Seminars - page 3
The DIG team holds a seminar about every two weeks with speakers either from the team, or invited.
You can add the seminars to your calendar with this ics file, and get emails about future seminars by subscribing to our mailing-list.
If you would like to present your work at our seminar, please contact Nils.
Upcoming Seminars
Riding the Waves of Change: Towards Streaming Continual Learning
Thursday, November 19, 2026 11:45, 4A301
Federico Giannini (Politecnico di Milano)
Real-world data streams continuously evolve, requiring Machine Learning models to adapt to changing distributions while preserving previously acquired knowledge. Some streaming scenarios, such as sensor data streams, also exhibit temporal dependencies. This setting is defined as Evolving Streaming Time Series (ESTS).
Past Seminars
Robust Knowledge Graph Cleaning
Tuesday, May 27, 2025 11:45, 4A301
Maximilian Egger
Data quality is needed to properly and reliably use the information represented in the dataset. The increasing volume of data renders data preparation and cleaning increasingly difficult. Additionally, more diverse types of data structures for databases, like graphs, get used and need to be handled differently. This leads to the necessity of robust methods to increase data integrity, scalable approaches for finding and fixing errors, and local-oriented algorithms that can be used to pinpoint attention where needed.
Synthesis & Augmentation of Tabular Data In the Age of Foundation Models
Tuesday, May 13, 2025 11:45, 4A301
Nikola Simidjievski
Foundation models - large pre-trained performant models - have shown remarkable success in applications that predominately focus on vision, language, and sound data. On the other hand, tabular data - one of the most prevalent data modalities in many critical domains of business, science, and healthcare - has seen limited benefits from these advances. Tabular data poses unique challenges that relate to heterogeneity, dimensionality, and scarcity as well as lack of explicit symmetries, implicit structures and incomplete prior knowledge – all of which have limiting effects on how we construct, train and apply/transfer large models for tabular data.
GPTKB: Comprehensively Materializing Factual LLM Knowledge
Tuesday, April 29, 2025 11:45, 4A301
Simon Razniewski (TU Dresden)
LLMs have majorly advanced NLP and AI, and next to their ability to perform a wide range of procedural tasks, a major success factor is their internalized factual knowledge. Since (Petroni et al., 2019), analyzing this knowledge has gained attention. However, most approaches investigate one question at a time via modest-sized pre-defined samples, introducing an “availability bias” (Tversky and Kahneman, 1973) that prevents the discovery of knowledge (or beliefs) of LLMs beyond the experimenter’s predisposition. To address this challenge, we propose a novel methodology to comprehensively materialize an LLM’s factual knowledge through recursive querying and result consolidation. As a prototype, we employ GPT-4o-mini to construct GPTKB, a large-scale knowledge base (KB) comprising 101 million triples for over 2.9 million entities. This work marks a milestone in two areas: For LLM research, for the first time, it provides constructive insights into the scope and structure of LLMs’ knowledge (or beliefs), and its strengths and weaknesses. For KB construction, it pioneers new pathways for the long-standing challenge of general-domain KB construction. GPTKB is accessible at https://gptkb.org.
ProvSQL: Provenance and Probabilistic Querying in Uncertain Databases
Tuesday, April 08, 2025 11:45, 4A125
Pratik Karmakar (None)
Probabilistic databases provide a powerful framework for managing and querying uncertain data, enabling principled reasoning under uncertainty. ProvSQL extends PostgreSQL to support provenance tracking and probability computation in probabilistic databases, leveraging provenance circuits to efficiently compute probabilities and Shapley-based data valuations. In this talk, we introduce ProvSQL, demonstrate its capabilities, and explore a key use case—content based image retrieval from the COCO dataset. We show how probabilistic query evaluation and data valuation techniques enhance explainability and trust in AI-driven decision-making.
Tabular foundation models: priors for numbers and strings
Tuesday, March 25, 2025 11:45, 4A301
Gaël Varoquaux (INRIA)
Deep-learning typically does not outperform tree-based models on tabular data. Often this may be explained by the small size of such datasets. For images, sound, text, the solution has be pretrained models, leading to foundation models, adapted and reused for many tasks. I will discuss the challenges to bring these ideas to tabular learning, and the progress that we have made, building priors for tables, ie columns of different natures, with numbers and strings.
Neuro-symbolic approaches for the knowledge graph lifecycle
Tuesday, March 18, 2025 11:45, 4A301
Pierre Monnin (INRIA)
In the Web of Data, an increasing number of knowledge graphs (KGs) are concurrently published, edited, and accessed by human and software agents. Their wide adoption makes essential the tasks of their lifecycle: construction, refinement (e.g., matching, link prediction), mining, and usage to support applications (e.g., explainable AI, recommender systems). However, all these tasks require facing the inherent heterogeneity of KGs, e.g., in terms of granularities, vocabularies, and completeness. Besides, scalability issues arise due to their increasing size and combinatorial nature. In my talk, I will present my research on neuro-symbolic approaches for the KG lifecycle, intertwining domain knowledge from ontologies, deductive reasoning, analogical reasoning, and machine learning models. Throughout my presentation, I will show that such approaches enhance models by improving their semantic awareness, frugality, and the semantic interpretability of their latent representation space.
None
Tuesday, March 04, 2025 11:45, 4A301
Ken Satoh (None)
None
None
Tuesday, February 04, 2025 11:45, 4A125
Fabian (None)
None
None
Tuesday, January 21, 2025 11:45, 4A301
Simon Delarue (None)
None
None
Tuesday, December 10, 2024 11:45, 4A125
Lanfang Kong (None)
None