Publications

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 …
Preprint
Neural Coordination and Capacity Control for Inventory Management
Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models
Self-improvement is a mechanism in Large Language Model (LLM) pre-training, post-training and test-time inference. We explore a …
Preprint
Mind the Gap: Examining the Self-Improvement Capabilities of Large Language 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 …
Preprint
Learning an Inventory Control Policy with General Inventory Arrival Dynamics
Deep Inventory Management
This work provides a Deep Reinforcement Learning approach to solving a periodic review inventory control system with stochastic vendor …
Preprint
Deep Inventory Management
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 PDF
MQRetNN: Multi-Horizon Time Series Forecasting with Retrieval Augmentation
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
High Dimensional Inference for Cluster-Based Graphical Models
Motivated by modern applications in which one constructs graphical models based on a very large number of features, this paper …
Preprint PDF Project
High Dimensional Inference for Cluster-Based Graphical Models
Efficient, Certifiably Optimal Clustering with Applications to Latent Variable Graphical Models
We consider SDP relaxation methods for data and variable clustering problems, which have been shown in the literature to have good …
Preprint PDF Project Project
Efficient, Certifiably Optimal Clustering with Applications to Latent Variable Graphical Models
Marginal Policy Gradients: A Unified Family of Estimators for Bounded Action Spaces with Applications
Many complex domains, such as robotics control and real-time strategy (RTS) games, require an agent to learn a continuous control. In …
Preprint Code Project
Marginal Policy Gradients: A Unified Family of Estimators for Bounded Action Spaces with Applications
Natural Policy Gradient for Exponential Families
Recent work has highlighted how a misalignment between the support of the policy and the action space of the reinforcement learning …
PDF Code Project
Nonparametrically Learning Activation Functions in Deep Neural Nets
We provide a principled framework for non-parametrically learning activation functions in deep neural networks. Currently, …
PDF Project
Nonparametrically Learning Activation Functions in Deep Neural Nets