Tootfinder

Senate Passes Stablecoin Bill In Major Win For Crypto Industry - Slashdot
https://slashdot.org/story/25/06/18/0036236/senate-passes-stablecoin-bill-in-major-win-for-crypto-industry

Nach Alpha-Feedback: Bungie verschiebt "Marathon" auf unbestimmte Zeit
Für das Studio Bungie sind es schwierige Monate: Nach dem Kunstklau-Ärger um "Marathon" fiel auch das Alpha-Feedback schlecht aus. Der Shooter wird verschoben.

from my link log —
Toxic Origins, Toxic Decisions: bias in CEO selection towards risk-taking, analysed using polluted Superfund sites.
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5270031
saved 2025-05-31

SENIOR: Efficient Query Selection and Preference-Guided Exploration in Preference-based Reinforcement Learning
Hexian Ni, Tao Lu, Haoyuan Hu, Yinghao Cai, Shuo Wang
https://arxiv.org/abs/2506.14648

SENIOR: Efficient Query Selection and Preference-Guided Exploration in Preference-based Reinforcement Learning
Preference-based Reinforcement Learning (PbRL) methods provide a solution to avoid reward engineering by learning reward models based on human preferences. However, poor feedback- and sample- efficiency still remain the problems that hinder the application of PbRL. In this paper, we present a novel efficient query selection and preference-guided exploration method, called SENIOR, which could select the meaningful and easy-to-comparison behavior segment pairs to improve human feedback-efficiency…

Was die „Aber China … !“-Rufer jetzt wohl machen wenn über den Öl-Ausstieg diskutiert wird?
Clean Energy Just Put China's CO2 Emissions Into Reverse For First Time - Slashdot https://news.slashdot.org/story/25/05/

Unsere Sieger-Teams der Schulschachmeisterschaft WK HR:
🥇 SARB Hasbergen I
🥈 Johann-Pachelbel-Realschule Nürnberg
🥉 Freie Schule Oldenburg
▶️ Vollständige Schlusstabelle: https://www.deutsche-schachjugend.de/2025/dsm-wk-hr/

Refract ICL: Rethinking Example Selection in the Era of Million-Token Models
Arjun R. Akula, Kazuma Hashimoto, Krishna Srinivasan, Aditi Chaudhary, Karthik Raman, Michael Bendersky
https://arxiv.org/abs/2506.12346

Refract ICL: Rethinking Example Selection in the Era of Million-Token Models
The emergence of long-context large language models (LLMs) has enabled the use of hundreds, or even thousands, of demonstrations for in-context learning (ICL) - a previously impractical regime. This paper investigates whether traditional ICL selection strategies, which balance the similarity of ICL examples to the test input (using a text retriever) with diversity within the ICL set, remain effective when utilizing a large number of demonstrations. Our experiments demonstrate that, while longer…

For me if someone schedules a meeting before 08:00 and after 15:00 it is an automatic NO.
Early morning mail checks (before 07:00) is done only when I am on call and to see if there was any emergencies in the prior evening production run that I missed. When I do that my day starts at that time! I adjust the end of day time to allow for the early start. Normal hours for me is 07:00 to 15:30.

Generalizable estimation of conditional average treatment effects using Causal Forest in randomized
Rikuta Hamaya, Etsuji Suzuki, Konan Hara
https://arxiv.org/abs/2506.12296

Generalizable estimation of conditional average treatment effects using Causal Forest in randomized
Generalizing conditional average treatment effects (CATE) estimates in a randomized controlled trial (RCT) to a broader source population can be challenging because of selection bias and high-dimensional covariates. We aim to evaluate CATE estimation approaches using Causal Forest that address selection bias due to trial participation. We propose and compare four CATE estimation approaches using Causal Forest: (1) ignoring selection variables, (2) including selection variables, (3) using invers…

California AI Policy Report Warns of 'Irreversible Harms' - Slashdot
https://yro.slashdot.org/story/25/06/17/214215/california-ai-policy-report-warns-of-irreversible-harms