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Found 7 papers, 0 papers with code
Deep Reinforcement Learning for Picker Routing Problem in Warehousing
A key advantage of our proposed method is its ability to offer an option to reduce the perceived complexity of routes.
SALSA: Sequential Approximate Leverage-Score Algorithm with Application in Analyzing Big Time Series Data
We demonstrate that, with high probability, the accuracy of SALSA's approximations is within $(1 + O({\varepsilon}))$ of the true leverage scores.
A Hybrid Statistical-Machine Learning Approach for Analysing Online Customer Behavior: An Empirical Study
While most mere machine learning methods are plagued by the lack of interpretability in practice, our novel hybrid approach will address this practical issue by generating explainable output.
Toeplitz Least Squares Problems, Fast Algorithms and Big Data
In time series analysis, when fitting an autoregressive model, one must solve a Toeplitz ordinary least squares problem numerous times to find an appropriate model, which can severely affect computational times with large data sets.
Average-reward model-free reinforcement learning: a systematic review and literature mapping
Reinforcement learning is important part of artificial intelligence.
LSAR: Efficient Leverage Score Sampling Algorithm for the Analysis of Big Time Series Data
We first develop a new fast algorithm to estimate the leverage scores of an autoregressive (AR) model in big data regimes.
Learning to Project in Multi-Objective Binary Linear Programming
We also present several generic features/variables that can be used in machine learning techniques for identifying the best projected space.
