Search Results for author:
Found 27 papers, 8 papers with code
Causal inference using the algorithmic Markov condition
We explain why a consistent reformulation of causal inference in terms of algorithmic complexity implies a new inference principle that takes into account also the complexity of conditional probability densities, making it possible to select among Markov equivalent causal graphs.
Kernel-based Conditional Independence Test and Application in Causal Discovery
Conditional independence testing is an important problem, especially in Bayesian network learning and causal discovery.
Inferring deterministic causal relations
We consider two variables that are related to each other by an invertible function.
On Causal and Anticausal Learning
We consider the problem of function estimation in the case where an underlying causal model can be inferred.
Modeling Information Propagation with Survival Theory
Networks provide a skeleton for the spread of contagions, like, information, ideas, behaviors and diseases.
Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders
We propose a kernel method to identify finite mixtures of nonparametric product distributions.
Adaptive nonparametric detection in cryo-electron microscopy
Cryo-electron microscopy (cryo-EM) is an emerging experimental method to characterize the structure of large biomolecular assemblies.
Estimating Diffusion Network Structures: Recovery Conditions, Sample Complexity & Soft-thresholding Algorithm
Can we recover the hidden network structures from these observed cascades?
From Ordinary Differential Equations to Structural Causal Models: the deterministic case
We show how, and under which conditions, the equilibrium states of a first-order Ordinary Differential Equation (ODE) system can be described with a deterministic Structural Causal Model (SCM).
One-Class Support Measure Machines for Group Anomaly Detection
We propose one-class support measure machines (OCSMMs) for group anomaly detection which aims at recognizing anomalous aggregate behaviors of data points.
Causal inference for data-driven debugging and decision making in cloud computing
Cloud computing involves complex technical and economical systems and interactions.
From Deterministic ODEs to Dynamic Structural Causal Models
Structural Causal Models are widely used in causal modelling, but how they relate to other modelling tools is poorly understood.
Detecting confounding in multivariate linear models via spectral analysis
We study a model where one target variable Y is correlated with a vector X:=(X_1,..., X_d) of predictor variables being potential causes of Y.
From optimal transport to generative modeling: the VEGAN cookbook
We study unsupervised generative modeling in terms of the optimal transport (OT) problem between true (but unknown) data distribution $P_X$ and the latent variable model distribution $P_G$.
Probabilistic Active Learning of Functions in Structural Causal Models
We consider the problem of learning the functions computing children from parents in a Structural Causal Model once the underlying causal graph has been identified.
Wasserstein Auto-Encoders
We propose the Wasserstein Auto-Encoder (WAE)---a new algorithm for building a generative model of the data distribution.
Leveraging the Crowd to Detect and Reduce the Spread of Fake News and Misinformation
Online social networking sites are experimenting with the following crowd-powered procedure to reduce the spread of fake news and misinformation: whenever a user is exposed to a story through her feed, she can flag the story as misinformation and, if the story receives enough flags, it is sent to a trusted third party for fact checking.
Optimizing Human Learning
Can we find the optimal reviewing schedule to maximize the benefits of spaced repetition?
On the difference between building and extracting patterns: a causal analysis of deep generative models.
Generative models are important tools to capture and investigate the properties of complex empirical data.
On the Latent Space of Wasserstein Auto-Encoders
We study the role of latent space dimensionality in Wasserstein auto-encoders (WAEs).
Detecting non-causal artifacts in multivariate linear regression models
We consider linear models where $d$ potential causes $X_1,..., X_d$ are correlated with one target quantity $Y$ and propose a method to infer whether the association is causal or whether it is an artifact caused by overfitting or hidden common causes.
Tinkering with black boxes: counterfactuals uncover modularity in generative models
Deep generative models such as Generative Adversarial Networks (GANs) and Variational Auto-Encoders (VAEs) are important tools to capture and investigate the properties of complex empirical data.
Differentially Private Language Models for Secure Data Sharing
To protect the privacy of individuals whose data is being shared, it is of high importance to develop methods allowing researchers and companies to release textual data while providing formal privacy guarantees to its originators.
Psychologically-Inspired Causal Prompts
In this work, we take sentiment classification as an example and look into the causal relations between the review (X) and sentiment (Y).
Voices of Her: Analyzing Gender Differences in the AI Publication World
While several previous studies have analyzed gender bias in research, we are still missing a comprehensive analysis of gender differences in the AI community, covering diverse topics and different development trends.
On the Causal Nature of Sentiment Analysis
Sentiment analysis (SA) aims to identify the sentiment expressed in a text, such as a product review.
Mining the Cause of Political Decision-Making from Social Media: A Case Study of COVID-19 Policies across the US States
Mining the causes of political decision-making is an active research area in the field of political science.
