254 citations · 320 across the 17 of their papers we have counts for
4 papers · 1 filter
A Study on the Calibration of In-context Learning
Hanlin Zhang, Yi-Fan Zhang, Yaodong Yu +5
Accurate uncertainty quantification is crucial for the safe deployment of machine learning models, and prior research has demonstrated improvements in the calibration of modern lan…
Contextual Bandits for Evaluating and Improving Inventory Control Policies
Dean Foster, Randy Jia, Dhruv Madeka
Solutions to address the periodic review inventory control problem with nonstationary random demand, lost sales, and stochastic vendor lead times typically involve making strong as…
Learning an Inventory Control Policy with General Inventory Arrival Dynamics
Sohrab Andaz, Carson Eisenach, Dhruv Madeka +4
In this paper we address the problem of learning and backtesting inventory control policies in the presence of general arrival dynamics -- which we term as a quantity-over-time arr…
Scaling Laws for Imitation Learning in Single-Agent Games
Jens Tuyls, Dhruv Madeka, Kari Torkkola +3
Imitation Learning (IL) is one of the most widely used methods in machine learning. Yet, many works find it is often unable to fully recover the underlying expert behavior, even in…