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Found 11 papers, 4 papers with code
Characterizing the robustness of Bayesian adaptive experimental designs to active learning bias
We show that active learning bias can also afflict Bayesian adaptive experimental design, depending on model misspecification.
Conditional Mutual Information Estimation for Mixed Discrete and Continuous Variables with Nearest Neighbors
One reason conditional mutual information is not more widely used for these tasks is the lack of estimators which can handle combinations of continuous and discrete random variables, common in applications.
Statistics Theory Methodology Statistics Theory
The LICORS Cabinet: Nonparametric Algorithms for Spatio-temporal Prediction
Spatio-temporal data is intrinsically high dimensional, so unsupervised modeling is only feasible if we can exploit structure in the process.
Predictive PAC Learning and Process Decompositions
We informally call a stochastic process learnable if it admits a generalization error approaching zero in probability for any concept class with finite VC-dimension (IID processes are the simplest example).
Nonparametric risk bounds for time-series forecasting
We derive generalization error bounds for traditional time-series forecasting models.
Mixed LICORS: A Nonparametric Algorithm for Predictive State Reconstruction
We introduce 'mixed LICORS', an algorithm for learning nonlinear, high-dimensional dynamics from spatio-temporal data, suitable for both prediction and simulation.
Model Selection for Degree-corrected Block Models
We present the first principled and tractable approach to model selection between standard and degree-corrected block models, based on new large-graph asymptotics for the distribution of log-likelihood ratios under the stochastic block model, finding substantial departures from classical results for sparse graphs.
Rademacher complexity of stationary sequences
We show how to control the generalization error of time series models wherein past values of the outcome are used to predict future values.
Power-law distributions in empirical data
Power-law distributions occur in many situations of scientific interest and have significant consequences for our understanding of natural and man-made phenomena.
Data Analysis, Statistics and Probability Disordered Systems and Neural Networks Applications Methodology
Blind Construction of Optimal Nonlinear Recursive Predictors for Discrete Sequences
We present a new method for nonlinear prediction of discrete random sequences under minimal structural assumptions.
Methods and Techniques of Complex Systems Science: An Overview
In this chapter, I review the main methods and techniques of complex systems science.
Adaptation and Self-Organizing Systems Statistical Mechanics Chaotic Dynamics Cellular Automata and Lattice Gases Data Analysis, Statistics and Probability Quantitative Methods
