Automated Machine Learning (AutoML) approaches encompass traditional methods that optimize fixed pipelines for model selection and ensembling, as well as newer LLM-based frameworks that autonomously build pipelines. While LLM-based agents have shown promise in automating machine learning tasks, they often generate low-diversity and suboptimal code, even after multiple iterations. To overcome these limitations, we introduce Tree-Search Enhanced LLM Agents (SELA), an innovative agent-based system that leverages Monte Carlo Tree Search (MCTS) to optimize the AutoML process. By representing pipeline configurations as trees, our framework enables agents to conduct experiments intelligently and iteratively refine their strategies, facilitating a more effective exploration of the machine learning solution space. This novel approach allows SELA to discover optimal pathways based on experimental feedback, improving the overall quality of the solutions. In an extensive evaluation across 20 machine learning datasets, we compare the performance of traditional and agent-based AutoML methods, demonstrating that SELA achieves a win rate of 65% to 80% against each baseline across all datasets. Automated Machine Learning (AutoML) is a rapidly evolving field that seeks to automate the process of designing reliable machine learning solutions with minimal human intervention. Traditional AutoML frameworks, such as Auto-WEKA (Thornton et al., 2013), Auto-Sklearn (Feurer et al., 2015; 2020), AutoGluon (Tang et al., 2024b), and H2O AutoML (LeDell & Poirier, 2020), rely on predefined search spaces and routines. These frameworks primarily focus on optimizing hyperparameters and model ensembling to find the best model configuration. However, this fixed and static approach often lacks the adaptability needed to handle diverse and dynamic data scenarios, resulting in suboptimal performance in more complex settings.

Despite their impressive scale, knowledge bases (KBs), such as Wikidata, still contain significant gaps. Language models (LMs) have been proposed as a source for filling these gaps. However, prior works have focused on prominent entities with rich coverage by LMs, neglecting the crucial case of long-tail entities. In this paper, we present a novel method for LM-based-KB completion that is specifically geared for facts about long-tail entities. The method leverages two different LMs in two stages: for candidate retrieval and for candidate verification and disambiguation. To evaluate our method and various baselines, we introduce a novel dataset, called MALT, rooted in Wikidata. Our method outperforms all baselines in F1, with major gains especially in recall.

A chilling video featuring the faces of five Israeli women who were murdered by their husbands has gone viral in an eerie social media campaign that has brought them back to life after death. With artificial intelligence and animation capabilities from Israeli "creative reality" startup D-ID, the videos use the voice of each victim -- as well as realistic facial features and gestures -- to convey the message that someone living in the reality of domestic abuse can and should get out before its too late. The project, dubbed Listen To Our Voices, was created in response to a global and local surge in domestic violence since the start of the pandemic, and in honor of International Day for the Elimination of Violence Against Women on November 25. With deep learning technology, AI startup, D-ID captured the faces, voices, and gestures of the late Michal Sela, the late Esther Aharonovitch, the late Marin Haj Yechieh, the late Esther Barhani, and the late Sagit Ozeri, as they described their own marital difficulties which led to verbal and physical abuse from their spouses. The five victims also encouraged other women who experience similar relationships to talk to experts who know how to deal with these situations.

From writer-director Neil Burger ("Divergent") comes another young adult science-fiction tale, this one of a cruise ship in deep space full of restless teenagers under the supervision of a single adult. Some of the young people find out that the adult is keeping them drugged and docile and forcing them to reproduce artificially. Is that a recipe for YA trouble or what? Just when you thought you could not watch one more film of this kind, here is "Voyagers," a title that sounds enough like "Passengers" (2016) to put you off you spaceship-grown peas and carrots. The story is set in 2063 when Earth is ravaged, and scientists have searched for another planet to colonize.

American airport security has never been something to look forward to, but in the past few weeks, it's attained new levels of misery. The busy summer travel season is only just starting, and already the public's been warned to expect the worst. Understaffed Transport Security Administration checkpoints mean lines spilling out of airport doors. Hauled before Congress for a ritual grilling, TSA chief Peter Neffenger pledged to increase staffing (including sniffer dogs) and encourage more people to enroll in the TSA Pre-Check program, in which trusted travelers (who've undergone background checks and paid a fee) pass through a quicker, less rigorous screening. He sacked his head of security.