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Dr. Anna LatourExcited to attend my first PhD defence at TU Delft: Daniël Vos will be defending his thesis "Decision Tree Learning: Algorithms for Robust Prediction and Policy Optimization", containing work he did under supervision of Prof. Dr. Ir. R.L. Lagendijk and Dr. Ir. Sicco Verwer.<a href="https://mathstodon.xyz/tags/AcademicMastodon" class="mention hashtag" rel="tag">#AcademicMastodon</a> <a href="https://mathstodon.xyz/tags/AcademicChatter" class="mention hashtag" rel="tag">#AcademicChatter</a> <a href="https://mathstodon.xyz/tags/PhDLife" class="mention hashtag" rel="tag">#PhDLife</a> <a href="https://mathstodon.xyz/tags/Delft" class="mention hashtag" rel="tag">#Delft</a> <a href="https://mathstodon.xyz/tags/TUDelft" class="mention hashtag" rel="tag">#TUDelft</a> <a href="https://mathstodon.xyz/tags/DelftUniversityOfTechnology" class="mention hashtag" rel="tag">#DelftUniversityOfTechnology</a> <a href="https://mathstodon.xyz/tags/Dissertation" class="mention hashtag" rel="tag">#Dissertation</a> <a href="https://mathstodon.xyz/tags/Defence" class="mention hashtag" rel="tag">#Defence</a> <a href="https://mathstodon.xyz/tags/PhD" class="mention hashtag" rel="tag">#PhD</a> <a href="https://mathstodon.xyz/tags/PhDDefence" class="mention hashtag" rel="tag">#PhDDefence</a> <a href="https://mathstodon.xyz/tags/DecisionTrees" class="mention hashtag" rel="tag">#DecisionTrees</a> <a href="https://mathstodon.xyz/tags/Optimization" class="mention hashtag" rel="tag">#Optimization</a> <a href="https://mathstodon.xyz/tags/Optimisation" class="mention hashtag" rel="tag">#Optimisation</a> <a href="https://mathstodon.xyz/tags/Algorithms" class="mention hashtag" rel="tag">#Algorithms</a> <a href="https://mathstodon.xyz/tags/ExplainableAI" class="mention hashtag" rel="tag">#ExplainableAI</a> <a href="https://mathstodon.xyz/tags/RobustOptimization" class="mention hashtag" rel="tag">#RobustOptimization</a> <a href="https://mathstodon.xyz/tags/RobustAI" class="mention hashtag" rel="tag">#RobustAI</a> <a href="https://mathstodon.xyz/tags/Defense" class="mention hashtag" rel="tag">#Defense</a> <a href="https://mathstodon.xyz/tags/PhDDefense" class="mention hashtag" rel="tag">#PhDDefense</a>

AIROYoungThanks again to @Francy_Maggioni for the rich talk about bounds for Multistage <a href="https://mathstodon.xyz/tags/StochasticOptimization" class="mention hashtag" rel="tag">#StochasticOptimization</a> and Distributionally <a href="https://mathstodon.xyz/tags/RobustOptimization" class="mention hashtag" rel="tag">#RobustOptimization</a>! A lot of people from Italy, Brazil, and more attended the first webinar co-organized with @log_ufpb Stay tuned for the next ones😉 <a href="https://t.co/lKa2LuVeJY" target="_blank" rel="nofollow noopener noreferrer" translate="no">https://t.co/lKa2LuVeJY</a>

Jannis KurtzOur preprint "Finding Regions of Counterfactual Explanations via Robust Optimization" (written together with Donato Maragno, Tabea Röber, Rob Goedhart, Ilker Birbil and Dick den Hertog) is available online now.Paper: <a href="https://lnkd.in/embHrTvM" target="_blank" rel="nofollow noopener noreferrer" translate="no">https://lnkd.in/embHrTvM</a> Code & Slides: <a href="https://lnkd.in/eV2vaM2D" target="_blank" rel="nofollow noopener noreferrer" translate="no">https://lnkd.in/eV2vaM2D</a><a href="https://mathstodon.xyz/tags/robustoptimization" class="mention hashtag" rel="tag">#robustoptimization</a> <a href="https://mathstodon.xyz/tags/machinelearning" class="mention hashtag" rel="tag">#machinelearning</a> <a href="https://mathstodon.xyz/tags/counterfactualexplanations" class="mention hashtag" rel="tag">#counterfactualexplanations</a> <a href="https://mathstodon.xyz/tags/explainableAI" class="mention hashtag" rel="tag">#explainableAI</a>

Jannis KurtzWe completely revised our paper "Data-driven Prediction of Relevant Scenarios for Robust Combinatorial Optimization" (written together with Marc Goerigk) and replaced the old method by a completely new data-driven algorithm which generalizes well to problem instances which are of larger dimension than the training instances.<a href="https://mathstodon.xyz/tags/robustoptimization" class="mention hashtag" rel="tag">#robustoptimization</a> <a href="https://mathstodon.xyz/tags/machinelearning" class="mention hashtag" rel="tag">#machinelearning</a> <a href="https://mathstodon.xyz/tags/scenarioprediction" class="mention hashtag" rel="tag">#scenarioprediction</a><a href="https://arxiv.org/abs/2203.16642" target="_blank" rel="nofollow noopener noreferrer" translate="no">https://arxiv.org/abs/2203.16642</a>

Jannis KurtzI'm happy to share that our paper "Data-driven robust optimization using deep neural networks" (written together with Marc Goerigk) is published now in Computers & OR.We study <a href="https://mathstodon.xyz/tags/robustoptimization" class="mention hashtag" rel="tag">#robustoptimization</a> problems where observations of the uncertain parameters are given by historical data. On this data we train one-class deep <a href="https://mathstodon.xyz/tags/neuralnetworks" class="mention hashtag" rel="tag">#neuralnetworks</a> to detect outliers and extract the hidden data structures from the observations. <a href="https://doi.org/10.1016/j.cor.2022.106087" target="_blank" rel="nofollow noopener noreferrer" translate="no">https://doi.org/10.1016/j.cor.2022.106087</a>

Gabriele DragottoHighly recommended read! <a href="https://mas.to/tags/RobustOptimization" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#RobustOptimization</a> by Dimitris Bertsimas and Dick den Hertog

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