Algorithms and Theory

Inferring causal effects of a treatment, intervention or policy from observational data is central to many applications. However, state-of-the-art methods for causal inference seldom consider the possibility that covariates have missing values, which is ubiquitous in many real-world analyses. Missing data greatly complicate causal inference procedures as they require an adapted unconfoundedness...

In semi-supervised classification, one is given access both to labeled and unlabeled data. As unlabeled data is typically cheaper to acquire than labeled data, this setup becomes advantageous as soon as one can exploit the unlabeled data in order to produce a better classifier than with labeled data alone. However, the conditions under which such an improvement is possible are not fully...

In modern machine learning tasks, the presence of categorical features with extremely large vocabularies is a reality. This becomes a bottleneck when using an ML model, which generally grows at least linearly with the vocabulary size, affecting the memory, training and inference costs, as well as overfitting risk. In this work, we seek to compress the vocabulary by maximizing the mutual...

Suppose a customer is faced with a sequence of fluctuating prices, such as for airfare or a product sold by a large online retailer. Given distributional information about what price they might face each day, how should they choose when to purchase in order to maximize the likelihood of getting the best price in retrospect? This is related to the classical secretary problem, but with values...