Timo Berthold - Machine Learning Inside Mip Solvers - Ipam At Ucla

Timo Berthold - Machine Learning inside MIP solvers - IPAM at UCLA

Institute for Pure & Applied Mathematics (IPAM)

Bistra Dilkina - Machine Learning for MIP Solving - IPAM at UCLA

Institute for Pure & Applied Mathematics (IPAM)

Machine Learning inside MIP solvers

Mixed Integer Programming

Nathan Wiebe - Quantum Machine Learning - IPAM at UCLA

Institute for Pure & Applied Mathematics (IPAM)

Timo Berthold - MIP Solving: Presolving

Mixed Integer Programming

Stefan Chmiela - Machine learning basics: A speedrun - IPAM at UCLA

Institute for Pure & Applied Mathematics (IPAM)

Timo Berthold - Learning to Scale

Discrete Optimization Talks

Juliane Mueller - Adaptive Computing and multi-fidelity learning - IPAM at UCLA

Institute for Pure & Applied Mathematics (IPAM)

Rebecca Schulman - Can we build biomaterial robots with proprioception? - IPAM at UCLA

Institute for Pure & Applied Mathematics (IPAM)

Andrea Tirelli - Monte Carlo and Machine Learning Approaches in Quantum Mechanics - IPAM at UCLA

Institute for Pure & Applied Mathematics (IPAM)

Pascal Van Hentenryck - Fusing Machine Learning and Optimization - IPAM at UCLA

Institute for Pure & Applied Mathematics (IPAM)

Elvis Dohmatob - The Mathematics of Scaling Laws and Model Collapse in AI - IPAM at UCLA

Institute for Pure & Applied Mathematics (IPAM)

Sam Smith - How to train an LLM - IPAM at UCLA

Institute for Pure & Applied Mathematics (IPAM)

Marc Lackenby - Using machine learning to formulate mathematical conjectures - IPAM at UCLA

Institute for Pure & Applied Mathematics (IPAM)

Timo Berthold - MIP Solving: Primal Heuristics

Mixed Integer Programming

Osbert Bastani - Interpretable Machine Learning via Program Synthesis - IPAM at UCLA

Institute for Pure & Applied Mathematics (IPAM)