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Found 11 papers, 4 papers with code
Massively Scalable Inverse Reinforcement Learning in Google Maps
Inverse reinforcement learning (IRL) offers a powerful and general framework for learning humans' latent preferences in route recommendation, yet no approach has successfully addressed planetary-scale problems with hundreds of millions of states and demonstration trajectories.
Neural Nonnegative Matrix Factorization for Hierarchical Multilayer Topic Modeling
We introduce a new method based on nonnegative matrix factorization, Neural NMF, for detecting latent hierarchical structure in data.
Inference of Media Bias and Content Quality Using Natural-Language Processing
We then infer a ``media-bias chart'' of (bias, quality) coordinates for the media outlets by integrating the (bias, quality) measurements of the tweets of the media outlets.
Analysis of Legal Documents via Non-negative Matrix Factorization Methods
The California Innocence Project (CIP), a clinical law school program aiming to free wrongfully convicted prisoners, evaluates thousands of mails containing new requests for assistance and corresponding case files.
Sparseness-constrained Nonnegative Tensor Factorization for Detecting Topics at Different Time Scales
Further, we propose quantitative ways to measure the topic length and demonstrate the ability of S-NCPD (as well as its online variant) to discover short and long-lasting temporal topics in a controlled manner in semi-synthetic and real-world data including news headlines.
Adaptive Sketch-and-Project Methods for Solving Linear Systems
We present new adaptive sampling rules for the sketch-and-project method for solving linear systems.
Numerical Analysis Numerical Analysis 15A06, 15B52, 65F10, 68W20, 65N75, 65Y20, 68Q25, 68W40, 90C20
Bias of Homotopic Gradient Descent for the Hinge Loss
Gradient descent is a simple and widely used optimization method for machine learning.
An iterative method for classification of binary data
Building on a recently designed simple framework for classification using binary data, we demonstrate that one can improve classification accuracy of this approach through iterative applications whose output serves as input to the next application.
Hierarchical Classification using Binary Data
In classification problems, especially those that categorize data into a large number of classes, the classes often naturally follow a hierarchical structure.
Model Agnostic Supervised Local Explanations
Some of the most common forms of interpretability systems are example-based, local, and global explanations.
Matrix Completion for Structured Observations
We propose adjusting the standard nuclear norm minimization strategy for matrix completion to account for such structural differences between observed and unobserved entries by regularizing the values of the unobserved entries.
