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An Online Learning Approach to Interpolation and Extrapolation in Domain Generalization.

, , and . AISTATS, volume 151 of Proceedings of Machine Learning Research, page 2641-2657. PMLR, (2022)

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Universal Approximation Using Well-Conditioned Normalizing Flows., , , and . NeurIPS, page 12700-12711. (2021)Simulated Tempering Langevin Monte Carlo II: An Improved Proof using Soft Markov Chain Decomposition, , and . (2018)cite arxiv:1812.00793Comment: 69 pages. arXiv admin note: text overlap with arXiv:1710.02736.Provable benefits of annealing for estimating normalizing constants: Importance Sampling, Noise-Contrastive Estimation, and beyond., , and . CoRR, (2023)Extending and Improving Wordnet via Unsupervised Word Embeddings., , , and . CoRR, (2017)Representational aspects of depth and conditioning in normalizing flows., , and . ICML, volume 139 of Proceedings of Machine Learning Research, page 5628-5636. PMLR, (2021)Neural Network Approximations of PDEs Beyond Linearity: A Representational Perspective., , , and . ICML, volume 202 of Proceedings of Machine Learning Research, page 24139-24172. PMLR, (2023)Masked Prediction: A Parameter Identifiability View., , , and . NeurIPS, (2022)How Do Transformers Learn Topic Structure: Towards a Mechanistic Understanding., , and . ICML, volume 202 of Proceedings of Machine Learning Research, page 19689-19729. PMLR, (2023)Label optimal regret bounds for online local learning., , , and . CoRR, (2015)The Comparative Power of ReLU Networks and Polynomial Kernels in the Presence of Sparse Latent Structure., and . ICLR (Poster), OpenReview.net, (2019)