This paper studies a dynamic assortment problem on a two-sided service platform with incomplete information and heterogeneous customers, and develops a data-driven algorithm to learn parameters and op…

This paper investigates the performance of BEACON, a state-of-the-art method for low-resource, domain-aware Entity Matching, under varying algorithmic choices and data availability conditions.

The paper introduces Entropic Projection Alignment (EPA), a unified framework that estimates, explains, and improves model performance under distribution shift by aligning source and target distributi…

FedIDM introduces a novel federated learning framework that uses iterative distribution matching to achieve fast and stable convergence and maintain high model utility even when facing a large proport…

This paper proposes a method to improve few-step generation in diffusion models by introducing a marginal-alignment regularizer.

This paper studies distribution testing with respect to bounded classes of distinguishers, revealing connections between testable learning, verification of learning algorithms, and testing of structur…

This paper proves that no single-pass semi-streaming algorithm can achieve a better-than-half approximation to the maximum matching problem, implying the optimality of the naive greedy algorithm.

This paper improves the theoretical bounds for estimating discrete probability distributions using the $\ell_\infty$ norm, resolving several open questions in the field.

This paper establishes a connection between neural network approximation of score functions and approximation of probability distributions generated by reverse diffusion models.

This paper introduces survey sampling techniques to estimate or minimize empirical pairwise loss functions, showing that targeting informative pairs significantly reduces computational cost while main…

The paper proposes a novel two-stage framework to differentially privatize tables of counts by focusing on preserving the accuracy of the underlying count distribution, introducing the specialized cyc…

This paper presents a simpler variation of the NC detection criterion for perfect matchings in bipartite graphs with improved parameters.

The paper proposes Fair Fine-tuning (FFt), a method that fine-tunes a model using an Equalized Odds constraint on a complementary distribution, and theoretically proves that this approach significantl…

The paper proposes Fair Fine-tuning (FFt), a method that fine-tunes a model using an Equalized Odds constraint on a complementary distribution, and provides a formal theoretical bound linking this fai…

This paper introduces Distributional Random Forests, which replace mean-based CART splitting with criteria that compare full conditional response distributions in candidate children. The authors syste…

The paper develops a new framework for proving lower bounds for the maximum matching problem in the semi-streaming model, improving upon the previous best known bounds.