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Time-Varying Topic Models Using Dependent Dirichlet Processes

, and . 2005-003. Department of Computer Science, University of Toronto, (2005)

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Learnability, Stability and Uniform Convergence, , , and . Journal of Machine Learning Research, (2010)Stochastic Nonconvex Optimization with Large Minibatches., and . ALT, volume 98 of Proceedings of Machine Learning Research, page 856-881. PMLR, (2019)A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks., , and . ICLR (Poster), OpenReview.net, (2018)Lower Bounds for Non-Convex Stochastic Optimization., , , , , and . CoRR, (2019)Uniform Convergence of Interpolators: Gaussian Width, Norm Bounds and Benign Overfitting., , , and . NeurIPS, page 20657-20668. (2021)A Stochastic Newton Algorithm for Distributed Convex Optimization., , , , and . NeurIPS, page 26818-26830. (2021)Most Neural Networks Are Almost Learnable., , and . CoRR, (2023)Does Invariant Risk Minimization Capture Invariance?, , , and . AISTATS, volume 130 of Proceedings of Machine Learning Research, page 4069-4077. PMLR, (2021)The Limits and Potentials of Local SGD for Distributed Heterogeneous Learning with Intermittent Communication., , , , , , , and . CoRR, (2024)Lexicographic and Depth-Sensitive Margins in Homogeneous and Non-Homogeneous Deep Models., , , , and . ICML, volume 97 of Proceedings of Machine Learning Research, page 4683-4692. PMLR, (2019)