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  • Optimization for AI and ML Seminar: Training Neural Networks at Any Scale

    HDSI 123 and Virtual 3234 Matthews Ln, La Jolla, CA, United States

    Volkan Cevher, École Polytechnique Fédérale de Lausanne Abstract: At the heart of deep learning’s transformative impact lies the concept of scale--encompassing both data and computational resources, as well as their interaction with neural network architectures. Scale, however, presents critical challenges, such as increased instability during training and prohibitively expensive model-specific tuning. Given the substantial resources […]

  • Networking Lunch Reception at NeurIPS 2025

    Mezé Greek Fusion San Diego, CA, United States

    TILOS will host a networking lunch reception during NeurIPS 2025 at Mezé Greek Fusion from 12:00-2:00pm on Thursday, December 4, 2025. This event is open to all NeurIPS attendees affiliated with any of the NSF AI Research Institutes, as well as invited industry partners. Join us to connect with colleagues across the network of NSF […]

  • Optimization for ML and AI Seminar: Stochastic-Gradient and Diagonal-Scaling Algorithms for Constrained Optimization and Learning

    HDSI 123 and Virtual 3234 Matthews Ln, La Jolla, CA, United States

    Frank E. Curtis, Lehigh University Abstract: I will motivate and provide an overview of recent efforts in my research group on the design and analysis of stochastic-gradient-based algorithms for solving constrained optimization problems. I will focus in particular on our motivation for informed supervised learning, where constraints in the training problem can be used to […]

  • NeurIPS 2025 Workshop on Differentiable Learning of Combinatorial Algorithms

    San Diego Convention Center San Diego, CA, United States

    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 […]

  • NeurIPS 2025 Workshop on Optimization for Machine Learning

    San Diego Convention Center San Diego, CA, United States

    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. The focus of OPT […]

  • NeurIPS 2025 Workshop on Imageomics: Discovering Biological Knowledge from Images Using AI

    San Diego Convention Center San Diego, CA, United States

    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, […]

  • NeurIPS 2025 Workshop on New Perspectives in Advancing Graph Machine Learning

    San Diego Convention Center San Diego, CA, United States

    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 […]

  • TILOS-HDSI Seminar: Incentivizing Emergent Behaviors for LLMs via Reinforcement Learning

    Qualcomm Conference Center Room B (Jacobs Hall first floor) and Virtual 9736 Engineers Ln, La Jolla, CA, United States

    Yi Wu, Tsinghua University Abstract: Reinforcement Learning (RL) has become a powerful post-training method for eliciting advanced behaviors in large language models (LLMs). This talk presents recent results showing how RL can incentivize the emergence of LLM capabilities across three domains: (1) multi-player deduction game, Werewolf, where RL-trained LLM agents develop strategic behaviors and outperform […]

  • Optimization for ML and AI Seminar with Joel Tropp (Caltech)

    HDSI 123 and Virtual 3234 Matthews Ln, La Jolla, CA, United States

    Title and abstract TBA... Joel A. Tropp is Steele Family Professor of Applied & Computational Mathematics at the California Institute of Technology. His research centers on applied mathematics, machine learning, data science, numerical algorithms, and random matrix theory. Some of his best-known contributions include matching pursuit algorithms, randomized SVD algorithms, matrix concentration inequalities, and statistical phase transitions. Prof. Tropp attained the […]

  • TILOS-HDSI Seminar with Koushil Sreenath (UC Berkeley)

    HDSI 123 and Virtual 3234 Matthews Ln, La Jolla, CA, United States

    Title and abstract TBA... Koushil Sreenath is an Assistant Professor of Mechanical Engineering, at UC Berkeley. He received a Ph.D. degree in Electrical Engineering and Computer Science and a M.S. degree in Applied Mathematics from the University of Michigan at Ann Arbor, MI, in 2011. He was a Postdoctoral Scholar at the GRASP Lab at University […]