The programme below is tentative and may still be subject to minor changes.
| 09:30–09:45 | Opening remarks |
| 09:45–11:15 | Pierre Tarrès — Minicourse (Lecture 1) |
| 11:15–11:45 | Coffee break |
| 11:45–12:45 | Invited speaker 1 |
| 12:45–14:00 | Lunch break |
| 14:00–15:00 | Invited speaker 2 |
| 15:00–15:30 | Coffee break |
| 15:30–16:30 | Invited speaker 3 |
| 09:30–11:00 | Pierre Tarrès — Minicourse (Lecture 2) |
| 11:00–11:30 | Coffee break |
| 11:30–12:30 | Invited speaker 4 |
| 12:30–14:00 | Lunch break |
| 14:00–15:00 | Invited speaker 5 |
| 15:00–15:30 | Coffee break |
| 15:30–16:30 | Invited speaker 6 |
| 09:30–11:00 | Pierre Tarrès — Minicourse (Lecture 3) |
| 11:00–11:30 | Coffee break |
| 11:30–12:30 | Invited speaker 7 |
| 12:30–14:00 | Lunch break |
| 14:00–15:00 | Invited speaker 8 |
| 15:00–15:30 | Coffee break |
| 15:30–17:00 | Discussion session: “Probability meets AI” |
| 19:00–22:00 | Workshop dinner |
| 09:30–10:30 | Invited speaker 9 |
| 10:30–11:00 | Coffee break |
| 11:00–12:00 | Invited speaker 10 |
| 12:00–12:15 | Closing remarks |
| 12:15–13:30 | Lunch |
The critical window in growing random graphs
We describe the critical window for percolation on sparse growing random graphs. We consider a model of preferential attachment type, in which vertices arrive sequentially and connect independently to each earlier vertex with a probability proportional to a nonnegative power of its arrival time, thus giving preference to old vertices. Whenever the percolation threshold is positive, we show that the critical window has width of order 1/(log n)2 and a secondary phase transition at its finite upper boundary. Inside this window the largest component has size of order √n/log n, and the susceptibility remains finite and independent of the position in the window. The proofs couple component explorations to branching random walks killed outside an interval of length log n, allowing sharp control of the barely subcritical and critical regimes. The talk is based on joint work with Joost Jorritsma (Oxford) and Pascal Maillard (Toulouse).
Convergence of Reinforcement-Learning Algorithms
Abstract to follow.