Multi-arm bandit experimental designs are increasingly being adopted over standard randomized trials due to their potential to improve outcomes for study participants, enable faster identification of the best-performing options, and/or enhance the precision of estimating key parameters. Current approaches for inference after adaptive sampling either rely on asymptotic normality under restricted experiment designs or underpowered martingale concentration inequalities that lead to weak power in practice. To bypass these limitations, we propose a simulation-based approach for conducting hypothesis tests and constructing confidence intervals for arm specific means and their differences. Our simulation-based approach uses positively biased nuisances to generate additional trajectories of the experiment, which we call simulation with optimism. Using these simulations, we characterize the distribution potentially non-normal sample mean test statistic to conduct inference. We provide guarantees for (i) asymptotic type I error control, (ii) convergence of our confidence intervals, and (iii) asymptotic strong consistency of our estimator over a wide variety of common bandit designs. Our empirical results show that our approach achieves the desired coverage while reducing confidence interval widths by up to 50%, with drastic improvements for arms not targeted by the design.

The ViT-L/14 encoder processes images into visual tokens, which the LLaMA-2-7B decoder converts into text.

We propose SPARTAALIGNMENT, an algorithm to collectively align multiple LLMs through competition and combat. To complement a single model's lack of diversity in generation and biases in evaluation, multiple LLMs form a "sparta tribe" to compete against each other in fulfilling instructions while serving as judges for the competition of others. For each iteration, one instruction and two models are selected for a duel, the other models evaluate the two responses, and their evaluation scores are aggregated through a adapted elo-ranking based reputation system, where winners/losers of combat gain/lose weight in evaluating others.

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NVIDIA introduces Halos for Robotics, which the company calls the industry's first full-stack safety system for robotics and physical AI operating near people.

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Discovering novel molecules requires accurate out-of-distribution (OOD) predictions, but ML models struggle to generalize OOD. Currently, no systematic benchmarks exist for molecular OOD prediction tasks.

Cory Doctorow speaks at a digital society conference. Cory Doctorow speaks at a digital society conference. The Reverse Centaur's Guide to Life After AI by Cory Doctorow review - the real price of artificial intelligence A s former Google CEO Eric Schmidt could tell you, AI is a hard sell these days. Last month, he tried talking up the AI revolution during a commencement address at the University of Arizona and was loudly booed by students about to enter an AI-ravaged job market. Schmidt is not the only AI booster to crash out with students recently as the popular backlash grows.

Things to Do in L.A. Tap to enable a layout that focuses on the article. Alberto Carvalho, who resigned Sunday as LAUSD superintendent, addresses students at an elementary school in 2023. This is read by an automated voice. Please report any issues or inconsistencies here . Alberto Carvalho resigned Sunday night.