6 accepted papers
Predictive modeling in high-stakes domains often suffers from limited observed features due to ethical and practical constraints. To address this challenge, we propose a novel approach that formulates latent feature mining as a text-to-text propositional logic reasoning task, facilitating domain kno
Fairness is a growing concern in machine learning as state-of-the-art models may amplify social prejudice by making biased predictions against specific demographics such as race and gender. Such discrimination raises issues in various fields such as employment, criminal justice, and trust score eval…
Incarceration-diversion programs have proven effective in reducing recidivism. Accurate prediction of the number of individuals with different characteristics in the program and their program outcomes based on given eligibility criteria is crucial for successful implementation, because this predicti…
Time-series generation has crucial practical significance for decision-making under uncertainty. Existing methods have various limitations like accumulating errors over time, significantly impacting downstream tasks. We develop a novel generation method, DT-VAE, that incorporates generalizable domai…
The fairness in machine learning is getting increasing attention, as its applications in different fields continue to expand and diversify. To mitigate the discriminated model behaviors between different demographic groups, we introduce a novel post-processing method to optimize over multiple fairne…
This paper considers stochastic linear bandits with general nonlinear constraints. The objective is to maximize the expected cumulative reward over horizon $T$ subject to a set of constraints in each round $\tau\leq T$. We propose a pessimistic-optimistic algorithm for this problem, which is efficie…