Publications

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 …
Preprint Cite
Contextual Bandits for Evaluating and Improving Inventory Control Policies
Solutions to address the periodic review inventory control problem with nonstationary random demand, lost sales, and stochastic vendor …
Preprint Cite
Scaling Laws for Imitation Learning in NetHack
Imitation Learning (IL) is one of the most widely used methods in machine learning. Yet, while powerful, many works find it is often …
Preprint Cite
Linear Reinforcement Learning with Ball Structure Action Space
We study the problem of Reinforcement Learning (RL) with linear function approximation, i.e. assuming the optimal action-value function …
Preprint PDF Cite
A few expert queries suffices for sample-efficient rl with resets and linear value approximation
Preprint PDF Cite
MQRetNN: Multi-Horizon Time Series Forecasting with Retrieval Augmentation
Multi-horizon probabilistic time series forecasting has wide applicability to real-world tasks such as demand forecasting. Recent work …
Preprint Cite
Deep Inventory Management
This work provides a Deep Reinforcement Learning approach to solving a periodic review inventory control system with stochastic vendor …
Preprint Cite
A Framework for the Meta-Analysis of Randomized Experiments with Applications to Heavy-Tailed Response Data
A central obstacle in the objective assessment of treatment effect (TE) estimators in randomized control trials (RCTs) is the lack of …
Preprint Cite
Mqtransformer: Multi-horizon forecasts with context dependent and feedback-aware attention
Recent advances in neural forecasting have produced major improvements in accuracy for probabilistic demand prediction. In this work, …
Preprint PDF Cite
All roads lead to quantitative finance
PDF Cite Video
Sample path generation for probabilistic demand forecasting
The state of the art in probabilistic demand forecasting [40] minimizes Quantile Loss to predict the future demand quantiles for …
PDF Cite
Scatteract: Automated extraction of data from scatter plots
Charts are an excellent way to convey patterns and trends in data, but they do not facilitate further modeling of the data or close …
Preprint PDF Cite
Accurate prediction of electoral outcomes
We present novel methods for predicting the outcome of large elections. Our first algorithm uses a diffusion process to model the time …
Preprint Cite Slides
A multi-horizon quantile recurrent forecaster
We propose a framework for general probabilistic multi-step time series regression. Specifically, we exploit the expressiveness and …
Preprint Cite
Estimating Covariance Matrices for Investments Whose Histories Differ in Length
Preprint Cite