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Explore model-based control for physical systems, covering inverse dynamics, optimization techniques, and automated discovery methods for advanced robotics and control applications.
Explore algorithmic foundations of learning and control in robotics, covering model-based approaches, skill dynamics, and real-world applications with Google Brain researcher Vikash Kumar.
Explore stable and efficient reinforcement learning algorithms with Csaba Szepesvári's presentation on Politex, focusing on generalization and pseudo regret in online learning and linear prediction.
Explore student projects showcasing innovative wireless network systems, from enhanced e-readers to collaborative tools and vision-based applications.
Explore deep learning concepts, including lazy regime, relative scale, and practical applications in this comprehensive lecture by Joan Bruna from NYU.
Explore advanced deep learning concepts including symmetry, transformations, and geometric stability in neural networks with NYU professor Joan Bruna.
Explore reinforcement learning fundamentals, applications, and challenges with Stanford's Emma Brunskill, covering AI planning, machine learning, and decision-making processes.
Explore supervised learning, empirical risk, and fundamental theorems in deep learning with NYU professor Joan Bruna's comprehensive lecture on neural network foundations.
Explore advanced optimization techniques with Sebastien Bubeck, covering telescopic sums, continuous time analysis, and variance reduction in convex optimization.
Explore advanced concepts in convex optimization, including gradient flow, comparison techniques, and optimal algorithms with Sebastien Bubeck from Microsoft Research.
Explore advanced concepts in convex optimization, including gradient oracles, minimax principles, and efficient algorithms for solving complex optimization problems.
Explore statistical learning with Robert Schapire, covering strong/weak learning, boosting, and confidence in machine learning algorithms.
Explore bandit algorithms and their applications in machine learning, from drug development to content recommendation, with a focus on regret minimization and stochastic models.
Explore statistical learning with Robert Schapire, covering expected value theorems, ghost samples, PAC learning, and finite hypothesis spaces in this comprehensive lecture.
Diverse theoretical computer science research: algorithms, data structures, complexity, combinatorics, and economics. Insights from UW Allen School's theory group on cutting-edge topics.
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