Sam Hopkins (Miller Fellow 2018-2021): Proofs, Algorithms, and High-Dimensional Statistics Published 2021-04-07 Download video MP4 360p Download video MP4 720p Recommendations 1:03:45 Nonparametric Bayesian Methods: Models, Algorithms, and Applications II 1:17:55 Optimization I 47:01 Bridging Associative Memory and Probabilistic Modeling 11:45 Confidence intervals and margin of error | AP Statistics | Khan Academy 1:08:25 The Mathematics of Lattices I 1:30:15 High-Dimensional Statistics I 3:58:31 PySpark Full Course [2024] | Learn PySpark | PySpark Tutorial | Edureka 3:49:55 🔥Google Cloud InDepth Tutorial | Google Cloud Platform Tutorial 2022 | Cloud Computing | Simplilearn 54:49 Algorithmic Trading and Machine Learning 1:06:01 Nonparametric Bayesian Methods: Models, Algorithms, and Applications I 1:00:56 Are LLMs the Beginning or End of NLP? 1:04:46 Ultraproducts as a Bridge Between Discrete and Continuous Analysis 1:29:49 Pairings in Cryptography 1:58:49 Dlaczego fizyka kwantowa działa? - dr Tomasz Miller - BS3S 1:05:29 Variational Inference: Foundations and Innovations 3:29:07 🔥 Scrum Master Bootcamp 2023 | Scrum Master Bootcamp 2023 | Simplilearn 56:25 The Unreasonable Effectiveness of Spectral Graph Theory: A Confluence of Algorithms, Geometry & ... 51:32 Predictive Coding Models of Perception 1:44:20 Tutorial on Category Theory: Part 1 – Pure and Classical 54:29 The Contextual Bandits Problem Similar videos 03:44 Sam Hopkins (MF 2018-2021): Computation in the face of Big Data 58:24 Sam Hopkins on Heavy Tails, or, How I Learned to Stop Worrying and Love the Median 1:08:26 Sam Hopkins - MIT - Sum of Squares Methods for Statistical Problems II 52:17 Samuel Hopkins (Berkeley) -- How to Estimate the Mean of a Random Vector in Polynomial Time 33:30 Information/Computation Gaps in Heavy-Tailed Statistics 1:00:46 Recent Advances in Algorithmic Heavy-Tailed Statistics 1:00:48 Robustly Learning Mixtures of (Clusterable) Gaussians via the SoS Proofs to Algorithms Method 1:01:45 Sum-Of-Squares Lower Bound for Statistical Problems 59:42 STATS 200C: High-dimensional Statistics -- Spring 2022 -- Lecture 1 1:15:49 STATS 200C: High-dimensional Statistics -- Lecture 10 29:16 [Wainwright High Dimensional Statistics] Lec 1 Introduction 44:55 Sub-Gaussian Mean Estimation in Polynomial Time 1:02:23 Pseudocalibration and SoS Lower Bounds 1:35:41 (second part) Estimation of latent factors for high dimensional time series--Biometrika 1:00:21 Prof. Mireille (Mimi) Boutin - Keynote - The Geometry of High-Dimensional Data 1:15:37 High dimensional mediation analyses 1:12:28 Pedagogical Talk: Frameworks for Information-Computation Tradeoffs More results