Events

16 Jun 2021 ML Seminar: Event Prediction in the Big Data Era: Background, Recent Works, and Challenges Dr. Liang Zhao from Emory University delivered an ML Seminar for us, covering his research paper: "Event Prediction in the Big Data Era: Background, Recent Works, and Challenges". More info 02 Jun 2021 ML Seminar: How exact can we make approximate Gaussian process inference? Mark van der Wilk from Imperial College delivered an ML Seminar for us, covering his research on "How exact can we make approximate Gaussian process inference?". 26 May 2021 ML Seminar: T5 and large language models: The good, the bad, and the ugly Colin Raffel from the University of North Carolina at Chapel Hill delivered an ML Seminar for us, covering his research papers on the subject "T5 and large language models: The good, the bad, and the ugly". More info 12 May 2021 ML Seminar: Theoretical Analysis of Self-Training with Deep Networks on Unlabeled Data (Colin Wei, Stanford University) Colin Wei from Stanford University delivered an ML Seminar for us, covering his research paper, "Theoretical Analysis of Self-Training with Deep Networks on Unlabeled Data". More info 28 Apr 2021 ML Seminar: Unsupervised Environment Design for Transfer in RL (Michael Dennis, UC Berkeley) Michael Dennis from UC Berkeley delivered an ML Seminar for us, covering his research paper, "Unsupervised Environment Design for Transfer in RL". More info 21 Apr 2021 ML Seminar: Variationally Regularized Graph-based Representation Learning for Electronic Health Records (Jack Zhu, NYU) Jack Zhu from NYU delivered an ML Seminar for us, covering his research paper, "Variationally Regularized Graph-based Representation Learning for Electronic Health Records". More info 07 Apr 2021 ML Seminar: Parametric UMAP: learning embeddings with deep neural networks for representation and semi-supervised learning (Tim Sainburg, UCSD) Tim Sainburg from UCSD delivered an ML Seminar for us, covering his research paper, "Parametric UMAP: learning embeddings with deep neural networks for representation and semi-supervised learning". More info 03 Mar 2021 ML Seminar: Falkon: Large Scale Kernel Methods on the GPU (Giacomo Meanti, DIBRIS, University of Genova) Giacomo Meanti from DIBRIS, University of Genova delivered an ML Seminar for us, covering his research paper, "Falkon: Large Scale Kernel Methods on the GPU". More info 10 Feb 2021 ML Seminar: When Do Neural Networks Outperform Kernel Methods? (Theodor Misiakiewicz, Stanford University) Theodor Misiakiewicz from Stanford University delivered an ML Seminar for us, covering his research paper, "When Do Neural Networks Outperform Kernel Methods?". More info 27 Jan 2021 ML Seminar: A broad view on the double descent phenomenon (Stéphane d’Ascoli, ENS, FAIR) Stéphane d’Ascoli from ENS, FAIR delivered an ML Seminar for us, covering his research paper, "A broad view on the double descent phenomenon". 20 Jan 2021 ML Seminar: A Closer Look at Accuracy vs. Robustness (Yao-Yuan Yang, UCSD) Yao-Yuan Yang from UCSD delivered an ML Seminar for us, covering his research paper, "A Closer Look at Accuracy vs. Robustness". More info 13 Jan 2021 ML Seminar: Self-training Avoids Using Spurious Features Under Domain Shift (Yining Chen, Stanford University) Yining Chen from Stanford University delivered an ML Seminar for us, covering her research paper, "Self-training Avoids Using Spurious Features Under Domain Shift". More info