Jascha Grübel

Author: Jascha Grübel

  • Geometry of Big Data – Monday session

    All talks are summarised in my words which may not accurately represent the authors’ opinion. The focus is on aspects I found interesting. Please refer to the authors’ work for more details. Session 1 – Learning DAGs The talk DAGs with NO TEARS: Continuous Optimization for Structure Learning is given by Pradeep Ravikumar. A draft…

  • Starting “Geometry and Learning from Data in 3D and Beyond” at IPAM, UCLA

    Today is the first day of my stay at the Institute for Pure and Applied Mathematics (IPAM) at University of California Los Angeles (UCLA). Over the coming weeks I wil try to discuss interesting talks here at the long course Geometry and Learning from Data in 3D and Beyond. Stay tuned for the first workshop…

  • A Manifesto to Cite 50/50

    I recently came across Women Also Know Stuff. I think it is a great initiative that helps to slowly combat systemic and structural inequality. They point to many female scientists in most social sciences and I wondered whether I could find a similar program in computer science. The answer was no because apparently we first…

  • Off to the Chicago Forum on Global Cities

    Today I write you as part of a mini-series on my stay at the Chicago Forum on Global Cities (CFGC). I have been kindly sponsored by ETH Zurich and the Chicago Forum to participate in the event. I am currently sitting in my train to Zurich airport and I am looking forward to 3 days…

  • PIE: Ex Post Evaluation: Establishing Causality without Experimentation

    So far, we discussed evaluation based on ex ante Randomised Control Trials (RCT). In ex post experiments, we have an another opportunity for an evaluation. However, there are strong limitations: Treatment manipulation is no longer possible, observational data only (i.e. the outcome of social processes), and baseline may be missing To address these issues, the…

  • PIE: Ex Ante Evaluations: Randomised Control Trials

    For a Randomised Control Trial (RCT) several elements are necessary. Evaluators need to be involved long before it ends – ideally from the conception. Randomisation must take place. The operationalisation and measurement must be defined. The data collection process and the data analysis must be performed rigorously. Randomisation and the data collection process is what…

  • PIE: The Fundamental Problem of Causal Inference

    We evaluate policies for a multitude of reasons. On the one hand, we wish to increase our knowledge and learn about its underlying function to improve program design and effectiveness. On the other hand, considerations from economy, society, and politics are the reason behind the evaluation. This may include allocation decisions via cost-benefit analysis (economic),…

  • ASC: Concepts and Arguments

    The evaluation of the correctness of arguments is the core of this blog post. We will focus on justifications as premises are to be evaluated with the scientific method. However, the quality of premises must be considered. Only true premises can guarantee the truth of the conclusion, so the reasons must be impeccable. Therefore, acceptable…

  • SMABSC: Cognitive Agents

    Cognitive models are a representation of an agent control mechanism resembling the cognitive architecture of a mind.  It can be understood as a control system (e.g. a flow graph how to react) that takes sensory inputs and produces motor outputs (Piaget, 1985). More advanced models include adaptive memory (Anderson, 1983). Famous models include SOaR: State…

  • SMABSC: Disease Propagation

    The SIR model was introduced as a mathematical model with differential equations (Kermack & McKendrick, 1927). The basic states are Susceptible, Infected, and Recovered. In the SIR model, the fundamental trajectory of disease propagation could be captured, immunity was acquired after disease and the population is homogeneous. But the SIR model has short-comings: populations are…