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Found 14 papers, 2 papers with code
Sobolev Space Regularised Pre Density Models
We propose a new approach to non-parametric density estimation that is based on regularizing a Sobolev norm of the density.
Learning Hidden Markov Models When the Locations of Missing Observations are Unknown
However, standard HMM learning algorithms rely crucially on the assumption that the positions of the missing observations \emph{within the observation sequence} are known.
Two Regimes of Generalization for Non-Linear Metric Learning
Specifically, we discover and analyze two regimes of behavior of the networks, which are roughly related to the sparsity of the last layer.
Dimension Free Generalization Bounds for Non Linear Metric Learning
In this work we study generalization guarantees for the metric learning problem, where the metric is induced by a neural network type embedding of the data.
Finite Sample Analysis Of Dynamic Regression Parameter Learning
The proposed bounds depend on the shape of a certain spectrum related to the system operator, and thus provide the first known explicit geometric parameter of the data that can be used to bound estimation errors.
Topic Modeling via Full Dependence Mixtures
FDMs model second moment under general generative assumptions on the data.
Multi Instance Learning For Unbalanced Data
In the context of Multi Instance Learning, we analyze the Single Instance (SI) learning objective.
On-Line Learning of Linear Dynamical Systems: Exponential Forgetting in Kalman Filters
Based on this insight, we devise an on-line algorithm for improper learning of a linear dynamical system (LDS), which considers only a few most recent observations.
Interdependent Gibbs Samplers
Gibbs sampling, as a model learning method, is known to produce the most accurate results available in a variety of domains, and is a de facto standard in these domains.
A Reinforcement Learning System to Encourage Physical Activity in Diabetes Patients
Messages were personalized through a Reinforcement Learning (RL) algorithm which optimized messages to improve each participant's compliance with the activity regimen.
Clustering Time Series and the Surprising Robustness of HMMs
Suppose that we are given a time series where consecutive samples are believed to come from a probabilistic source, that the source changes from time to time and that the total number of sources is fixed.
Overlapping Community Detection by Online Cluster Aggregation
We present a new online algorithm for detecting overlapping communities.
Overlapping Communities Detection via Measure Space Embedding
We present a new algorithm for community detection.
