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DTSTART;VALUE=DATE:20251201
DTEND;VALUE=DATE:20251203
DTSTAMP:20260521T133313
CREATED:20250903T222016Z
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SUMMARY:Workshop on Topology\, Algebra\, and Geometry in Data Science (co-located with NeurIPS 2025)
DESCRIPTION:We are thrilled to announce the first official TAG-DS Stand-Alone Event–TAG… We’re it! This will be a two day event\, December 1 & 2\, 2025\, featuring keynotes\, poster sessions\, spotlight talks\, collaboration activities\, and community development. The dates and location were selected to align with NeurIPS 2025–twice the fun! The event will be hosted on the University of California San Diego campus both days and is readily accessible by public transit from downtown for those already planning to attend NeurIPS. There will be an associated Proceedings of Machine Learning Research volume for papers submitted to the archival track.
URL:https://tilos.ai/event/topology-algebra-and-geometry-in-data-science-2025/
LOCATION:UC San Diego\, La Jolla\, CA\, 92093\, United States
CATEGORIES:TILOS Sponsored Event,Workshop
ATTACH;FMTTYPE=image/png:https://tilos.ai/wp-content/uploads/2025/09/TAG-DS_logo-1-e1756938002600.png
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20251206
DTEND;VALUE=DATE:20251207
DTSTAMP:20260521T133313
CREATED:20250903T220623Z
LAST-MODIFIED:20250908T161741Z
UID:7469-1764979200-1765065599@tilos.ai
SUMMARY:NeurIPS 2025 Workshop on Differentiable Learning of Combinatorial Algorithms
DESCRIPTION:Combinatorial algorithms are fundamental across a wide range of domains\, owing to their ability to model optimization and decision-making tasks under complex constraints. These algorithms underpin practical applications such as vehicle routing\, network and chip design\, clustering and information retrieval. Combinatorial problems are also prominent in various areas of machine learning such as natural language processing and robotics. Recent research has focused on leveraging neural networks to design novel combinatorial algorithms and to come up with techniques that allow seamless integration of classic combinatorial algorithms in differentiable neural network architectures. Developments in this field\, commonly referred to as neural combinatorial optimization\, have raised several fundamental questions that span both theory and practice. \nIn this workshop\, we take a broad perspective on designing differentiable algorithms for combinatorial optimization. The goal of the workshop is to explore novel ideas in the design and applications of neural combinatorial optimization\, as well as to improve our theoretical understanding of existing methods.
URL:https://tilos.ai/event/neurips-2025-workshop-on-differentiable-learning-of-combinatorial-algorithms-diffcoalg/
LOCATION:San Diego Convention Center\, San Diego\, CA\, United States
CATEGORIES:Workshop
ATTACH;FMTTYPE=image/png:https://tilos.ai/wp-content/uploads/2025/09/NeurIPS-logo-square-e1756938657121.png
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20251206
DTEND;VALUE=DATE:20251207
DTSTAMP:20260521T133313
CREATED:20250908T160038Z
LAST-MODIFIED:20250908T161418Z
UID:7552-1764979200-1765065599@tilos.ai
SUMMARY:NeurIPS 2025 Workshop on Optimization for Machine Learning
DESCRIPTION:Optimization lies at the heart of many machine learning algorithms and enjoys great interest in our community. Indeed\, this intimate relation of optimization with ML is the key motivation for the OPT series of workshops. We aim to foster discussion\, discovery\, and dissemination of state-of-the-art research in optimization relevant to ML. \nThe focus of OPT 2025 is on “Statistics Meets Optimization”. Since its inception\, stochastic optimization has been grounded in statistical principles. Today\, many of the most pressing challenges in machine learning—such as generalization bounds\, the training dynamics of overparameterized models\, and the development of generative models—are directly inspired by statistical thinking. At the same time\, the scale and complexity of modern datasets\, along with the increasingly rich model classes used to represent them\, pose new questions about how optimization algorithms interact with these structures—both computationally and statistically. For example\, what role do data symmetries play in shaping optimization trajectories? How do statistical properties of the data affect the adaptivity and efficiency of learning algorithms? And how can optimization approaches be designed to scale with data while still preserving desirable statistical behavior? OPT 2025 will explore these questions with the goal of building bridges between the statistics and optimization communities\, and highlighting their shared impact on the theory and practice of machine learning.
URL:https://tilos.ai/event/neurips-workshop-on-optimization-for-machine-learning/
LOCATION:San Diego Convention Center\, San Diego\, CA\, United States
CATEGORIES:Workshop
ATTACH;FMTTYPE=image/png:https://tilos.ai/wp-content/uploads/2025/09/NeurIPS-logo-square-e1756938657121.png
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BEGIN:VEVENT
DTSTART;VALUE=DATE:20251206
DTEND;VALUE=DATE:20251207
DTSTAMP:20260521T133313
CREATED:20250908T161144Z
LAST-MODIFIED:20251031T180834Z
UID:7557-1764979200-1765065599@tilos.ai
SUMMARY:NeurIPS 2025 Workshop on Imageomics: Discovering Biological Knowledge from Images Using AI
DESCRIPTION:Imageomics is an emerging interdisciplinary field at the crossroads of machine learning (ML)\, computer vision (CV)\, and biological sciences. It leverages visual data—from microscopic images of single-cell species to videos of megafauna—to extract and analyze biological information\, specifically traits. By grounding ML models in existing scientific knowledge\, Imageomics aims to make traits computable from images\, facilitating insights into the evolution and function of living organisms. Imageomics poses research problems that resonate with the broad machine-learning community: multimodal representation learning\, object detection and tracking\, few-shot learning\, imbalanced-class learning\, video understanding\, 3D modeling\, hierarchical learning\, etc. When people leverage ML tools to solve biological questions\, the foundational bridges between ML and biological sciences also provide opportunities to address key challenges in ML\, creating a virtuous cycle between the two fields. \nWe welcome participation from anyone interested in learning about the field of Imageomics\, including: \n\nBiological Scientists who are interested in applying ML and CV to their research\, or who want to learn how to use ML tools to analyze biological images.\nMachine Learning Researchers who are interested in applying their expertise to biological image data\, or who want to learn about the unique challenges and opportunities in this domain.\n\nThe workshop will feature keynote talks\, paper presentations\, and discussions on the latest research in Imageomics. We encourage participants to submit papers and demos related to the topics outlined in the Call For Papers. The workshop will also provide opportunities for networking and collaboration among researchers from diverse backgrounds.
URL:https://tilos.ai/event/neurips-workhop-on-imageomics-discovering-biological-knowledge-from-images-using-ai/
LOCATION:San Diego Convention Center\, San Diego\, CA\, United States
CATEGORIES:Workshop
ATTACH;FMTTYPE=image/png:https://tilos.ai/wp-content/uploads/2025/09/NeurIPS-logo-square-e1756938657121.png
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20251207
DTEND;VALUE=DATE:20251208
DTSTAMP:20260521T133313
CREATED:20250903T215535Z
LAST-MODIFIED:20250908T161649Z
UID:7462-1765065600-1765151999@tilos.ai
SUMMARY:NeurIPS 2025 Workshop on New Perspectives in Advancing Graph Machine Learning
DESCRIPTION:Graphs serve as a powerful representational framework for machine learning\, and their integration has substantially advanced the field. Indeed\, extensive studies have pushed forward graph machine learning (GML) in both theory and applications. Recently\, new perspectives have been emerging in the machine learning community\, including algebraic–topological analyses\, foundation models\, generative models\, and large models in applications. Leveraging these ideas for core graph machine learning holds a lot of promise\, including the dual benefit of deeper theoretical insight\, new capabilities and more powerful\, application-aligned algorithms and models. The aim of this workshop is to explore and connect these new perspectives on GML\, and to identify overarching challenges and tools – in terms of theory\, methodology\, and modeling.
URL:https://tilos.ai/event/neurips-2025-workshop-new-perspectives-in-advancing-graph-machine-learning/
LOCATION:San Diego Convention Center\, San Diego\, CA\, United States
CATEGORIES:Workshop
ATTACH;FMTTYPE=image/png:https://tilos.ai/wp-content/uploads/2025/09/NeurIPS-logo-square-e1756938657121.png
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