23 citations · 91 across the 21 of their papers we have counts for
8 papers · 1 filter
Supervised Auto-Encoding Twin-Bottleneck Hashing
Yuan Chen, Stéphane Marchand-Maillet
Deep hashing has shown to be a complexity-efficient solution for the Approximate Nearest Neighbor search problem in high dimensional space. Many methods usually build the loss func…
Cold Start Active Learning Strategies in the Context of Imbalanced Classification
Etienne Brangbour, Pierrick Bruneau, Thomas Tamisier +1
We present novel active learning strategies dedicated to providing a solution to the cold start stage, i.e. initializing the classification of a large set of data with no attached…
Learning by stochastic serializations
Pablo Strasser, Stephane Armand, Stephane Marchand-Maillet +1
Complex structures are typical in machine learning. Tailoring learning algorithms for every structure requires an effort that may be saved by defining a generic learning procedure…
Large-scale Nonlinear Variable Selection via Kernel Random Features
Magda Gregorová, Jason Ramapuram, Alexandros Kalousis +1
We propose a new method for input variable selection in nonlinear regression. The method is embedded into a kernel regression machine that can model general nonlinear functions, no…
Forecasting and Granger Modelling with Non-linear Dynamical Dependencies
Magda Gregorová, Alexandros Kalousis, Stéphane Marchand-Maillet
Traditional linear methods for forecasting multivariate time series are not able to satisfactorily model the non-linear dependencies that may exist in non-Gaussian series. We build…
On Hölder projective divergences
Frank Nielsen, Ke Sun, Stéphane Marchand-Maillet
We describe a framework to build distances by measuring the tightness of inequalities, and introduce the notion of proper statistical divergences and improper pseudo-divergences. W…
Learning Leading Indicators for Time Series Predictions
Magda Gregorova, Alexandros Kalousis, Stéphane Marchand-Maillet
We consider the problem of learning models for forecasting multiple time-series systems together with discovering the leading indicators that serve as good predictors for the syste…
Two-Stage Metric Learning
Jun Wang, Ke Sun, Fei Sha +2
In this paper, we present a novel two-stage metric learning algorithm. We first map each learning instance to a probability distribution by computing its similarities to a set of f…