368 citations · 736 across the 32 of their papers we have counts for
34 papers
LyapuFlow: Controlling Generative Flows with Lyapunov Feedback for Inverse Problems
Minseon Gwak, Hans Hao-Hsun Hsu, Danielle C. Maddix +1
Pretrained flow models are now widely used as generative priors in science and vision, where inference-time guidance enables test-time constraints without retraining. Existing meth…
Threshold-Aware Conformal Routing
Shiwei Tan, Huzefa Rangwala, Danielle C. Maddix
High-fidelity simulations are essential to scientific and engineering design, but can be expensive to run repeatedly. Learned surrogates offer a faster alternative, yet their highe…
Mitra-v2 Technical Report
Yefan Tao, Xiyuan Zhang, Xinyi Liu +13
We introduce Mitra-v2, a tabular foundation model that delivers state-of-the-art performance on real-world classification and regression problems, from credit-risk scoring and clin…
SenTSR-Bench: Thinking with Injected Knowledge for Time-Series Reasoning
Zelin He, Boran Han, Xiyuan Zhang +10
Time-series diagnostic reasoning is essential for many applications, yet existing solutions face a persistent gap: general reasoning large language models (GRLMs) possess strong re…
Mitra: Mixed Synthetic Priors for Enhancing Tabular Foundation Models
Xiyuan Zhang, Danielle C. Maddix, Junming Yin +11
Since the seminal work of TabPFN, research on tabular foundation models (TFMs) based on in-context learning (ICL) has challenged long-standing paradigms in machine learning. Withou…
Understanding the Implicit Biases of Design Choices for Time Series Foundation Models
Annan Yu, Danielle C. Maddix, Boran Han +7
Time series foundation models (TSFMs) are a class of potentially powerful, general-purpose tools for time series forecasting and related temporal tasks, but their behavior is stron…