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Found 16 papers, 7 papers with code
Learning Discrete Directed Acyclic Graphs via Backpropagation
Recently continuous relaxations have been proposed in order to learn Directed Acyclic Graphs (DAGs) from data by backpropagation, instead of using combinatorial optimization.
On Margins and Generalisation for Voting Classifiers
We study the generalisation properties of majority voting on finite ensembles of classifiers, proving margin-based generalisation bounds via the PAC-Bayes theory.
Unsupervised Change Detection of Extreme Events Using ML On-Board
In this paper, we introduce RaVAEn, a lightweight, unsupervised approach for change detection in satellite data based on Variational Auto-Encoders (VAEs) with the specific purpose of on-board deployment.
Learning Stochastic Majority Votes by Minimizing a PAC-Bayes Generalization Bound
We investigate a stochastic counterpart of majority votes over finite ensembles of classifiers, and study its generalization properties.
RainBench: Towards Global Precipitation Forecasting from Satellite Imagery
Extreme precipitation events, such as violent rainfall and hail storms, routinely ravage economies and livelihoods around the developing world.
Learning Binary Decision Trees by Argmin Differentiation
We address the problem of learning binary decision trees that partition data for some downstream task.
Learning Binary Trees via Sparse Relaxation
In this work we derive a novel sparse relaxation for binary tree learning.
Cumulo: A Dataset for Learning Cloud Classes
One of the greatest sources of uncertainty in future climate projections comes from limitations in modelling clouds and in understanding how different cloud types interact with the climate system.
Learning Landmark-Based Ensembles with Random Fourier Features and Gradient Boosting
Unlike state-of-the-art Multiple Kernel Learning techniques that make use of a pre-computed dictionary of kernel functions to select from, at each iteration we fit a kernel by approximating it as a weighted sum of Random Fourier Features (RFF) and by optimizing their barycenter.
Fully Decentralized Joint Learning of Personalized Models and Collaboration Graphs
We consider the fully decentralized machine learning scenario where many users with personal datasets collaborate to learn models through local peer-to-peer exchanges, without a central coordinator.
Adversarial Robustness Toolbox v1.0.0
Defending Machine Learning models involves certifying and verifying model robustness and model hardening with approaches such as pre-processing inputs, augmenting training data with adversarial samples, and leveraging runtime detection methods to flag any inputs that might have been modified by an adversary.
Efficient Defenses Against Adversarial Attacks
Following the recent adoption of deep neural networks (DNN) accross a wide range of applications, adversarial attacks against these models have proven to be an indisputable threat.
L$^3$-SVMs: Landmarks-based Linear Local Support Vectors Machines
For their ability to capture non-linearities in the data and to scale to large training sets, local Support Vector Machines (SVMs) have received a special attention during the past decade.
beta-risk: a New Surrogate Risk for Learning from Weakly Labeled Data
During the past few years, the machine learning community has paid attention to developping new methods for learning from weakly labeled data.
Metric Learning as Convex Combinations of Local Models With Generalization Guarantees
Over the past ten years, metric learning allowed the improvement of the numerous machine learning approaches that manipulate distances or similarities.
Lipschitz Continuity of Mahalanobis Distances and Bilinear Forms
Many theoretical results in the machine learning domain stand only for functions that are Lipschitz continuous.
