This blog post is written by AI CDT student, Matt Clifford
ESSAI 2023 – https://essai.si/
A few of us from the CDT – Me (Matt), Jonny and Rachael attended the ESSAI summer school on the 24th -28th of July 2023. ESSAI is the first European summer school on Artificial Intelligence and was held in Ljubljana, Slovenia. There were a variety of interesting topics and classes on offer (https://essai.si/schedule/) but here I’ll share some of the classes that I attended. I’ll keep the information brief of each topic here but feel free to reach out to me if you would like to chat through any of the topics which might be useful to you or if would like to know more!
AutoML – https://www.automl.org/
Optimise machine learning algorithm hyperparameters and Neural architectures automatically by using various techniques (Baysian optimisation etc.) Python packages for sklearn and pytorch: https://pypi.org/project/smac/
https://github.com/automl/Auto-PyTorch
Very useful when you want a more objective training approach which will save you time, computation and more importantly frustration!
Learning Beyond Static Datasets – https://owll-lab.com/
Exploring mechanisms to help catastrophic forgetting when learning a new task in ML.
Topics related to: transfer learning, active learning, continual learning, lifelong learning, curriculum learning, open world learning, knowledge distillation.
A nice survey paper to map out the whole landscape – https://www.sciencedirect.com/science/article/pii/S089360802300014X?via%3Dihub
Uncertainty Quantification
Adding uncertainty to a model (important with neural networks being so overly confident!). Methods can either be inherent (Bayesian NN etc.) or post hoc (calibration, ensembling, Monte-Carlo dropout) and can disentangle aleatoric and epistemic uncertainty measures.
Fairness & Privacy –
https://aif360.readthedocs.io/en/latest/
The president of Slovenia (plus her not so inconspicuous bodyguards) attended these talks which was a bit of a surprise!
Explored navigating the somewhat conflicting landscape of statical fairness by ensuring groups of people have the same model statistics. Picking which statistics, however, not so easy and it’s impossible to ensure all statistics match in real life scenarios – https://arxiv.org/pdf/2304.06057.pdf .
Also looked at privacy through anonymity (K-anonymity, L-diversity, T-closeness) and differential privacy. I won’t go into details but thought I’d mention some of the main techniques currently used in academic and industry.
Again, let me know if you want to go into the details of anything that is useful or interesting to you!
Also, a side note, Slovenia is an amazingly beautiful country, and I can very much recommend to anyone thinking of going! Here’s a few photos:
