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05/10/2023
Orders between channels and some implications for partial information decomposition
The partial information decomposition (PID) framework is concerned with ...
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03/29/2023
Fairness-Aware Data Valuation for Supervised Learning
Data valuation is a ML field that studies the value of training instance...
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03/14/2023
Distinguishing Cause from Effect on Categorical Data: The Uniform Channel Model
Distinguishing cause from effect using observations of a pair of random ...
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09/04/2022
ProBoost: a Boosting Method for Probabilistic Classifiers
ProBoost, a new boosting algorithm for probabilistic classifiers, is pro...
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07/13/2022
Understanding Unfairness in Fraud Detection through Model and Data Bias Interactions
In recent years, machine learning algorithms have become ubiquitous in a...
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06/27/2022
Human-AI Collaboration in Decision-Making: Beyond Learning to Defer
Human-AI collaboration (HAIC) in decision-making aims to create synergis...
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06/27/2022
Prisoners of Their Own Devices: How Models Induce Data Bias in Performative Prediction
The unparalleled ability of machine learning algorithms to learn pattern...
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03/04/2022
Differentiable Causal Discovery Under Latent Interventions
Recent work has shown promising results in causal discovery by leveragin...
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08/04/2021
Sparse Continuous Distributions and Fenchel-Young Losses
Exponential families are widely used in machine learning; they include m...
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06/20/2021
Distributed Picard Iteration: Application to Distributed EM and Distributed PCA
In recent work, we proposed a distributed Picard iteration (DPI) that al...
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03/31/2021
Distributed Picard Iteration
The Picard iteration is widely used to find fixed points of locally cont...
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11/30/2020
TimeSHAP: Explaining Recurrent Models through Sequence Perturbations
Recurrent neural networks are a standard building block in numerous mach...
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11/03/2020
Control with adaptive Q-learning
This paper evaluates adaptive Q-learning (AQL) and single-partition adap...
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09/01/2020
Variational Mixture of Normalizing Flows
In the past few years, deep generative models, such as generative advers...
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06/15/2020
Equilibrium Propagation for Complete Directed Neural Networks
Artificial neural networks, one of the most successful approaches to sup...
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06/12/2020
Sparse and Continuous Attention Mechanisms
Exponential families are widely used in machine learning; they include m...
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01/18/2020
A Classification-Based Approach to Semi-Supervised Clustering with Pairwise Constraints
In this paper, we introduce a neural network framework for semi-supervis...
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07/19/2018
Conditional Random Fields as Recurrent Neural Networks for 3D Medical Imaging Segmentation
The Conditional Random Field as a Recurrent Neural Network layer is a re...
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07/09/2018
Image Restoration Using Conditional Random Fields and Scale Mixtures of Gaussians
This paper proposes a general framework for internal patch-based image r...
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07/09/2018
External Patch-Based Image Restoration Using Importance Sampling
This paper introduces a new approach to patch-based image restoration ba...
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04/12/2018
Impulsive Noise Robust Sparse Recovery via Continuous Mixed Norm
This paper investigates the problem of sparse signal recovery in the pre...
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03/01/2018
Poisson Image Denoising Using Best Linear Prediction: A Post-processing Framework
In this paper, we address the problem of denoising images degraded by Po...
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01/02/2018
Scene-Adapted Plug-and-Play Algorithm with Guaranteed Convergence: Applications to Data Fusion in Imaging
The recently proposed plug-and-play (PnP) framework allows leveraging re...
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09/06/2017
Blind image deblurring using class-adapted image priors
Blind image deblurring (BID) is an ill-posed inverse problem, usually ad...
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06/21/2017
Class-specific image denoising using importance sampling
In this paper, we propose a new image denoising method, tailored to spec...
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06/09/2017
Class-specific Poisson denoising by patch-based importance sampling
In this paper, we address the problem of recovering images degraded by P...
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04/10/2017
Adaptive Relaxed ADMM: Convergence Theory and Practical Implementation
Many modern computer vision and machine learning applications rely on so...
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02/08/2017
Scene-adapted plug-and-play algorithm with convergence guarantees
Recent frameworks, such as the so-called plug-and-play, allow us to leve...
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05/23/2016
Image Restoration with Locally Selected Class-Adapted Models
State-of-the-art algorithms for imaging inverse problems (namely deblurr...
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02/12/2016
Image Restoration and Reconstruction using Variable Splitting and Class-adapted Image Priors
This paper proposes using a Gaussian mixture model as a prior, for solvi...
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09/15/2014
The Ordered Weighted ℓ_1 Norm: Atomic Formulation, Projections, and Algorithms
The ordered weighted ℓ_1 norm (OWL) was recently proposed, with two diff...
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02/20/2014
Group-sparse Matrix Recovery
We apply the OSCAR (octagonal selection and clustering algorithms for re...
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10/18/2013
A novel sparsity and clustering regularization
We propose a novel SPARsity and Clustering (SPARC) regularizer, which is...
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09/24/2013
Solving OSCAR regularization problems by proximal splitting algorithms
The OSCAR (octagonal selection and clustering algorithm for regression) ...
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12/28/2012
Alternating Directions Dual Decomposition
We propose AD3, a new algorithm for approximate maximum a posteriori (MA...
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10/09/2012
Deconvolving Images with Unknown Boundaries Using the Alternating Direction Method of Multipliers
The alternating direction method of multipliers (ADMM) has recently spar...
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10/13/2010
