7 papers
Mind the Residual Gap: Probabilistic Downscaling under Real-World Bias
Yujin Kim, Nidhi Soma, Sarah Dean
Probabilistic downscaling is the task of modeling the conditional distribution of high-resolution fields given coarse inputs, and is a central challenge to atmospheric science, cli…
Pre-trained Large Language Models Learn Hidden Markov Models In-context
Yijia Dai, Zhaolin Gao, Yahya Sattar +2
Hidden Markov Models (HMMs) are foundational tools for modeling sequential data with latent Markovian structure, yet fitting them to real-world data remains computationally challen…
Do LLMs Favor LLMs? Quantifying Interaction Effects in Peer Review
Vibhhu Sharma, Thorsten Joachims, Sarah Dean
There are increasing indications that LLMs are not only used for producing scientific papers, but also as part of the peer review process. In this work, we provide the first compre…
Sparse-to-Field Reconstruction via Stochastic Neural Dynamic Mode Decomposition
Yujin Kim, Sarah Dean
Many consequential real-world systems, like wind fields and ocean currents, are dynamic and hard to model. Learning their governing dynamics remains a central challenge in scientif…
High-Altitude Balloon Station-Keeping with First Order Model Predictive Control
Myles Pasetsky, Jiawei Lin, Bradley Guo +1
High-altitude balloons (HABs) are common in scientific research due to their wide range of applications and low cost. Because of their nonlinear, underactuated dynamics and the par…
Policy Design for Two-sided Platforms with Participation Dynamics
Haruka Kiyohara, Fan Yao, Sarah Dean
In two-sided platforms (e.g., video streaming or e-commerce), viewers and providers engage in interactive dynamics: viewers benefit from increases in provider populations, while pr…