Speaker Professor Luis Nunes Vicente will talk about 'Reducing Sample Complexity in Stochastic Derivative-Free Optimization via Tail Bounds and Hypothesis Testing';

We introduce and analyze new probabilistic strategies for enforcing sufficient decrease conditions in stochastic derivative-free optimization, with the goal of reducing sample complexity and simplifying convergence analysis. First, we develop a new tail bound condition imposed on the estimated reduction in function value, which permits flexible selection of the power used in the sufficient decrease test, q in (1,2]. This approach allows us to reduce the number of samples per iteration from the standard O(delta^{−4}) to O(delta^{-2q}), assuming that the noise moment of order q/(q-1) is bounded. Second, we formulate the sufficient decrease condition as a sequential hypothesis testing problem, in which the algorithm adaptively collects samples until the evidence suffices to accept or reject a candidate step. This test provides statistical guarantees on decision errors and can further reduce the required sample size, particularly in the Gaussian noise setting, where it can approach O(delta^{−2-r}) when the decrease is of the order of delta^r. We incorporate both techniques into stochastic direct-search and trust-region methods for potentially non-smooth, noisy objective functions, and establish their global convergence rates and properties. 

This is joint work with Anjie Ding, Francesco Rinaldi, and Damiano Zeffiro.

Migrant communities shape cross-border investment to their country of origin by reducing

information frictions and attitudes bias. Whether these benefits spill over to locals depends

not only on the size of the diaspora but also on the intensity of interaction between migrants

and locals in the host country. I present a theoretical model with agent-based simulation to

study how homophily between migrants and locals affects information and attitude diffusion

in the host society. I implement varying homophily preferences in a Schelling-style

segregation model and compare two diffusion processes: (i) a simple susceptible–infected

(SI) model for information diffusion; (ii) an adoption-threshold model for attitude diffusion.

For information diffusion, preliminary results indicate that higher homophily slows the

spread and confines diffusion within the migrant group, especially under high segregation. In

the attitude model, adoption varies non-monotonically with homophily. I also provide an

initial analysis of how these patterns interact with different migrant population shares and

seeding rules.

TBA

Major depressive disorder (MDD) is a heterogeneous mental disorder. International guidelines present overall symptom severity as the key dimension for clinical characterisation. However, additional layers of heterogeneity may reside within severity levels related to how symptoms interact with one-another in a patient, called symptom dynamics. We investigate these individual differences by estimating the proportion of patients that display differences in their symptom dynamics while sharing the same diagnosis and overall symptom severity. We show that examining symptom dynamics provides information about the person-specific psychopathological expression of patients beyond severity levels by revealing how symptoms aggravate each other over time. These results suggest that symptom dynamics may serve as a promising new dimension for clinical characterisation. Areas of opportunity are outlined for the field of precision psychiatry in uncovering disorder evolution patterns (e.g., spontaneous recovery; critical worsening) and the identification of granular treatment effects by moving toward investigations that leverage symptom dynamics as their foundation. Future work aimed at investigating the cascading dynamics underlying depression onset and maintenance using the large-scale (N > 5.5 million) CIPA Study are outlined. 

A 1977 conjecture of Erdős and Hajnal asserts that for every hereditary class of graphs not containing all graphs, every graph in the class has a polynomial-sized clique or stable set. Fox, Pach, and Suk and independently Chernikov, Starchenko, and Thomas asked whether this conjecture holds for every class of graphs of bounded VC-dimension. In joint work with Alex Scott and Paul Seymour, we resolved this question in the affirmative. The talk will introduce the Erdős–Hajnal conjecture and discuss some ideas behind the proof of the bounded VC-dimension case.