arXiv:2509.03898v2 Announce Type: replace Abstract: In this study we develop dimension-reduction techniques to accelerate diffusion model inference in the context of synthetic data generation. The idea is to integrate compressed sensing into diffusion models (hence, CSDM): First, compress the dataset…
arXiv:2509.23544v1 Announce Type: new Abstract: Many modern applications involve predicting structured, non-Euclidean outputs such as probability distributions, networks, and symmetric positive-definite matrices. These outputs are naturally modeled as elements of general metric spaces, where classical regression techniques that rely on…
arXiv:2409.15252v3 Announce Type: replace-cross Abstract: We characterize the squared prediction risk of ensemble estimators obtained through subagging (subsample bootstrap aggregating) regularized M-estimators and construct a consistent estimator for the risk. Specifically, we consider a heterogeneous collection of $M ge 1$…
arXiv:2509.23800v1 Announce Type: new Abstract: Modern generative AI models such as diffusion and flow matching can sample from rich data distributions, but many downstream tasks — such as experimental design or creative content generation — require a higher level of…
arXiv:2501.19401v4 Announce Type: replace-cross Abstract: We introduce a practical, black-box framework termed Detection Augmented Learning (DAL) for the problem of non-stationary bandits without prior knowledge of the underlying non-stationarity. DAL accepts any stationary bandit algorithm as input and augments it…
arXiv:2509.24095v1 Announce Type: new Abstract: Conformal prediction can be used to construct prediction sets that cover the true outcome with a desired probability, but can sometimes lead to large prediction sets that are costly in practice. The most useful outcome…
arXiv:2509.24293v1 Announce Type: new Abstract: Estimating causal quantities (CQs) typically requires large datasets, which can be expensive to obtain, especially when measuring individual outcomes is costly. This challenge highlights the importance of sample-efficient active learning strategies. To address the narrow…
arXiv:2506.04695v2 Announce Type: replace-cross Abstract: While reinforcement learning (RL) demonstrated remarkable success in enhancing the reasoning capabilities of language models, the training dynamics of RL in LLMs remain unclear. In this work, we provide an explanation of the RL training…
arXiv:2509.24312v1 Announce Type: new Abstract: Representation learning is a key technique in modern machine learning that enables models to identify meaningful patterns in complex data. However, different methods tend to extract distinct aspects of the data, and relying on a…
arXiv:2509.24493v1 Announce Type: new Abstract: Preference-based data often appear complex and noisy but may conceal underlying homogeneous structures. This paper introduces a novel framework of ranking structure recognition for preference-based data. We first develop an approach to identify dynamic ranking…
