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Explore a comprehensive lecture on the generalization properties of score-based losses in machine learning. Delve into the comparison between score-based losses and maximum likelihood for fitting probabilistic generative models with intractable likelihoods. Examine the trade-offs between computational tractability and statistical efficiency. Investigate the emerging connection between statistical efficiency of generalized score losses and the algorithmic efficiency of diffusion-based sampling algorithms. Learn about the design space for score losses with favorable statistical behavior, drawing parallels to techniques for accelerating Markov chain convergence. Discover the analogous story for learning discrete probability distributions using masked prediction-like losses. Gain insights into the current state and future prospects of generative model theory, covering both short-term and long-term perspectives.
