2 code implementations • 12 Mar 2024 • Abdul Fatir Ansari, Lorenzo Stella, Caner Turkmen, Xiyuan Zhang, Pedro Mercado, Huibin Shen, Oleksandr Shchur, Syama Sundar Rangapuram, Sebastian Pineda Arango, Shubham Kapoor, Jasper Zschiegner, Danielle C. Maddix, Michael W. Mahoney, Kari Torkkola, Andrew Gordon Wilson, Michael Bohlke-Schneider, Yuyang Wang
We introduce Chronos, a simple yet effective framework for pretrained probabilistic time series models.
no code implementations • 26 Oct 2023 • Sohrab Andaz, Carson Eisenach, Dhruv Madeka, Kari Torkkola, Randy Jia, Dean Foster, Sham Kakade
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).
no code implementations • 18 Jul 2023 • Jens Tuyls, Dhruv Madeka, Kari Torkkola, Dean Foster, Karthik Narasimhan, Sham Kakade
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.
no code implementations • 6 Oct 2022 • Dhruv Madeka, Kari Torkkola, Carson Eisenach, Anna Luo, Dean P. Foster, Sham M. Kakade
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.
no code implementations • 24 Jul 2019 • Ruofeng Wen, Kari Torkkola
We introduce a new category of multivariate conditional generative models and demonstrate its performance and versatility in probabilistic time series forecasting and simulation.
5 code implementations • 29 Nov 2017 • Ruofeng Wen, Kari Torkkola, Balakrishnan Narayanaswamy, Dhruv Madeka
We propose a framework for general probabilistic multi-step time series regression.