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Found 10 papers, 3 papers with code
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.
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.
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).
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.
MQRetNN: Multi-Horizon Time Series Forecasting with Retrieval Augmentation
For example, MQTransformer - an improvement of MQCNN - has shown the state-of-the-art performance in probabilistic demand forecasting.
MQTransformer: Multi-Horizon Forecasts with Context Dependent and Feedback-Aware Attention
In this work, we propose novel improvements to the current state of the art by incorporating changes inspired by recent advances in Transformer architectures for Natural Language Processing.
Marginal Policy Gradients: A Unified Family of Estimators for Bounded Action Spaces with Applications
In the former, an agent learns a policy over $\mathbb{R}^d$ and in the latter, over a discrete set of actions each of which is parametrized by a continuous parameter.
High-Dimensional Inference for Cluster-Based Graphical Models
We employ model assisted clustering, in which the clusters contain features that are similar to the same unobserved latent variable.
Efficient, Certifiably Optimal Clustering with Applications to Latent Variable Graphical Models
Compared to the naive interior point method, our method reduces the computational complexity of solving the SDP from $\tilde{O}(d^7\log\epsilon^{-1})$ to $\tilde{O}(d^{6}K^{-2}\epsilon^{-1})$ arithmetic operations for an $\epsilon$-optimal solution.
PARAMETRIZED DEEP Q-NETWORKS LEARNING: PLAYING ONLINE BATTLE ARENA WITH DISCRETE-CONTINUOUS HYBRID ACTION SPACE
Most existing deep reinforcement learning (DRL) frameworks consider action spaces that are either discrete or continuous space.
