1sVGGuuEZ4 #postdoc#MachineLearning@ICMAT@AIhubCSIC
Workshop "Mathematical optimization and statistics for explainable and fair machine learning (Edition 2024)
September 19th 2024, at @imus_us
https://t.co/VBaOLASD5a
Are you missing it???
"Hay mucha prisa por incorporar los sistemas de IA. Pero no debe hacerse sin atender los problemas que pueden surgir y atajarlos", entrevista con @roinaveiro, quien hizo la tesis en @_ICMAT, en @lavozdegalicia https://t.co/5KsaM26Et4
"Intentamos hacer sistemas [de inteligencia artificial] más robustos que aguanten los ataques" - @davidrinsua de sus aportaciones desde el análisis de riesgo adversario para enfrentar los riesgos de la IA en @elpais_tec https://t.co/CDkQIzjVhg