Zobrazeno 1 - 10
of 27
pro vyhledávání: '"Madeka, Dhruv"'
This paper addresses the capacitated periodic review inventory control problem, focusing on a retailer managing multiple products with limited shared resources, such as storage or inbound labor at a facility. Specifically, this paper is motivated by
Externí odkaz:
http://arxiv.org/abs/2410.02817
Autor:
Zhang, Hanlin, Zhang, Yi-Fan, Yu, Yaodong, Madeka, Dhruv, Foster, Dean, Xing, Eric, Lakkaraju, Himabindu, Kakade, Sham
Accurate uncertainty quantification is crucial for the safe deployment of machine learning models, and prior research has demonstrated improvements in the calibration of modern language models (LMs). We study in-context learning (ICL), a prevalent me
Externí odkaz:
http://arxiv.org/abs/2312.04021
Autor:
Andaz, Sohrab, Eisenach, Carson, Madeka, Dhruv, Torkkola, Kari, Jia, Randy, Foster, Dean, Kakade, Sham
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 arrivals model (QOT). We also allow for order quantities to be modified a
Externí odkaz:
http://arxiv.org/abs/2310.17168
Solutions to address the periodic review inventory control problem with nonstationary random demand, lost sales, and stochastic vendor lead times typically involve making strong assumptions on the dynamics for either approximation or simulation, and
Externí odkaz:
http://arxiv.org/abs/2310.16096
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 constrained environments like single-agent games. However, none of th
Externí odkaz:
http://arxiv.org/abs/2307.09423
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 $d$-dimensional feature mapping. Unfortunately, however, based on only this assumption, the w
Externí odkaz:
http://arxiv.org/abs/2211.07419
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, and price matching. While this dynamic program has historically been con
Externí odkaz:
http://arxiv.org/abs/2210.03137
Multi-horizon probabilistic time series forecasting has wide applicability to real-world tasks such as demand forecasting. Recent work in neural time-series forecasting mainly focus on the use of Seq2Seq architectures. For example, MQTransformer - an
Externí odkaz:
http://arxiv.org/abs/2207.10517
The current paper studies sample-efficient Reinforcement Learning (RL) in settings where only the optimal value function is assumed to be linearly-realizable. It has recently been understood that, even under this seemingly strong assumption and acces
Externí odkaz:
http://arxiv.org/abs/2207.08342
Autor:
Tripuraneni, Nilesh, Madeka, Dhruv, Foster, Dean, Perrault-Joncas, Dominique, Jordan, Michael I.
A central obstacle in the objective assessment of treatment effect (TE) estimators in randomized control trials (RCTs) is the lack of ground truth (or validation set) to test their performance. In this paper, we propose a novel cross-validation-like
Externí odkaz:
http://arxiv.org/abs/2112.07602