Search Results for author:
Found 9 papers, 3 papers with code
A shared-revenue Bertrand game
We introduce and analyze a variation of the Bertrand game in which the revenue is shared between two players.
Using Pre-trained LLMs for Multivariate Time Series Forecasting
Pre-trained Large Language Models (LLMs) encapsulate large amounts of knowledge and take enormous amounts of compute to train.
Neural Coordination and Capacity Control for Inventory Management
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.
Chronos: Learning the Language of Time Series
We introduce Chronos, a simple yet effective framework for pretrained probabilistic time series models.
Learning an Inventory Control Policy with General Inventory Arrival Dynamics
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).
Scaling Laws for Imitation Learning in Single-Agent Games
Inspired by recent work in Natural Language Processing (NLP) where "scaling up" has resulted in increasingly more capable LLMs, we investigate whether carefully scaling up model and data size can bring similar improvements in the imitation learning setting for single-agent games.
Deep Inventory Management
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.
Deep Generative Quantile-Copula Models for Probabilistic Forecasting
We introduce a new category of multivariate conditional generative models and demonstrate its performance and versatility in probabilistic time series forecasting and simulation.
Probabilistic Time Series Forecasting
quantile regression
+1
A Multi-Horizon Quantile Recurrent Forecaster
We propose a framework for general probabilistic multi-step time series regression.
