XGBoost: A Scalable Tree Boosting System

9 Mar 201616 code implementations

In this paper, we describe a scalable end-to-end tree boosting system called XGBoost, which is used widely by data scientists to achieve state-of-the-art results on many machine learning challenges.

DIMENSIONALITY REDUCTION REGRESSION

Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Models

14 May 20191 code implementation

We introduce an off-policy evaluation procedure for highlighting episodes where applying a reinforcement learned (RL) policy is likely to have produced a substantially different outcome than the observed policy.

MRFusion: A Deep Learning architecture to fuse PAN and MS imagery for land cover mapping

29 Jun 20181 code implementation

Common techniques to produce land cover maps from such VHSR images typically opt for a prior pansharpening of the multi-resolution source for a full resolution processing.

City-wide Analysis of Electronic Health Records Reveals Gender and Age Biases in the Administration of Known Drug-Drug Interactions

9 Mar 20181 code implementation

The occurrence of drug-drug-interactions (DDI) from multiple drug dispensations is a serious problem, both for individuals and health-care systems, since patients with complications due to DDI are likely to re-enter the system at a costlier level.

Metaheuristics in Flood Disaster Management and Risk Assessment

26 Jun 2013no code implementations

A conceptual area is divided into units or barangays, each was allowed to evolve under a physical constraint.

Risk Management via Anomaly Circumvent: Mnemonic Deep Learning for Midterm Stock Prediction

3 Aug 2019no code implementations

In this paper, we propose a novel deep neural network Mid-LSTM for midterm stock prediction, which incorporates the market trend as hidden states.

REGRESSION STOCK PREDICTION STOCK PRICE PREDICTION

Automatic Financial Trading Agent for Low-risk Portfolio Management using Deep Reinforcement Learning

7 Sep 2019no code implementations

The two primary goals of the portfolio management problem are maximizing profit and restrainting risk.

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