Seminars - page 5

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

Toward Adaptive Intelligence

Tuesday, October 13, 2026 11:45, 4A301

Nilesh Verma (University of Waikato)

Real-world data arrives as an endless stream whose distribution shifts over time. Models that work today may degrade tomorrow, and the human effort required to repeatedly re-tune them does not scale. This talk presents a line of work on adaptive intelligence, where systems select, configure, and revise their own learning pipelines as the data changes. I begin with AutoML for non-stationary data streams, covering online pipeline search, automated drift and outlier handling, and meta-learning for data streams. I then turn to tabular foundation models and ask what happens when in-context learners encounter concept drift. Along the way, I introduce TuiML, an open-source MCP-native ML runtime that enables agents and researchers to run, benchmark, and empirically study machine learning workflows.

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From Observations to Explanations: Exploring Methodologies for Identifying Relevant Actual Causes in Complex Systems

Tuesday, October 27, 2026 11:45, 4A301

Samuel Reyd

Complex systems such as multi-agent and cyber-physical systems exhibit emergent behaviors that are hard to understand and explain. Causal models offer a principled framework for this, but building one for a complex system is already difficult: the relevant scope, abstraction scale, and variables are not given in advance. Moreover, such models capture general causation, i.e., which factors influence a class of outcomes, whereas a user asking “why did this happen?” needs an actual cause: the specific facts responsible for a specific event. Actual causation is conceptually and computationally demanding; finding actual causes is intractable in general, and not all causes are equally informative. Producing a useful explanation therefore requires three steps: modeling the system causally, identifying its actual causes, and filtering them for relevance. This model-identify-filter pipeline forms the backbone of the thesis. This thesis first reviews the challenges of causal modeling in complex adaptive systems and examines the choice of abstraction scale through causal emergence, showing that emergence detection depends heavily on the sampling distribution used. It then addresses the identification of actual causes, proposing a data-based method for Markovian multivariate systems and approximate search algorithms with adjustable precision that recover the full set of actual causes in general SCMs, released as an open-source Python module. Finally, it presents a general, tunable method for selecting relevant causes according to user and context, and develops a normality criterion to produce more relevant explanations. Overall, the thesis connects formal causal models, scalable computation, and human-centred explanation into a coherent framework for context-aware explanation in complex systems.

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