News

Mariam Barry

Adaptive Scalable Online Learning for Handling Heterogeneous Streaming Data in Large-Scale Banking Infrastructure

In this thesis, we have addressed different algorithmic and infrastructure challenges faced when dealing with online machine learning capabilities over high-volume data streams from heterogeneous sources. The research encompasses big data summarization, the construction of industrial knowledge graphs dynamically updated, online change detection, and the operationalization of streaming models in production. Initially, we introduced StreamFlow, an incremental algorithm and a system for big data summarization, generating feature vectors suitable for both batch and online machine learning tasks. These enriched features significantly enhance the performance of both time and accuracy for training batch and online machine-learning models. Subsequently, we proposed Stream2Graph, a stream-based solution facilitating the dynamic and incremental construction and updating of enterprise knowledge graphs. Experimental results indicated that leveraging graph features in conjunction with online learning notably enhances machine learning outcomes. Thirdly, we presented StreamChange, an explainable online change detection model designed for big data streaming, featuring constant space and time complexity. Real-world experiments demonstrated superior performance compared to state-of-the-art models, particularly in detecting both gradual and abrupt changes. Lastly, we demonstrated the operationalization of online machine learning in production, enabling horizontal scaling and incremental learning from streaming data in real-time. Experiments utilizing feature-evolving datasets with millions of dimensions validated the effectiveness of our MLOps pipelines. Our design ensures model versioning, monitoring, audibility, and reproducibility, affirming the efficiency of employing online learning models over batch methods in terms of both time and space complexity.

Rajaa El Hamdani

Towards Zero-Shot Knowledge Base Construction with Pretrained Large Language Models

Joint work with Mehwish Alam, Thomas Bonald, Fragkiskos Malliaros

Knowledge bases are critical tools for structuring and understanding information, yet creating them from scratch is expensive and time-consuming.

This paper presents a methodology for Knowledge Base Construction (KBC) using Pretrained Large Language Models (PLLMs), particularly focusing on extracting structured data from natural language texts. Our objective is to evaluate the efficiency of PLLMs, specifically GPT-4, in a zero-shot learning setting for KBC within the legal domain, using Wikipedia articles as our primary data source. This approach is unique in its domain and text-agnostic nature, enabling scalable applications across various fields by simply extending the taxonomy.

Our initial findings show that while GPT-4 exhibits high F1 scores for some properties, it struggles with those requiring deep domain understanding. Interestingly, GPT-4 also surfaced verifiable facts not present in our ground truth, indicating its potential for uncovering novel information.

Charbel-Raphael Segerie

https://crsegerie.github.io

An introduction to AI Safety

The rapid advancements in artificial intelligence is advancing quickly. While these technologies are awe-inspiring, models like ChatGPT or Bing Chat, although specifically developed to be polite and benevolent towards the user, can be easily manipulated.

In this presentation, we will address these major technical flaws. These models remain large black boxes and we cannot guarantee that their actions will conform to our expectations. A second flaw is the lack of robustness; the models are trained on a particular dataset and must therefore generalize to new situations during their deployment. The fact that Bing Chat threatens users when it was trained to help them illustrates this failure of generalization. The third flaw lies in the difficulty of specifying precisely to a model the desired objective, given the complexity and diversity of human values.

Then, we will address different solution paradigms: Specification techniques with Reinforcement Learning (RLHF and its variations), interpretability (how information is represented in neural networks, robustly editing a language model’s knowledge by modifying its memory, …), scalable oversight (training and alignment techniques that are likely to work even with human-level AIs).

Simon Delarue and Thomas Bonald

Sparse Graph Neural Networks with Scikit-network (Simon Delarue)

Joint work with Thomas Bonald

In recent years, Graph Neural Networks (GNNs) have undergone rapid development and have become an essential tool for building representations of complex relational data. Large real-world graphs, characterised by sparsity in relations and features, necessitate dedicated tools that existing dense tensor-centred approaches cannot easily provide. To address this need, we introduce a GNNs module in Scikit-network, a Python package for graph analysis, leveraging sparse matrices for both graph structures and features. Our contribution enhances GNNs efficiency without requiring access to significant computational resources, unifies graph analysis algorithms and GNNs in the same framework, and prioritises user-friendliness.

A Consistent Diffusion-Based Algorithm for Semi-Supervised Graph Learning (Thomas Bonald)

Joint work with Nathan De Lara

The task of semi-supervised classification aims at assigning labels to all nodes of a graph based on the labels known for a few nodes, called the seeds. One of the most popular algorithms relies on the principle of heat diffusion, where the labels of the seeds are spread by thermo-conductance and the temperature of each node at equilibrium is used as a score function for each label. In this paper, we prove that this algorithm is not consistent unless the temperatures of the nodes at equilibrium are centered before scoring. This crucial step does not only make the algorithm provably consistent on a block model but brings significant performance gains on real graphs.

Nedeljko Radulovic

Post-hoc Explainable AI for Black Box Models on Tabular Data

Current state-of-the-art Artificial Intelligence (AI) models have been proven to be very successful in solving various tasks, such as classification, regression, Natural Language Processing (NLP), and image processing. The resources that we have at our hands today allow us to train very complex AI models to solve problems in almost any field: medicine, finance, justice, transportation, forecast, etc. With the popularity and widespread use of the AI models, the need to ensure the trust in them also grew. Complex as they come today, these AI models are impossible to be interpreted and understood by humans. In this thesis, we focus on the specific area of research, namely Explainable Artificial Intelligence (xAI), that aims to provide the approaches to interpret the complex AI models and explain their decisions. We present two approaches STACI and BELLA which focus on classification and regression tasks, respectively, for tabular data.

Both methods are deterministic model-agnostic post-hoc approaches, which means that they can be applied to any black-box model after its creation. In this way, interpretability presents an added value without the need to compromise on black-box model’s performance. Our methods provide accurate, simple and general interpretations of both the whole black-box model and its individual predictions. We confirmed their high performance through extensive experiments and a user study.

Julien Lie-Panis

Models of reputation-based cooperation. Bridging the Gap between Reciprocity and Signaling.

Human cooperation is often understood through the lens of reciprocity. In classic models, cooperation is sustained because it is reciprocal: individuals who bear costs to help others can then expect to be helped in return. Another framework is honest signal theory. According to this approach, cooperation can be sustained when helpers reveal information about themselves, which in turn affects receivers’ behavior. Here, we aim to bridge the gap between these two approaches, in order to better characterize human cooperation. We show how integrating both approaches can help explain the variability of human cooperation, its extent, and its limits.

In chapter 1, we introduce evolutionary game theory, and its application to human behavior.

In chapter 2, we show that cooperation with strangers can be understood as a signal of time preferences. In equilibrium, patient individuals cooperate more often, and individuals who reveal higher preference for the future inspire more trust. We show how our model can help explain the variability of cooperation and trust.

In chapter 3, we turn to the psychology of revenge. Revenge is often understood in terms of enforcing cooperation, or equivalently, deterring transgressions: vengeful individuals pay costs, which may be offset by the benefit of a vengeful reputation. Yet, revenge does not always seem designed for optimal deterrence. Our model reconciles the deterrent function of revenge with its apparent quirks, such as our propensity to overreact to minuscule transgressions, and to forgive dangerous behavior based on a lucky positive outcome.

In chapter 4, we turn to dysfunctional forms of cooperation and signaling. We posit that outrage can sometimes act as a second-order signal, demonstrating investment in another, first-order signal. We then show how outrage can lead to dishonest displays of commitment, and escalating costs.

In chapter 5, we extend the model in chapter 2 to include institutions. Institutions are often invoked as solutions to hard cooperation problems: they stabilize cooperation in contexts where reputation is insufficient. Yet, institutions are at the mercy of the very problem they are designed to solve. People must devote time and resources to create new rules and compensate institutional operatives. We show that institutions for hard cooperation problems can emerge nonetheless, as long as they rest on an easy cooperation problem. Our model shows how designing efficient institutions can allow humans to extend the scale of cooperation.

Finally, in chapter 6, we discuss the merits of mathematical modeling in the social sciences.