arXiv:2604.02474v1 Announce Type: new
Abstract: Dynamical systems describe how a physical system evolves over time. Physical processes can evolve faster or slower in different environmental conditions. We use time-warping as rescaling the time in a mo…
arXiv:2603.26227v2 Announce Type: replace-cross
Abstract: We study privacy-preserving sparse linear regression in the high-dimensional regime, focusing on the LASSO estimator. We analyze two widely used mechanisms for differential privacy: output pert…
arXiv:2604.02250v1 Announce Type: new
Abstract: Understanding causal dependencies in observational data is critical for informing decision-making. These relationships are often modeled as Bayesian Networks (BNs) and Directed Acyclic Graphs (DAGs). Exi…
arXiv:2601.11016v2 Announce Type: replace-cross
Abstract: In this paper, we introduce a framework for contextual distributionally robust optimization (DRO) that considers the causal and continuous structure of the underlying distribution by developing…
arXiv:2506.12553v2 Announce Type: replace
Abstract: Differential privacy (DP) is obtained by randomizing a data analysis algorithm, which necessarily introduces a tradeoff between its utility and privacy. Many DP mechanisms are built upon one of two u…
arXiv:2603.20359v2 Announce Type: replace-cross
Abstract: Machine learning has opened new frontiers in purely data-driven algorithms for data assimilation in, and for forecasting of, dynamical systems; the resulting methods are showing some promise. H…
arXiv:2603.02491v2 Announce Type: replace-cross
Abstract: As artificial agents become increasingly capable, what internal structure is *necessary* for an agent to act competently under uncertainty? Classical results show that optimal control can be *i…
arXiv:2508.14285v3 Announce Type: replace
Abstract: Fine-tuning large language models (LLMs) with low-rank adaptation (LoRA) is a cost-effective way to incorporate information from a specific dataset. However, when a problem requires incorporating inf…
arXiv:2604.02248v1 Announce Type: cross
Abstract: Multimodal time-to-event prediction often requires integrating sensitive data distributed across multiple parties, making centralized model training impractical due to privacy constraints. At the same …
arXiv:2405.03083v5 Announce Type: replace-cross
Abstract: Causal effects are often characterized with population summaries. These might provide an incomplete picture when there are heterogeneous treatment effects across subgroups. Since the subgroup s…