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Publications
Q-learning for Quantile MDPs: A Decomposition, Performance, and Convergence Analysis
Causal representation learning (CRL) enhances machine learning models' robustness and generalizability by learning structural causal models … (voir plus)associated with data-generating processes. We focus on a family of CRL methods that uses contrastive data pairs in the observable space, generated before and after a random, unknown intervention, to identify the latent causal model. (Brehmer et al., 2022) showed that this is indeed possible, given that all latent variables can be intervened on individually. However, this is a highly restrictive assumption in many systems. In this work, we instead assume interventions on arbitrary subsets of latent variables, which is more realistic. We introduce a theoretical framework that calculates the degree to which we can identify a causal model, given a set of possible interventions, up to an abstraction that describes the system at a higher level of granularity.
2025-04-23
Proceedings of The 28th International Conference on Artificial Intelligence and Statistics (publié)
We study the generalization properties of randomly initialized neural networks, under the assumption that the network is larger than some un… (voir plus)known "teacher" network that achieves low risk. We extend the analysis of Buzaglo et al. (2024) to allow for student networks of arbitrary width and depth, and to the setting where no (small) teacher network perfectly interpolates the data. We obtain an oracle inequality, relating the risk of Gibbs posterior sampling to that of narrow teacher networks. As a result, the sample complexity is once again bounded in terms of the size of narrow teacher networks that themselves achieve small risk. We then introduce a new notion of data complexity, based on the minimal size of a teacher network required to achieve a certain level of excess risk. By comparing the scaling laws resulting from our bounds to those observed in empirical studies, we are able to estimate the data complexity of standard benchmarks according to our measure.
2025-04-23
Proceedings of The 28th International Conference on Artificial Intelligence and Statistics (publié)
BACKGROUND
The social stigma of families of children living with colostomies due to anorectal malformation (ARM) is significant in low-incom… (voir plus)e countries (LICs). Improved access to pediatric surgery has resulted in more 1-stage ARM procedures in Southwestern Uganda, avoiding colostomy creation, but the impact on social stigma experienced by families is unknown. We hypothesized that this change would decrease the social stigma experienced by families.
METHODS
A single-center mixed retrospective and prospective cohort study with combined qualitative data of families of children with ARM who underwent corrective surgery compared the stigma experienced by those with colostomies to those without. The Kilifi Stigma Scale of Epilepsy (KSSE) was used to assess social stigma. Multivariable regression analysis assessed differences in the stigma experienced, controlling for age at diagnosis, rurality, distance traveled, sex, and parental education. Subgroup analysis assessed the impact of colostomy duration on stigma, stratified over parental education.
RESULTS
Patient/family dyads with 238 ARM were included; 177 (74%) received a colostomy. Most patients were male (51%), lived in rural areas (71%), and had parents with primary school education (65%). For those without a colostomy, the median KSSE was 0 (Q1-Q3 0-0), compared to 11 (Q1-Q3 3-20) for colostomy. On multivariable analysis, after controlling for age at diagnosis, rurality, distance traveled, sex, and parental education attainment, families of patients with ARM who received a colostomy had a median KSSE score 7.8 points higher than those who did not receive a colostomy (coefficient 7.78, 95% 3.14-12.43, and p = 0.001). When the duration of colostomy (in years) was examined, the median KSSE score increased by 1.58 points for each additional year for a patient who had a colostomy (IRR 1.58, 95% CI: 0.76-2.40, and p 0.001).
CONCLUSION
Adopting a 1-stage ARM repair for the select types, which avoids colostomy creation, significantly reduces the exper
