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Explore efficient decentralized algorithms for multiagent reinforcement learning, focusing on V-learning's ability to overcome challenges in joint action spaces and achieve Nash equilibria.
Explore the macroscopic arrow of time, causal networks, and their relation to fundamental physics. Delve into entropy, complexity, and the nature of causality in our universe.
Explore theoretical and empirical evidence for exponential quantum advantage in ground-state energy determination for chemical problems, examining state preparation methods and numerical simulations.
Explore stability in repeated games, focusing on traffic routing and online auctions. Learn about no-regret learning, its limitations, and the impact of strategic user behavior on game outcomes.
Explore the foundations of causal reasoning and its impact on AI, decision-making, and scientific understanding with UCLA's Judea Pearl, a Turing Award winner and pioneer in probabilistic reasoning.
Explore statistical challenges in learning from biased data, focusing on self-selection issues in various fields and discussing recent progress in econometric modeling and auction theory.
Explore kernel ridge regression for causal inference, covering dose responses, treatment effects, and extensions to various settings. Learn about consistency, efficiency, and robustness in estimation.
Explore neural networks' vulnerability to spurious correlations and strategies to enhance robustness, including reweighting, subsampling, and overcoming simplicity bias in machine learning models.
Explore causal inference and autoencoders in drug repurposing for COVID-19, covering observational data, causality hierarchy, and predicting interventions with Caroline Uhler from MIT.
Explore machine learning-based protein design with Jennifer Listgarten, covering directed evolution, predictive models, and optimization techniques for gene therapy applications.
Explore advanced concepts in multi-agent reinforcement learning, including equilibrium, rewards, and techniques like Q-value and no-regret learning. Gain insights into challenges and applications in AI and game theory.
Explore multi-agent reinforcement learning, covering game theory, interaction protocols, and Nash equilibrium. Gain insights into challenges and formulations in this emerging field.
Explore reinforcement learning fundamentals, including credit assignment, dynamic programming, and exploration algorithms. Gain insights into algorithm design and sample complexity in this comprehensive introduction.
Explore advanced min-max optimization concepts, applications, and theoretical foundations with MIT expert Constantinos Daskalakis in this comprehensive lecture.
Explore advanced min-max optimization techniques, including convergence analysis, implicit methods, and applications to multiplayer games and polynomial-type problems.
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