arXiv:2511.08846v1 Announce Type: cross 
Abstract: Topological descriptors have been increasingly utilized for capturing multiscale structural information in relational data. In this work, we consider various filtrations on the (box) product of graphs and the effect on their outputs on the topological descriptors - the Euler characteristic (EC) and persistent homology (PH). In particular, we establish a complete characterization of the expressive power of EC on general color-based filtrations. We also show that the PH descriptors of (virtual) graph products contain strictly more information than the computation on individual graphs, whereas EC does not. Additionally, we provide algorithms to compute the PH diagrams of the product of vertex- and edge-level filtrations on the graph product. We also substantiate our theoretical analysis with empirical investigations on runtime analysis, expressivity, and graph classification performance. Overall, this work paves way for powerful graph persistent descriptors via product filtrations. Code is available at https://github.com/Aalto-QuML/tda_graph_product.

On topological descriptors for graph products

Guardio is leveraging its experience building browser extensions and apps that scan for malicious and phishing sites to build a tool that looks for artifacts in code and websites made with vibe coding tools.

نجحت شركة Guardio الناشئة في مجال الأمن السيبراني في تأمين 80 مليون دولار من التمويل من ION Crossover Partners. تشتهر الشركة بخبرتها في تطوير إضافات المتصفح والتطبيقات التي تكشف عن المواقع الضارة والمحتالة. تخطط Guardio لاستخدام هذا التمويل لإنشاء أداة تبحث عن الآثار في الشيفرة والمواقع التي تم إنشاؤها باستخدام أدوات البرمجة vibe.

La startup de seguridad Guardio ha asegurado 80 millones de dólares en financiamiento de ION Crossover Partners. La empresa es conocida por su experiencia en el desarrollo de extensiones de navegador y aplicaciones que detectan sitios maliciosos y de phishing. Guardio planea utilizar estos fondos para crear una herramienta que identifique artefactos en el código y sitios web construidos con herramientas de codificación vibe.

La startup de sécurité Guardio a obtenu 80 millions de dollars de financement de la part d'ION Crossover Partners. Connue pour son expertise dans le développement d'extensions de navigateur et d'applications détectant les sites malveillants et de phishing, Guardio prévoit d'utiliser ce financement pour créer un outil identifiant les artefacts dans le code et les sites web construits avec des outils de codage vibe.

Security startup Guardio has secured $80 million in funding from ION Crossover Partners. The company is known for its expertise in developing browser extensions and applications that detect malicious and phishing websites. Guardio plans to utilize this funding to create a tool that identifies artifacts in code and websites built with vibe coding tools.

Security startup Guardio nabs $80M from ION Crossover Partners

A new artificial intelligence startup founded by the creators of <a href="https://opencv.org/">the world&#x27;s most widely used computer vision library</a> has emerged from stealth with technology that generates realistic human-centric videos up to five minutes long — a dramatic leap beyond the capabilities of rivals including OpenAI&#x27;s <a href="https://openai.com/sora/">Sora</a> and Google&#x27;s <a href="https://deepmind.google/models/veo/">Veo</a>.<a href="https://craftstory.com/">CraftStory</a>, which launched Tuesday with $2 million in funding, is introducing Model 2.0, a video generation system that addresses one of the most significant limitations plaguing the nascent AI video industry: duration. While OpenAI&#x27;s <a href="https://openai.com/index/sora-2/">Sora 2</a> tops out at 25 seconds and most competing models generate clips of 10 seconds or less, CraftStory&#x27;s system can produce continuous, coherent video performances that run as long as a typical YouTube tutorial or product demonstration.The breakthrough could unlock substantial commercial value for enterprises struggling to scale video production for training, marketing, and customer education — markets where brief AI-generated clips have proven inadequate despite their visual polish.&quot;If you really try to create a video with one of these video generation systems, you find that a lot of the times you want to implement a certain creative vision, and regardless of how detailed the instructions are, the systems basically ignore a part of your instructions,&quot; said Victor Erukhimov, CraftStory&#x27;s founder and CEO, in an exclusive interview with VentureBeat. &quot;We developed a system that can generate videos basically as long as you need them.&quot;<h3>How parallel processing solves the long-form video problem</h3>CraftStory&#x27;s advance rests on what the company describes as a parallelized diffusion architecture — a fundamentally different approach to how AI models generate video compared to the sequential methods employed by most competitors.Traditional video generation models work by running diffusion algorithms on increasingly large three-dimensional volumes where time represents the third axis. To generate a longer video, these models require proportionally larger networks, more training data, and significantly more computational resources.<a href="https://craftstory.com/">CraftStory</a> instead runs multiple smaller diffusion algorithms simultaneously across the entire duration of the video, with bidirectional constraints connecting them. &quot;The latter part of the video can influence the former part of the video too,&quot; Erukhimov explained. &quot;And this is pretty important, because if you do it one by one, then an artifact that appears in the first part propagates to the second one, and then it accumulates.&quot;Rather than generating eight seconds and then stitching on additional segments, CraftStory&#x27;s system processes all five minutes concurrently through interconnected diffusion processes.Crucially, CraftStory trained its model on proprietary footage rather than relying solely on internet-scraped videos. The company hired studios to shoot actors using high-frame-rate camera systems that capture crisp detail even in fast-moving elements like fingers — avoiding the motion blur inherent in standard 30-frames-per-second YouTube clips.&quot;What we showed is that you don&#x27;t need a lot of data and you don&#x27;t need a lot of training budget to create high quality videos,&quot; Erukhimov said. &quot;You just need high quality data.&quot;Model 2.0 currently operates as a video-to-video system: users upload a still image to animate and a &quot;driving video&quot; containing a person whose movements the AI will replicate. CraftStory provides preset driving videos shot with professional actors, who receive revenue shares when their motion data is used, or users can upload their own footage.The system generates 30-second clips at low resolution in approximately 15 minutes. An advanced lip-sync system synchronizes mouth movements to scripts or audio tracks, while gesture alignment algorithms ensure body language matches speech rhythm and emotional tone.<h3>Fighting a war chest battle with $2 million against billions</h3>CraftStory&#x27;s funding comes almost entirely from <a href="https://finance.yahoo.com/news/2-25-billion-exit-taught-130300997.html">Andrew Filev</a>, who sold his project management software company Wrike to Citrix for <a href="https://techcrunch.com/2021/01/19/citrix-is-acquiring-wrike-from-vista-for-2-25b/">$2.25 billion</a> in 2021 and now runs <a href="https://zencoder.ai/">Zencoder</a>, an AI coding company. The modest raise stands in stark contrast to the billions flowing into competing efforts — OpenAI has <a href="https://www.reuters.com/technology/artificial-intelligence/openai-closes-66-billion-funding-haul-valuation-157-billion-with-investment-2024-10-02/">raised over $6 billion</a> in its latest funding round alone.Erukhimov pushed back on the notion that massive capital is prerequisite for success. &quot;I don&#x27;t necessarily buy the thesis that compute is the path to success,&quot; he said. &quot;It definitely helps if you have compute. But if you raise a billion dollars on a PowerPoint, in the end, no one is happy, neither the founders nor the investors.&quot;Filev defended the David-versus-Goliath approach. &quot;When you invest in startups, you&#x27;re fundamentally betting on people,&quot; he said in an interview with VentureBeat. &quot;To paraphrase Margaret Mead: never underestimate what a small group of thoughtful, committed engineers and scientists can build.&quot;He argued that CraftStory benefits from a focused strategy. &quot;The big labs are in an arms race to build general-purpose video foundation models,&quot; Filev said. &quot;CraftStory is riding that wave and going very deep into a specific format: long-form, engaging, human-centric video.&quot;<h3>Why computer vision expertise matters in generative AI video</h3>Erukhimov&#x27;s credibility stems from his deep roots in computer vision rather than the transformer architectures that have dominated recent AI advances. He was an early contributor to <a href="https://opencv.org/">OpenCV</a> — the Open Source Computer Vision Library that has become the de facto standard for computer vision applications, with over <a href="https://github.com/opencv/opencv">84,000 stars on GitHub</a>.When Intel reduced its support for OpenCV in the mid-2000s, Erukhimov co-founded Itseez with the explicit goal of maintaining and advancing the library. The company expanded OpenCV significantly and pivoted toward automotive safety systems before Intel acquired it in 2016.Filev said this background is precisely what makes Erukhimov well-positioned for video generation. &quot;What people sometimes miss is that generative AI video isn&#x27;t just about the generative part. It&#x27;s about understanding motion, facial dynamics, temporal coherence, and how humans actually move,&quot; Filev said. &quot;Victor has spent his career mastering exactly those problems.&quot;<h3>Enterprise focus targets training videos and product demos</h3>While much of the public excitement around AI video generation has centered on creative tools for consumers, CraftStory is pursuing a decidedly enterprise-focused strategy.&quot;We are definitely thinking about B2B more than consumer,&quot; Erukhimov said. &quot;We&#x27;re thinking about companies, specifically software companies, being able to make cool training videos and product videos and launch videos.&quot;The logic is straightforward: corporate training, product tutorials, and customer education videos often run several minutes and require consistent quality throughout. A 10-second AI clip cannot effectively demonstrate how to use enterprise software or explain a complex product feature.&quot;If you need a longer-form video, then you should go with us,&quot; Erukhimov said. &quot;We can create up to five minutes, consistent video, high quality.&quot;Filev echoed this assessment. &quot;One huge gap in this market is the lack of models that can generate consistent videos over longer sequences — and that&#x27;s extremely important for real-world use,&quot; he said. &quot;If you&#x27;re creating a commercial for your company, a 10-second video, no matter how good it looks, just isn&#x27;t enough. You need 30 seconds, you need two minutes — you need more.&quot;The company anticipates cost savings for customers. Filev suggested that &quot;a small business owner could create content in minutes that previously would have cost $20,000 and taken two months to produce.&quot;CraftStory is also courting creative agencies that produce video content for corporate clients, with the value proposition centered on cost and speed: agencies can record an actor on camera and transform that footage into a finished AI video, rather than managing expensive multi-day shoots.The next major development on CraftStory&#x27;s roadmap is a text-to-video model that would allow users to generate long-form content directly from scripts. The team is also developing support for moving-camera scenarios, including the popular &quot;walk-and-talk&quot; format common in high-end advertising.<h3>Where CraftStory fits in a fragmented competitive landscape</h3>CraftStory enters a crowded and rapidly evolving market. OpenAI&#x27;s <a href="https://openai.com/index/sora-2/">Sora 2</a>, while not yet publicly available, has generated significant buzz. Google&#x27;s <a href="https://deepmind.google/models/veo/">Veo models</a> are advancing quickly. <a href="https://runwayml.com/">Runway</a>, <a href="https://pika.art/login">Pika</a>, and <a href="https://stability.ai/">Stability AI </a>all offer video generation tools with different capabilities.Erukhimov acknowledged the competitive pressure but emphasized that CraftStory serves a distinct niche focused on human-centric videos. He positioned rapid innovation and market capture as the company&#x27;s primary strategy rather than relying on technical moats.Filev sees the market fragmenting into distinct layers, with large tech companies serving as &quot;API providers of powerful, general-purpose generation models&quot; while specialized players like CraftStory focus on specific use cases. &quot;If the big players are building the engines, CraftStory is building the production studio and assembly line on top,&quot; he said.Model 2.0 is available now at app.craftstory.com/model-2.0, with the company offering early access to users and enterprises interested in testing the technology. Whether a lightly-funded startup can capture meaningful market share against deep-pocketed incumbents remains uncertain, but Erukhimov is characteristically confident about the opportunity ahead.&quot;AI-generated video will soon become the primary way companies communicate their stories,&quot; he said.

أطلقت CraftStory، وهي شركة ناشئة جديدة في مجال الذكاء الاصطناعي أسسها مبتكرو OpenCV، نظامًا لتوليد الفيديو قادرًا على إنتاج مقاطع فيديو واقعية تركز على الإنسان تصل مدتها إلى خمس دقائق. تتجاوز هذه التكنولوجيا بشكل كبير قدرات المنافسين مثل Sora من OpenAI وVeo من Google، الذين لديهم حدود زمنية أقصر. حصلت الشركة الناشئة على تمويل بقيمة مليوني دولار لدعم نهجها المبتكر في صناعة الفيديو بالذكاء الاصطناعي.

CraftStory, una nueva startup de IA fundada por los creadores de OpenCV, ha lanzado un sistema de generación de video capaz de producir videos realistas centrados en humanos de hasta cinco minutos de duración. Esta tecnología supera significativamente a competidores como Sora de OpenAI y Veo de Google, que tienen límites de duración más cortos. La startup ha asegurado 2 millones de dólares en financiamiento para apoyar su enfoque innovador en la industria de video de IA.

CraftStory, une nouvelle startup d'IA fondée par les créateurs d'OpenCV, a lancé un système de génération vidéo capable de produire des vidéos réalistes centrées sur l'humain d'une durée allant jusqu'à cinq minutes. Cette technologie surpasse considérablement les concurrents tels que Sora d'OpenAI et Veo de Google, qui ont des limites de durée plus courtes. La startup a sécurisé 2 millions de dollars de financement pour soutenir son approche innovante de l'industrie vidéo IA.

CraftStory, a new AI startup founded by the creators of OpenCV, has launched a video generation system capable of producing realistic human-centric videos up to five minutes long. This technology significantly outpaces competitors like OpenAI's Sora and Google's Veo, which have shorter duration limits. The startup has secured $2 million in funding to support its innovative approach to the AI video industry.

OpenCV founders launch AI video startup to take on OpenAI and Google

<a href="https://petapixel.com/2025/11/19/remote-cameras-may-have-captured-first-recorded-tool-use-by-a-wild-wolf/"><img width="1600" height="840" src="https://petapixel.com/assets/uploads/2025/11/wolve-tool-use.jpg" class="attachment-card-large size-card-large wp-post-image" alt="A wolf stands at the edge of a rocky shoreline, holding an orange and white fishing bobber in its mouth. A fishing net and additional gear are lying on the ground nearby. Rippling water is in the background." decoding="async" fetchpriority="high" /></a>Remote cameras have captured footage of wild wolves pulling crab traps out of the sea by their lines to eat the bait inside -- in the first evidence of possible tool use by the canines.
[<a href="https://petapixel.com/2025/11/19/remote-cameras-may-have-captured-first-recorded-tool-use-by-a-wild-wolf/">Read More</a>]

التقطت الكاميرات عن بُعد لقطات لذئاب برية تستخدم تقنية تتضمن سحب فخاخ السلطعون من البحر للوصول إلى الطعم داخلها. وهذا يمثل أول دليل موثق على استخدام محتمل للأدوات من قبل هذه الكلاب.

Cámaras remotas han grabado a lobos salvajes utilizando una técnica que consiste en sacar trampas de cangrejos del mar para acceder al cebo en su interior. Esto marca la primera evidencia documentada de un posible uso de herramientas por parte de estos caninos.

Des caméras à distance ont enregistré des loups sauvages utilisant une technique qui consiste à tirer des pièges à crabes de la mer pour accéder à l'appât à l'intérieur. Cela marque la première preuve documentée d'un potentiel usage d'outils par ces canidés.

Remote cameras have recorded wild wolves using a technique that involves pulling crab traps from the sea to access the bait inside. This marks the first documented evidence of potential tool use by these canines.

Remote Cameras May Have Captured First Recorded Tool Use by a Wild Wolf

The AI startup Firebird Inc. has received US government approval to export Nvidia Corp. chips to Armenia for a supercomputer project in the country, part of a global push to expand artificial intelligence infrastructure.

حصلت شركة Firebird Inc. الناشئة في مجال الذكاء الاصطناعي على موافقة الحكومة الأمريكية لتصدير شرائح Nvidia Corp. إلى أرمينيا، مما يسهل إنشاء مشروع حاسوب فائق في البلاد. تأتي هذه المبادرة كجزء من جهد عالمي أوسع لتعزيز بنية الذكاء الاصطناعي التحتية.

La startup de IA Firebird Inc. ha recibido la aprobación del gobierno de EE. UU. para exportar chips de Nvidia Corp. a Armenia, facilitando el establecimiento de un proyecto de supercomputadora en el país. Esta iniciativa forma parte de un esfuerzo global más amplio para mejorar la infraestructura de inteligencia artificial.

La startup d'IA Firebird Inc. a obtenu l'approbation du gouvernement américain pour exporter des puces de Nvidia Corp. en Arménie, facilitant ainsi l'établissement d'un projet de superordinateur dans le pays. Cette initiative s'inscrit dans un effort mondial plus large pour améliorer l'infrastructure de l'intelligence artificielle.

AI startup Firebird Inc. has obtained approval from the US government to export Nvidia Corp. chips to Armenia, facilitating the establishment of a supercomputer project in the country. This initiative is part of a broader global effort to enhance artificial intelligence infrastructure.

AI Startup Firebird Gets US Approval to Use Nvidia Chips in Armenian Data Center

Gesture of goodwill temporarily cools EU–China dispute that rattled global car supply chains.
The post <a href="https://www.techrepublic.com/article/news-netherlands-nexperia-chipmaker-control/">Netherlands Pauses Move to Seize Chinese-Owned Chipmaker Nexperia</a> appeared first on <a href="https://www.techrepublic.com">TechRepublic</a>.

أوقفت هولندا مؤقتًا جهودها للاستيلاء على شركة Nexperia لصناعة الرقائق المملوكة للصين، وهو إجراء يُعتبر بمثابة لفتة حسن نية تهدف إلى تهدئة التوترات في النزاع القائم بين الاتحاد الأوروبي والصين. تأتي هذه الخطوة وسط مخاوف بشأن تأثيرها على سلاسل الإمداد العالمية التي تأثرت بالفعل بنقص أشباه الموصلات.

Los Países Bajos han detenido temporalmente sus esfuerzos por apoderarse del fabricante de chips Nexperia, de propiedad china, un gesto que se considera como una buena voluntad para aliviar las tensiones en la disputa entre la UE y China. Esta decisión se produce en medio de preocupaciones sobre el impacto en las cadenas de suministro globales, que ya se han visto afectadas por la escasez de semiconductores.

Les Pays-Bas ont temporairement suspendu leurs efforts pour saisir le fabricant de puces Nexperia, détenu par des Chinois, un geste perçu comme une volonté d'apaiser les tensions dans le conflit en cours entre l'UE et la Chine. Cette décision intervient dans un contexte d'inquiétudes concernant l'impact sur les chaînes d'approvisionnement mondiales, déjà affectées par des pénuries de semi-conducteurs.

The Netherlands has temporarily halted its efforts to seize control of the Chinese-owned chipmaker Nexperia, a move seen as a gesture of goodwill aimed at easing tensions in the ongoing EU-China dispute. This decision comes amid concerns over the impact on global car supply chains, which have been affected by semiconductor shortages.

Netherlands Pauses Move to Seize Chinese-Owned Chipmaker Nexperia

استقال لاري سامرز من مجلس إدارة OpenAI، كما أفاد نيويورك تايمز. تأتي استقالته بعد التدقيق في اتصالاته السابقة مع المدان بجريمة الاعتداء الجنسي جيفري إبستين. تمثل هذه الخطوة انسحابًا كبيرًا لسامرز من الأدوار العامة وسط انتقادات متزايدة.

Larry Summers ha renunciado a la junta de OpenAI, según informa The New York Times. Su renuncia se produce tras el escrutinio sobre sus comunicaciones pasadas con el delincuente sexual condenado Jeffrey Epstein. Esta decisión marca un paso significativo en el retiro de Summers de los roles públicos en medio de una creciente crítica.

Larry Summers a démissionné du conseil d'administration d'OpenAI, selon le New York Times. Sa démission fait suite à un examen minutieux de ses communications passées avec le délinquant sexuel condamné Jeffrey Epstein. Cette décision marque un retrait significatif de Summers de ses rôles publics face à une critique croissante.

Larry Summers has resigned from the board of OpenAI, as reported by The New York Times. His resignation follows scrutiny over his past communications with convicted sex offender Jeffrey Epstein. This decision marks a significant step in Summers' withdrawal from public roles amid growing criticism.

Larry Summers Resigns From OpenAI’s Board

arXiv:2511.13911v1 Announce Type: cross 
Abstract: Despite recent progress in predicting biomarker trajectories from real clinical data, uncertainty in the predictions poses high-stakes risks (e.g., misdiagnosis) that limit their clinical deployment. To enable safe and reliable use of such predictions in healthcare, we introduce a conformal method for uncertainty-calibrated prediction of biomarker trajectories resulting from randomly-timed clinical visits of patients. Our approach extends conformal prediction to the setting of randomly-timed trajectories via a novel nonconformity score that produces prediction bands guaranteed to cover the unknown biomarker trajectories with a user-prescribed probability. We apply our method across a wide range of standard and state-of-the-art predictors for two well-established brain biomarkers of Alzheimer's disease, using neuroimaging data from real clinical studies. We observe that our conformal prediction bands consistently achieve the desired coverage, while also being tighter than baseline prediction bands. To further account for population heterogeneity, we develop group-conditional conformal bands and test their coverage guarantees across various demographic and clinically relevant subpopulations. Moreover, we demonstrate the clinical utility of our conformal bands in identifying subjects at high risk of progression to Alzheimer's disease. Specifically, we introduce an uncertainty-calibrated risk score that enables the identification of 17.5% more high-risk subjects compared to standard risk scores, highlighting the value of uncertainty calibration in real-world clinical decision making. Our code is available at github.com/vatass/ConformalBiomarkerTrajectories.

تم تطوير طريقة جديدة للتنبؤ بمسارات المؤشرات الحيوية في مرض الزهايمر، حيث تعالج عدم اليقين في التنبؤات السريرية. تتيح هذه التقنية التنبؤية المتوافقة إجراء تنبؤات موثوقة من الزيارات السريرية التي تتم في أوقات عشوائية، مما يضمن أن تغطي نطاقات التنبؤ المسارات الفعلية للمؤشرات الحيوية باحتمالية محددة. تم اختبار النهج باستخدام بيانات التصوير العصبي من دراسات سريرية معروفة، مما يوضح فعاليته في تعزيز أمان التطبيقات السريرية.

Se ha desarrollado un nuevo método para predecir las trayectorias de biomarcadores en la enfermedad de Alzheimer, abordando la incertidumbre en las predicciones clínicas. Esta técnica de predicción conforme permite pronósticos fiables a partir de visitas clínicas programadas aleatoriamente, asegurando que las bandas de predicción cubran las trayectorias reales de los biomarcadores con una probabilidad especificada. El enfoque se probó utilizando datos de neuroimagen de estudios clínicos establecidos, demostrando su eficacia para mejorar la seguridad de las aplicaciones clínicas.

Une nouvelle méthode de prédiction des trajectoires de biomarqueurs dans la maladie d'Alzheimer a été développée, abordant l'incertitude des prédictions cliniques. Cette technique de prédiction conforme permet des prévisions fiables à partir de visites cliniques chronométrées de manière aléatoire, garantissant que les bandes de prédiction couvrent les trajectoires réelles des biomarqueurs avec une probabilité spécifiée. L'approche a été testée à l'aide de données d'imagerie cérébrale provenant d'études cliniques établies, démontrant son efficacité pour améliorer la sécurité des applications cl…

A new method for predicting biomarker trajectories in Alzheimer's disease has been developed, addressing the uncertainty in clinical predictions. This conformal prediction technique allows for reliable forecasts from randomly-timed clinical visits, ensuring that the prediction bands cover the actual biomarker trajectories with a specified probability. The approach was tested using neuroimaging data from established clinical studies, demonstrating its effectiveness in enhancing the safety of clinical applications.

Uncertainty-Calibrated Prediction of Randomly-Timed Biomarker Trajectories with Conformal Bands

arXiv:2511.14441v1 Announce Type: cross 
Abstract: To distinguish Markov equivalent graphs in causal discovery, it is necessary to restrict the structural causal model. Crucially, we need to be able to distinguish cause $X$ from effect $Y$ in bivariate models, that is, distinguish the two graphs $X \to Y$ and $Y \to X$. Location-scale noise models (LSNMs), in which the effect $Y$ is modeled based on the cause $X$ as $Y = f(X) + g(X)N$, form a flexible class of models that is general and identifiable in most cases. Estimating these models for arbitrary noise terms $N$, however, is challenging. Therefore, practical estimators are typically restricted to symmetric distributions, such as the normal distribution. As we showcase in this paper, when $N$ is a skewed random variable, which is likely in real-world domains, the reliability of these approaches decreases. To approach this limitation, we propose SkewD, a likelihood-based algorithm for bivariate causal discovery under LSNMs with skewed noise distributions. SkewD extends the usual normal-distribution framework to the skew-normal setting, enabling reliable inference under symmetric and skewed noise. For parameter estimation, we employ a combination of a heuristic search and an expectation conditional maximization algorithm. We evaluate SkewD on novel synthetically generated datasets with skewed noise as well as established benchmark datasets. Throughout our experiments, SkewD exhibits a strong performance and, in comparison to prior work, remains robust under high skewness.

تناقش الورقة الحاجة إلى التمييز بين السبب والأثر في النماذج الثنائية المتغيرة لاكتشاف الأسباب، وخاصة ضمن نماذج الضوضاء ذات الموقع والمقياس (LSNMs). تسلط الضوء على التحدي المتمثل في تقدير هذه النماذج عندما تكون مصطلحات الضوضاء مائلة، وهو أمر شائع في السيناريوهات الواقعية. يقدم المؤلفون SkewD، وهو خوارزمية قائمة على الاحتمالية مصممة لتحسين اكتشاف الأسباب الثنائية في هذه الظروف، مع معالجة القيود التي تواجهها المقدرات التقليدية التي تعتمد عادةً على التوزيعات المتماثلة.

El artículo discute la necesidad de distinguir entre causa y efecto en modelos bivariados para el descubrimiento causal, específicamente dentro de los modelos de ruido de localización-escala (LSNMs). Destaca el desafío de estimar estos modelos cuando los términos de ruido son sesgados, lo cual es común en escenarios del mundo real. Los autores presentan SkewD, un algoritmo basado en la verosimilitud diseñado para mejorar el descubrimiento causal bivariado en estas condiciones, abordando las limitaciones de los estimadores tradicionales que generalmente dependen de distribuciones simétricas.

Cet article aborde la nécessité de distinguer la cause de l'effet dans les modèles bivariés pour la découverte causale, en particulier dans les modèles de bruit de localisation-échelle (LSNMs). Il met en évidence le défi d'estimer ces modèles lorsque les termes de bruit sont asymétriques, ce qui est courant dans les scénarios du monde réel. Les auteurs introduisent SkewD, un algorithme basé sur la vraisemblance conçu pour améliorer la découverte causale bivariée dans ces conditions, en répondant aux limitations des estimateurs traditionnels qui reposent généralement sur des distributions symét…

The paper discusses the need to distinguish between cause and effect in bivariate models for causal discovery, specifically within location-scale noise models (LSNMs). It highlights the challenge of estimating these models when noise terms are skewed, which is common in real-world scenarios. The authors introduce SkewD, a likelihood-based algorithm designed to improve bivariate causal discovery under these conditions, addressing the limitations of traditional estimators that typically rely on symmetric distributions.

Skewness-Robust Causal Discovery in Location-Scale Noise Models

arXiv:2412.09498v3 Announce Type: replace-cross 
Abstract: Gradient descent is one of the most widely used iterative algorithms in modern statistical learning. However, its precise algorithmic dynamics in high-dimensional settings remain only partially understood, which has limited its broader potential for statistical inference applications.
  This paper provides a precise, non-asymptotic joint distributional characterization of gradient descent iterates and their debiased statistics in a broad class of empirical risk minimization problems, in the so-called mean-field regime where the sample size is proportional to the signal dimension. Our non-asymptotic state evolution theory holds for both general non-convex loss functions and non-Gaussian data, and reveals the central role of two Onsager correction matrices that precisely characterize the non-trivial dependence among all gradient descent iterates in the mean-field regime.
  Leveraging the joint state evolution characterization, we show that the gradient descent iterate retrieves approximate normality after a debiasing correction via a linear combination of all past iterates, where the debiasing coefficients can be estimated by the proposed gradient descent inference algorithm. This leads to a new algorithmic statistical inference framework based on debiased gradient descent, which (i) applies to a broad class of models with both convex and non-convex losses, (ii) remains valid at each iteration without requiring algorithmic convergence, and (iii) exhibits a certain robustness to possible model misspecification. As a by-product, our framework also provides algorithmic estimates of the generalization error at each iteration. As canonical examples, we demonstrate our theory and inference methods in the single-index regression model and a generalized logistic regression model, where the natural loss functions may exhibit arbitrarily non-convex landscapes.

يُعتبر الانحدار التدرجي خوارزمية تكرارية تُستخدم على نطاق واسع في التعلم الإحصائي، إلا أن دينامياتها في الإعدادات عالية الأبعاد لا تزال غير مفهومة تمامًا. يقدم هذا البحث توصيفًا مشتركًا غير حدّي لتكرارات الانحدار التدرجي وإحصائياتها المُعالجة في مشاكل تقليل المخاطر التجريبية، لا سيما في نظام الحقل المتوسط. تسلط النتائج الضوء على الدور المركزي لمصفوفات تصحيح أونساجر في فهم الاعتماديات بين جميع تكرارات الانحدار التدرجي، القابلة للتطبيق على كل من دوال الخسارة غير المحدبة والبيانات غير الغاوسية.

El descenso de gradiente es un algoritmo iterativo ampliamente utilizado en el aprendizaje estadístico, pero su dinámica en entornos de alta dimensión no se comprende completamente. Este artículo proporciona una caracterización no asintótica de los iterados del descenso de gradiente y sus estadísticas desbiaseadas en problemas de minimización del riesgo empírico, especialmente en el régimen de campo medio. Los hallazgos destacan el papel central de las matrices de corrección de Onsager en la comprensión de las dependencias entre los iterados del descenso de gradiente, aplicables tanto a funcio…

La descente de gradient est un algorithme itératif largement utilisé dans l'apprentissage statistique, mais sa dynamique dans des contextes de haute dimension n'est pas entièrement comprise. Cet article présente une caractérisation non asymptotique des itérations de la descente de gradient et de leurs statistiques débiaisées dans des problèmes de minimisation du risque empirique, en particulier dans le régime de champ moyen. Les résultats mettent en évidence l'importance des matrices de correction d'Onsager pour comprendre les dépendances entre les itérations de la descente de gradient, applic…

Gradient descent is a widely used iterative algorithm in statistical learning, yet its dynamics in high-dimensional settings are not fully understood. This paper presents a non-asymptotic characterization of gradient descent iterates and their debiased statistics in empirical risk minimization problems, particularly in the mean-field regime. The findings highlight the significance of Onsager correction matrices in understanding the dependencies among gradient descent iterates, applicable to both non-convex loss functions and non-Gaussian data.

On topological descriptors for graph products

Was this article worth reading? Share it