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Explore cutting-edge research in causal representation learning, policy selection, independence criteria, Bayesian modeling, and more. Gain insights into advanced AI topics presented at UAI 2023.
Explore cutting-edge research in causal discovery, adversarial training, graph algorithms, generative models, probabilistic forecasting, temporal point processes, crowd wisdom, and more in this AI uncertainty session.
Explore the power of cooperation in agent networks for enhanced learning, focusing on performance bounds and online learning scenarios.
Explore cutting-edge research in causal inference, including bounding effects, model reduction, interventions in games, and causally abstracted bandits.
Explore geometric probabilistic models for data-efficient, uncertainty-aware decision-making in applications like drug design and robotics. Learn theory and implementation of Gaussian processes and related techniques.
Explore performative prediction in machine learning, examining its impact on data distribution, equilibrium challenges, and connections to statistics, game theory, and causality. Gain insights into steering and power dynamics in digital markets.
Explore advanced topics in probabilistic modeling and decision-making, including facility location, answer set programs, utility theory, and probabilistic circuits.
Explore cutting-edge research on cost-sensitive failure recognition, interpretable decision trees, and Bayesian active learning in AI. Gain insights into advanced learning algorithms and their applications.
Explore cutting-edge research in sequential learning, covering robust reinforcement learning, online learning in MDPs, fairness in predict-then-optimize settings, and constrained POMDPs.
Explore cutting-edge deep learning research, including Bayesian methods, generative modeling, visual concept programming, conformal regression, and heteroskedastic regression analysis.
Explore the concept of causal DAGs, their usefulness, and challenges in causal discovery. Gain insights into evaluating causal models without ground truth and understanding causality's meaning across various tasks.
Explore probabilistic circuits' robustness to out-of-distribution data and learn about tractable dropout inference for improved uncertainty quantification in a single forward pass.
Explore a novel approach combining probabilistic circuits with normalizing flows for enhanced expressivity while maintaining tractable inference in deep probabilistic models.
Explore efficient inference algorithms for Probabilistic Generating Circuits, addressing computational challenges and discussing implications for learning from data in probabilistic modeling.
Explore an alternative message passing algorithm for cyclic graphs, challenging SPA's extrinsic principle and optimizing factor node updates for improved performance on complex networks.
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