169 citations · 243 across the 10 of their papers we have counts for
11 papers
Deep Inventory Management
Dhruv Madeka, Kari Torkkola, Carson Eisenach +3
This work provides a Deep Reinforcement Learning approach to solving a periodic review inventory control system with stochastic vendor lead times, lost sales, correlated demand, an…
Linear Reinforcement Learning with Ball Structure Action Space
Zeyu Jia, Randy Jia, Dhruv Madeka +1
We study the problem of Reinforcement Learning (RL) with linear function approximation, i.e. assuming the optimal action-value function is linear in a known -dimensional feature…
Forecast Hedging and Calibration
Dean P. Foster, Sergiu Hart
Calibration means that forecasts and average realized frequencies are close. We develop the concept of forecast hedging, which consists of choosing the forecasts so as to guarantee…
Smooth Calibration, Leaky Forecasts, Finite Recall, and Nash Dynamics
Dean P. Foster, Sergiu Hart
We propose to smooth out the calibration score, which measures how good a forecaster is, by combining nearby forecasts. While regular calibration can be guaranteed only by randomiz…
On Submodular Contextual Bandits
Dean P. Foster, Alexander Rakhlin
We consider the problem of contextual bandits where actions are subsets of a ground set and mean rewards are modeled by an unknown monotone submodular function that belongs to a cl…
Variable Selection is Hard
Dean Foster, Howard Karloff, Justin Thaler
Variable selection for sparse linear regression is the problem of finding, given an m x p matrix B and a target vector y, a sparse vector x such that Bx approximately equals y. Ass…