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Ph.D de

Group : Learning and Optimization

Generative Neural Networks to Infer Causal Mechanisms: Algorithms and Applications

Starts on 01/01/2017
Advisor : GUYON, Isabelle

Funding : contrat doctoral UPS
Affiliation : Université Paris-Sud
Laboratory : LRI - amphithéâtre Shannon du Bâtiment 660

Defended on 17/12/2019, committee :
Kristin Bennett, Professeur, Rensselaer Polytechnic Institute | Rapporteur
Kun Zhang, Assistant Professor, Carnegie Mellon University | Rapporteur
Jean-Pierre Nadal, Directeur de Recherche au CNRS, École Normale Supérieure | Examinateur
Julie Josse, Professeur, CMAP & INRIA | Examinateur

Research activities :

Abstract :
Causal discovery is of utmost importance for agents who must plan, reason and decide based on observations; where mistaking correlation with causation might lead to unwanted consequences. The gold standard to discover causal relations is to perform experiments. However, experiments are in many cases expensive, unethical, or impossible to realize. In these situations, there is a need for observational causal discovery, that is, the estimation of causal relations from observations alone. Causal discovery in the observational data setting traditionally involves making significant assumptions on the data and on the underlying causal model.
This thesis aims to alleviate some of the assumptions made on the causal models by exploiting the modularity and expressiveness of neural networks for causal discovery, leveraging both conditional independencies and simplicity of the causal mechanisms through two algorithms. Extensive experiments on both simulated and real-world data and a throughout theoretical analysis prove the good performance and the soundness of the proposed approaches.

Ph.D. dissertations & Faculty habilitations
Question Answering is a discipline which lies in between natural language processing and information retrieval domains. Emergence of deep learning approaches in several fields of research such as computer vision, natural language processing, speech recognition etc. has led to the rise of end-to-end models. In the context of GoASQ project, we investigate, compare and combine different approaches for answering questions formulated in natural language over textual data on open domain and biomedical domain data. The thesis work mainly focuses on 1) Building models for small scale and large scale datasets, and 2) Leveraging structured and semantic information into question answering models. Hybrid data in our research context is fusion of knowledge from free text, ontologies, entity information etc. applied towards free text question answering. The current state-of-the-art models for question answering use deep learning based models. In order to facilitate using them on small scale datasets on closed domain data, we propose to use domain adaptation. We model the BIOASQ biomedical question answering task dataset into two different QA task models and show how the Open Domain Question Answering task suits better than the Reading Comprehension task by comparing experimental results. We pre-train the Reading Comprehension model with different datasets to show the variability in performance when these models are adapted to biomedical domain. We find that using one particular dataset (SQUAD v2.0 dataset) for pre-training performs the best on single dataset pre-training and a combination of four Reading Comprehension datasets performed the best towards the biomedical domain adaptation. We perform some of the above experiments using large scale pre-trained language models like BERT which are fine-tuned to the question answering task. The performance varies based on the type of data used to pre-train BERT. For BERT pre-training on the language modelling task, we find the biomedical data trained BIOBERT to be the best choice for biomedical QA. Since deep learning models tend to function in an end-to-end fashion, semantic and structured information coming from expert annotated information sources are not explicitly used. We highlight the necessity for using Lexical and Expected Answer Types in open domain and biomedical domain question answering by performing several verification experiments. These types are used to highlight entities in two QA tasks which shows improvements while using entity embeddings based on the answer type annotations. We manually annotated an answer variant dataset for BIOASQ and show the importance of learning a QA model with answer variants present in the paragraphs. Our hypothesis is that the results obtained from deep learning models can further be improved using semantic features and collective features from different paragraphs for a question. We propose to use ranking models based on binary classification methods to better rank Top-1 prediction among Top-K predictions using these features, leading to an hybrid model that outperforms state-of-art-results on several datasets. We experiment with several overall Open Domain Question Answering models on QA sub-task datasets built for Reading Comprehension and Answer Sentence Selection tasks. We show the difference in performance when these are modelled as overall QA task and highlight the wide gap in building end-to-end models for overall question answering task.

The original manuscript conceptualizes the recent rise of digital platforms along three main dimensions: their nature of coordination devices fueled by data, the ensuing transformations of labor, and the accompanying promises of societal innovation. The overall ambition is to unpack the coordination role of the platform and where it stands in the horizon of the classical firm – market duality. It is also to precisely understand how it uses data to do so, where it drives labor, and how it accommodates socially innovative projects. I extend this analysis to show continuity between today’s society dominated by platforms and the “organizational society”, claiming that platforms are organized structures that distribute resources, produce asymmetries of wealth and power, and push social innovation to the periphery of the system. I discuss the policy implications of these tendencies and propose avenues for follow-up research.