
Online Selection of Diverse Committees
Citizens' assemblies need to represent subpopulations according to their...
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Online certification of preferencebased fairness for personalized recommender systems
We propose to assess the fairness of personalized recommender systems in...
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On the robustness of randomized classifiers to adversarial examples
This paper investigates the theory of robustness against adversarial att...
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Mixed Nash Equilibria in the Adversarial Examples Game
This paper tackles the problem of adversarial examples from a game theor...
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Detecting and adapting to crisis pattern with context based Deep Reinforcement Learning
Deep reinforcement learning (DRL) has reached super human levels in comp...
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Fast Accurate Method for Bounding the Singular Values of Convolutional Layers with Application to Lipschitz Regularization
This paper tackles the problem of Lipschitz regularization of Convolutio...
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Handling Multiple Costs in Optimal Transport: Strong Duality and Efficient Computation
We introduce an extension of the optimal transportation (OT) problem whe...
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Randomization matters. How to defend against strong adversarial attacks
Is there a classifier that ensures optimal robustness against all advers...
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Yet another but more efficient blackbox adversarial attack: tiling and evolution strategies
We introduce a new blackbox attack achieving state of the art performan...
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A unified view on differential privacy and robustness to adversarial examples
This short note highlights some links between two lines of research with...
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NGOGM: Natural Gradient Optimization for Graphical Models
This paper deals with estimating model parameters in graphical models. W...
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Robust Neural Networks using Randomized Adversarial Training
Since the discovery of adversarial examples in machine learning, researc...
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Theoretical evidence for adversarial robustness through randomization: the case of the Exponential family
This paper investigates the theory of robustness against adversarial att...
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On the Expressive Power of Deep Fully Circulant Neural Networks
In this paper, we study deep fully circulant neural networks, that is de...
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A discrete version of CMAES
Modern machine learning uses more and more advanced optimization techniq...
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Training compact deep learning models for video classification using circulant matrices
In real world scenarios, model accuracy is hardly the only factor to con...
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Graphbased Clustering under Differential Privacy
In this paper, we present the first differentially private clustering me...
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Explanatory relations in arbitrary logics based on satisfaction systems, cutting and retraction
The aim of this paper is to introduce a new framework for defining abduc...
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On the Needs for Rotations in Hypercubic Quantization Hashing
The aim of this paper is to endow the wellknown family of hypercubic qu...
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Multidimensional signal approximation with sparse structured priors using split Bregman iterations
This paper addresses the structurallyconstrained sparse decomposition o...
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TripleSpin  a generic compact paradigm for fast machine learning computations
We present a generic compact computational framework relying on structur...
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Relaxationbased revision operators in description logics
As ontologies and description logics (DLs) reach out to a broader audien...
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Multidimensional sparse structured signal approximation using split Bregman iterations
The paper focuses on the sparse approximation of signals using overcompl...
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Metrics for Multivariate Dictionaries
Overcomplete representations and dictionary learning algorithms kept att...
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Jamal Atif
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