Fall 2026 – Page 2

SILO: Distribution Transport with Identifiability: A Signal Processing Perspective on Multimodal Generative AI

Abstract A central objective of modern generative AI is to enable learning and inference across multiple modalities, supporting tasks such as cross-modal generation, multimodal translation, domain transfer, and data fusion. Despite impressive empirical progress, many multimodal AI frameworks still lack solid theoretical foundations, raising concerns about reliability and robustness. In …

SILO: Smoothed Analysis of Learning from Positive Samples

Abstract Binary classification from positive-only samples is a variant of PAC learning where the learner receives i.i.d. positive samples and aims to learn a classifier with low error. Previous work by Natarajan, Gereb-Graus, and Shvaytser characterized learnability and revealed a largely negative picture: almost no interesting classes are learnable. In …

SILO: Dynamic Assortment Optimization: Fluid Relaxations, Submodularity, and Approximation Algorithms

Abstract Assortment and inventory decisions lie at the core of supply chain and retail operations. In many settings, these decisions are complicated by customer substitution: when a preferred product is unavailable, customers may switch to other products, and as inventory is depleted over time, the demand faced by the remaining …

SILO: Uncertainty Quantification for Foundation Models under Data and Compute Constraints

Abstract Foundation models are increasingly used in settings where labeled data are scarce and adaptation must be fast and lightweight. But scarce data must often do double duty: we need labeled examples both to adapt a foundation model to a new task and to evaluate how much we can trust …