Statistical Learning
Breaking the Activation Function Bottleneck through Adaptive Parameterization
Sebastian Flennerhag, Hujun Yin, John Keane, Mark Elliot
Adaptive parameterization is a means of increasing this flexibility and thereby increasing the model's capacity to learn non-linear patterns.
Diminishing Returns Shape Constraints for Interpretability and Regularization
Maya Gupta, Dara Bahri, Andrew Cotter, Kevin Canini
We demonstrate on real-world examples that these shape constrained models can provide tuning-free regularization and improve model understandability.
Geometry Based Data Generation
Ofir Lindenbaum, Jay Stanley, Guy Wolf, Smita Krishnaswamy
Geometry-based data generation with SUGAR is motivated by numerous uses in data exploration.
Beyond Log-concavity: Provable Guarantees for Sampling Multi-modal Distributions using Simulated Tempering Langevin Monte Carlo
Holden Lee, Andrej Risteski, Rong Ge
A key task in Bayesian machine learning is sampling from distributions that are only specified up to a partition function (i.e., constant of proportionality).
Estimators for Multivariate Information Measures in General Probability Spaces
Arman Rahimzamani, Himanshu Asnani, Pramod Viswanath, Sreeram Kannan
A key quantity of interest is the mutual information and generalizations thereof, including conditional mutual information, multivariate mutual information, total correlation and directed information.
Sublinear Time Low-Rank Approximation of Distance Matrices
Neural Information Processing Systems http://nips.cc/
Sample Efficient Stochastic Gradient Iterative Hard Thresholding Method for Stochastic Sparse Linear Regression with Limited Attribute Observation
In real-world sequential prediction scenarios, the features (or attributes) of examples are typically high-dimensional and construction of the all features for each example may be expensive or impossible.
Wavelet regression and additive models for irregularly spaced data
Asad Haris, Ali Shojaie, Noah Simon
Neural Information Processing Systems http://nips.cc/
Fairness Behind a Veil of Ignorance: A Welfare Analysis for Automated Decision Making
Hoda Heidari, Claudio Ferrari, Krishna Gummadi, Andreas Krause
We draw attention to an important, yet largely overlooked aspect of evaluating fairness for automated decision making systems--namely risk and welfare considerations.
Binary Classification from Positive-Confidence Data
Takashi Ishida, Gang Niu, Masashi Sugiyama
Our work is related to one-class classification which is aimed at "describing" the positive class by clustering-related methods, but one-class classification does not have the ability to tune hyper-parameters