AI/ML Seminar Series: Ruiqi Gao (2/14/2022)

UCI AI/ML Seminar Series Ruiqi Gao Research Scientist Google Brain Advanced training of energy-based models Energy-based models (EBMs) are an appealing class of probabilistic models, which can be viewed as generative versions of discriminators, yet can be learned from unlabeled data. Despite a number of desirable properties, two challenges remain for training EBMs on high-dimensional datasets. First, learning EBMs by maximum likelihood requires Markov Chain Monte Carlo (MCMC) to generate samples from the model, which can be extremely expensive. Second, the energy potentials learned with non-convergent MCMC can be highly biased, making it difficult to evaluate the learned energy potentials or apply the learned models to downstream tasks. In this talk, I will present two algorithms to tackle the challenges of training EBMs. (1) Diffusion Recovery Likelihood, where we tractably learn and sample from a sequence of EBMs trained on increasingly noisy ve