A Tree-Search Heuristic for Stochastic Production-Distribution Planning with Transportation Mode-Dependent Lead Times
Explore a tree-search heuristic for stochastic production-distribution planning, optimizing costs and lead times across transportation modes. Gain insights into efficient problem-solving techniques for complex supply chain scenarios.
Enhancing General-Purpose Simulation-Based Optimization Algorithms Via Mixed Integer Linear Programming: A Case Study in Autonomous Ridesharing
Enhance simulation-based optimization for complex stochastic problems using mixed integer linear programming, illustrated through an autonomous ridesharing case study.
Learning and Stochastic Optimization for Industrial Mining Complexes
Explorez une approche novatrice intégrant l'apprentissage automatique et l'optimisation pour la prise de décision dans les complexes miniers industriels face aux incertitudes d'approvisionnement et de demande.
Differentially Private Online-to-Batch Conversion for Stochastic Optimization
Explore differentially private stochastic optimization techniques, comparing simple preprocessing and custom algorithms. Learn about a novel online-to-batch conversion method for optimal private convergence rates.
Chance Constrained Simultaneous Task Allocation and Path Planning for Multi-Robot Systems
Explore multi-robot task allocation and path planning with probabilistic performance guarantees. Learn algorithmic approaches for optimizing team performance in open environments.
Distributed Stochastic Non-Convex Optimization - Optimal Regimes and Tradeoffs
Explore optimal regimes and tradeoffs in distributed stochastic non-convex optimization with Usman A. Khan. Gain insights into advanced data science concepts and their applications in signal processing.
Almost Optimal Variance-Constrained Best Arm Identification - Lecture 2
Explores advanced techniques in variance-constrained best arm identification, focusing on optimal algorithms and theoretical analysis for efficient decision-making in multi-armed bandit problems.
Particle Methods for Optimization Over Measures - Lecture 2
Explore particle methods for measure optimization in this advanced lecture, covering theoretical foundations and practical applications in data science and machine learning.
Particle Methods for Optimization over Measures - Lecture 1
Explore particle methods for measure optimization, focusing on theoretical foundations and practical applications in data science and machine learning.
Dynamic Fleet Size and Order Assignment Problem - Coffee Talk
Explore dynamic fleet size and order assignment in logistics, balancing rental costs and demand uncertainty through stochastic optimization and algorithmic decision-making.
Optimisation - An Introduction: Professor Coralia Cartis, University of Oxford
Explore optimization techniques, from basic concepts to advanced methods, covering minimizers, derivatives, quadratic functions, and gradient methods for solving complex mathematical problems.
Variational Bayesian Approximation of Inverse Problems Using Sparse Precision Matrices
Explore Variational Bayes as an efficient alternative to MCMC for solving inverse problems in PDEs, using sparse precision matrices and stochastic optimization for improved computational tractability.
Sample Size Estimates for Risk-Neutral Semilinear PDE-Constrained Optimization
Explore sample size estimates for risk-neutral semilinear PDE-constrained optimization using SAA approach, covering nonasymptotic analysis and numerical illustrations.
Monte Carlo Guided Diffusion for Bayesian Linear Inverse Problems
Explore Monte Carlo guided diffusion techniques for solving Bayesian linear inverse problems in this advanced mathematical presentation by Sylvain Le Corff.
Monte Carlo Methods
Explore Monte Carlo methods for probabilistic AI, covering key concepts and applications in this comprehensive tutorial from ProbAI 2024.
Jolteon - Unleashing the Promise of Serverless for Serverless Workflows
Explore Jolteon, an innovative orchestrator for serverless workflows that optimizes resource provisioning, enhancing cost-efficiency and performance through advanced modeling and optimization techniques.
Average Reward Markov Decision Process - Policy Gradient Algorithms and Regret Analysis
Delve into infinite horizon average reward Markov Decision Process (MDP), exploring policy gradient algorithms, gradient estimation techniques, and innovative approaches to reduce mixing time dependencies.
Adaptive Multi-armed Bandit Algorithms for Markovian and IID Rewards
Explore adaptive multi-armed bandit algorithms and their applications in both Markovian and i.i.d reward scenarios, focusing on regret guarantees and novel algorithmic approaches.
Probably Anytime-Safe Stochastic Combinatorial Semi-Bandits
Delve into stochastic combinatorial semi-bandits with a focus on risk-controlled online decision making, exploring algorithms for optimal subset selection while maintaining variance constraints.
Overview of Statistical Learning Theory - Part 2
Explore fundamental concepts in statistical learning theory, including capacity control, generalization, and optimization techniques. Gain insights into classic theory and modern applications.
Overview of Statistical Learning Theory - Part 1
Explore fundamental concepts of statistical learning theory, including capacity control, generalization, and optimization techniques. Gain insights into classic theories and their relevance to modern machine learning paradigms.