Topic: optimal policies

Tito Homem-de-Mello presents decision-focused learning for two-stage stochastic optimization conditioned on contextual data. Recent theory shows that some two-stage stochastic linear programs can be solved using a single, initially unknown scenario. The talk establishes when learned scenario generators induce optimal policies and why one scenario component outside the observed support can suffice. A log-barrier smoothing operator makes that learning possible. The resulting method draws on interior-point optimization and is evaluated on standard contextual stochastic-programming benchmarks.

We trained five rats to perform a complex auditory-guided task in a large environment (diameter 160 cm) with twelve nose-poke ports. To obtain rewards, rats had to position themselves at specific locations indicated by sounds. Despite the nontrivial task, rats reached high success rates within two 70-minute sessions. We modeled the task as a Markov Decision Process. Observed rat trajectories resembled the model's optimal policies. However, while optimal policies were deterministic, observed behavior was non-deterministic. We introduced non-deterministic, information-limited policies that reali

One of Tali's cherished goals was to transform biology into physics. In his view, biologists were far too enamored by the details of the specific models they studied, losing sight of the big principles that may govern the behavior of these models. One such big principle that he suggested was the 'information bottleneck (IB) principle'. The iIB principle is an information-theoretical approach for extracting the relevant information that one random variable carries about another. Tali applied the IB principle to numerous problems in biology, gaining important insights in the process. Here I will