Program for stream Optimization and Artificial Intelligence

TE-01: Optimization and Artificial Intelligence I
Stream: Optimization and Artificial Intelligence
Room: Fermat
Chair(s): Fabian Bastin

• Exploiting Sparse Decision Trees To Learn Minimal Rules
  Tommaso Aldinucci
• Improved Forward Selection Algorithms for Sparse Principal Component Analysis
  Mustafa Pinar
• Kernel Regression with Hard Shape Constraints
  
• Leveraging deep reinforcement learning through the framework of operations research
  Ysaël Desage, Fabian Bastin, François Bouffard

FA-05: Optimization and Artificial Intelligence II
Stream: Optimization and Artificial Intelligence
Room: Pontryagin
Chair(s): Sébastien Gerchinovitz

• K-Quant: a non uniform post-training quantization algorithm
  Enrico Civitelli, Leonardo Taccari, Fabio Schoen
• An Adaptive ML-Based Discretization Method for Computing Optimal Experimental Designs
  Philipp Seufert, Jan Schwientek, Tobias Seidel, Michael Bortz, Karl-Heinz Küfer
• Sparse RBF Regression for the Optimization of Noisy Expensive Functions
  Alessio Sortino, Matteo Lapucci, Fabio Schoen
• On the optimality of the Piyavskii-Shubert algorithm for global Lipschitz optimization: a bandit perspective
  Clément Bouttier, Sébastien Gerchinovitz, Tommaso Cesari