Comparison and Transfer Between Tasks in Overparameterized Learning
Explore similarities and differences in classification and regression tasks for overparameterized linear models. Gain insights into task transfer and generalization in high-dimensional regimes.
Surprising Phenomena of Max-LP-Margin Classifiers in High Dimensions
Delve into surprising phenomena of max-lp-margin classifiers in high dimensions, exploring optimal rates, adaptivity, and performance in noisy observations for machine learning theory enthusiasts.
Regularization via Permutation of Labels - Lecture 2
Explore advanced statistical techniques for regularization through label permutation, focusing on mathematical theory and practical applications.
Generalisation of Some Overparametrised Models
Explorez la généralisation des modèles surparamétrés avec Stéphane Chrétien. Plongez dans les concepts mathématiques avancés et leurs applications dans l'apprentissage et l'optimisation.
Statistical Methodology and Theory - Professor Richard Samworth, Cambridge
Explore high-dimensional and nonparametric statistical inference with Prof. Samworth. Learn techniques for handling Big Data challenges, including random projections and subsampling for variable selection.
Auto-regressive Approximations to Non-stationary Time Series: Inference and Applications
Explore auto-regressive approximations for non-stationary time series, covering inference methods and practical applications in forecasting, estimation, and resampling.
Beating the Odds - Learning or Hallucinating? The Science of Data Today
Explore the intersection of data science, learning, and hallucination with insights from a renowned mathematician on current trends and challenges in the field.
Robust Estimation and Generative Adversarial Nets
Explore robust estimation techniques and their applications in high-dimensional statistics, including Huber's model, multivariate location depth, and f-Learning, with insights on computational challenges and theoretical foundations.
Invariance, Causality and Novel Robustness
Explore invariance, causality, and robustness in high-dimensional statistics. Learn about modern applications, heterogeneous data, and novel approaches to prediction problems using causal inference techniques.
The Kikuchi Hierarchy and Tensor PCA
Explores advanced algorithms for tensor PCA, introducing a new hierarchy inspired by Kikuchi free energy to improve belief propagation performance and match sum-of-squares methods.
Efficient Algorithms for Outlier-Robust Regression
Explore advanced techniques for outlier-robust regression, focusing on efficient algorithms and their applications in high-dimensional statistics and machine learning.
Mean and Location Estimation with Optimal Constants - UW Madison
Explore advanced statistical techniques for mean and location estimation, focusing on finite sample theories and optimal constants in high-dimensional regimes.
From Quantum Entanglement to Future Data-Driven Engineering - Rothschild Lecture
Explore high-dimensional Bayesian statistics in engineering, focusing on efficient data-physical model integration and uncertainty quantification. Learn about multilevel MCMC and measure-transport approaches.
The Debiased Lasso - Lecture 3
Explore sparsity in high-dimensional statistics, focusing on confidence intervals and the Debiased Lasso method for parameter estimation and asymptotic normality.
Adaptive Bayesian Predictive Inference
Explore adaptive Bayesian predictive inference for high-dimensional sparse mean estimation, examining sparsity priors and their performance in predictive density estimation under Kullback-Leibler loss.
Combinatorial Testing Problems - Lecture 1
Explore combinatorial testing problems, including hidden clique and high-dimensional random geometric graphs, with insights on statistical challenges and estimation techniques.
High-dimensional Statistics and Approximate Message Passing Algorithms - Course 3
Explore high-dimensional statistics and approximate message passing algorithms in this advanced mathematics lecture. Gain insights into cutting-edge research in discrete probability and statistics.
High-dimensional Statistics and Approximate Message Passing Algorithms - Workshop on Randomness and Learning on Networks
Explore high-dimensional statistics and approximate message passing algorithms with expert Cynthia Rush. Gain insights into advanced mathematical concepts and their applications in network analysis and learning.
High-dimensional Statistics and Approximate Message Passing Algorithms - Course 3
Explore high-dimensional statistics and approximate message passing algorithms with Cynthia Rush. Gain insights into advanced mathematical concepts and their applications in probability and statistics.
High-dimensional Statistics and Approximate Message Passing Algorithms - Course 3
Explore high-dimensional statistics and approximate message passing algorithms in this advanced mathematics lecture. Gain insights into cutting-edge research in discrete probability and statistics.
Can Non-Convex Optimization Be Robust?
Explore non-convex optimization's robustness, examining locally optimizable functions, perturbed objectives, and matrix completion. Insights on empirical vs. population risk and semi-random adversaries.