Search Results for author:
Found 41 papers, 23 papers with code
Bayesian Nonparametric Modeling of Suicide Attempts
In the present paper, we are interested in seeking the hidden causes behind the suicide attempts, for which we propose to model the subjects using a nonparametric latent model based on the Indian Buffet Process (IBP).
Bayesian nonparametric comorbidity analysis of psychiatric disorders
To this end, we use the large amount of information collected in the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) database and propose to model these data using a nonparametric latent model based on the Indian Buffet Process (IBP).
Shaping Social Activity by Incentivizing Users
Events in an online social network can be categorized roughly into endogenous events, where users just respond to the actions of their neighbors within the network, or exogenous events, where users take actions due to drives external to the network.
General Table Completion using a Bayesian Nonparametric Model
Even though heterogeneous databases can be found in a broad variety of applications, there exists a lack of tools for estimating missing data in such databases.
Fairness Constraints: Mechanisms for Fair Classification
Algorithmic decision making systems are ubiquitous across a wide variety of online as well as offline services.
Infinite Factorial Dynamical Model
We propose the infinite factorial dynamic model (iFDM), a general Bayesian nonparametric model for source separation.
Modeling the Dynamics of Online Learning Activity
People are increasingly relying on the Web and social media to find solutions to their problems in a wide range of domains.
Distilling Information Reliability and Source Trustworthiness from Digital Traces
Then, we propose a temporal point process modeling framework that links these temporal traces to robust, unbiased and interpretable notions of information reliability and source trustworthiness.
Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment
To account for and avoid such unfairness, in this paper, we introduce a new notion of unfairness, disparate mistreatment, which is defined in terms of misclassification rates.
Uncovering the Dynamics of Crowdlearning and the Value of Knowledge
In this paper, we present a probabilistic modeling framework of crowdlearning, which uncovers the evolution of a user's expertise over time by leveraging other users' assessments of her contributions.
General Latent Feature Models for Heterogeneous Datasets
Second, its Bayesian nonparametric nature allows us to automatically infer the model complexity from the data, i. e., the number of features necessary to capture the latent structure in the data.
From Parity to Preference-based Notions of Fairness in Classification
The adoption of automated, data-driven decision making in an ever expanding range of applications has raised concerns about its potential unfairness towards certain social groups.
General Latent Feature Modeling for Data Exploration Tasks
This paper introduces a general Bayesian non- parametric latent feature model suitable to per- form automatic exploratory analysis of heterogeneous datasets, where the attributes describing each object can be either discrete, continuous or mixed variables.
Automatic Discovery of the Statistical Types of Variables in a Dataset
A common practice in statistics and machine learning is to assume that the statistical data types (e. g., ordinal, categorical or real-valued) of variables, and usually also the likelihood model, is known.
Enhancing the Accuracy and Fairness of Human Decision Making
Societies often rely on human experts to take a wide variety of decisions affecting their members, from jail-or-release decisions taken by judges and stop-and-frisk decisions taken by police officers to accept-or-reject decisions taken by academics.
Boosting Black Box Variational Inference
Finally, we present a stopping criterion drawn from the duality gap in the classic FW analyses and exhaustive experiments to illustrate the usefulness of our theoretical and algorithmic contributions.
Handling Incomplete Heterogeneous Data using VAEs
Variational autoencoders (VAEs), as well as other generative models, have been shown to be efficient and accurate for capturing the latent structure of vast amounts of complex high-dimensional data.
Automatic Bayesian Density Analysis
Classical approaches for {exploratory data analysis} are usually not flexible enough to deal with the uncertainty inherent to real-world data: they are often restricted to fixed latent interaction models and homogeneous likelihoods; they are sensitive to missing, corrupt and anomalous data; moreover, their expressiveness generally comes at the price of intractable inference.
Infinite Factorial Finite State Machine for Blind Multiuser Channel Estimation
New communication standards need to deal with machine-to-machine communications, in which users may start or stop transmitting at any time in an asynchronous manner.
Fair Decisions Despite Imperfect Predictions
In this paper, we show that in this selective labels setting, learning a predictor directly only from available labeled data is suboptimal in terms of both fairness and utility.
Model-Agnostic Counterfactual Explanations for Consequential Decisions
Predictive models are being increasingly used to support consequential decision making at the individual level in contexts such as pretrial bail and loan approval.
Algorithmic Recourse: from Counterfactual Explanations to Interventions
As machine learning is increasingly used to inform consequential decision-making (e. g., pre-trial bail and loan approval), it becomes important to explain how the system arrived at its decision, and also suggest actions to achieve a favorable decision.
Lipschitz standardization for multivariate learning
While MTL solutions do not directly apply in the probabilistic setting (as they cannot handle the likelihood constraints) we show that similar ideas may be leveraged during data preprocessing.
Algorithmic recourse under imperfect causal knowledge: a probabilistic approach
Recent work has discussed the limitations of counterfactual explanations to recommend actions for algorithmic recourse, and argued for the need of taking causal relationships between features into consideration.
A survey of algorithmic recourse: definitions, formulations, solutions, and prospects
Machine learning is increasingly used to inform decision-making in sensitive situations where decisions have consequential effects on individuals' lives.
Scaling Guarantees for Nearest Counterfactual Explanations
Counterfactual explanations (CFE) are being widely used to explain algorithmic decisions, especially in consequential decision-making contexts (e. g., loan approval or pretrial bail).
On the Fairness of Causal Algorithmic Recourse
Algorithmic fairness is typically studied from the perspective of predictions.
Rotograd: Dynamic Gradient Homogenization for Multitask Learning
GradNorm eases the fitting of all individual tasks by dynamically equalizing the contribution of each task to the overall gradient magnitude.
A Ranking Approach to Fair Classification
In this paper, we focus on scenarios where only imperfect labels are available and propose a new fair ranking-based decision system based on monotonic relationships between legitimate features and the outcome.
RotoGrad: Gradient Homogenization in Multitask Learning
Multitask learning is being increasingly adopted in applications domains like computer vision and reinforcement learning.
Variational Causal Autoencoder for Interventional and Counterfactual Queries
We propose the Variational Causal Autoencoder (VCAUSE), a novel class of variational graph autoencoders for causal inference in the absence of hidden confounders, when only observational data and the causal graph are available.
RotoGrad: Gradient Homogenization in Multi-Task Learning
Multi-task learning is being increasingly adopted in applications domains like computer vision and reinforcement learning.
VACA: Design of Variational Graph Autoencoders for Interventional and Counterfactual Queries
In this paper, we introduce VACA, a novel class of variational graph autoencoders for causal inference in the absence of hidden confounders, when only observational data and the causal graph are available.
Don't Throw it Away! The Utility of Unlabeled Data in Fair Decision Making
In addition, data is often selectively labeled, i. e., even the biased labels are only observed for a small fraction of the data that received a positive decision.
Mitigating Modality Collapse in Multimodal VAEs via Impartial Optimization
We refer to this limitation as modality collapse.
Learnable Graph Convolutional Attention Networks
Existing Graph Neural Networks (GNNs) compute the message exchange between nodes by either aggregating uniformly (convolving) the features of all the neighboring nodes, or by applying a non-uniform score (attending) to the features.
Variational Mixture of HyperGenerators for Learning Distributions Over Functions
Recent approaches build on implicit neural representations (INRs) to propose generative models over function spaces.
Causal normalizing flows: from theory to practice
In this work, we deepen on the use of normalizing flows for causal reasoning.
Probabilistic Neural Transfer Function Estimation with Bayesian System Identification
While deep neural network models have demonstrated excellent power on neural prediction, they usually do not provide the uncertainty of the resulting neural representations and derived statistics, such as the stimuli driving neurons optimally, from in silico experiments.
Designing Long-term Group Fair Policies in Dynamical Systems
Neglecting the effect that decisions have on individuals (and thus, on the underlying data distribution) when designing algorithmic decision-making policies may increase inequalities and unfairness in the long term - even if fairness considerations were taken in the policy design process.
Improving the interpretability of GNN predictions through conformal-based graph sparsification
Graph Neural Networks (GNNs) have achieved state-of-the-art performance in solving graph classification tasks.
