XGBoost: A Scalable Tree Boosting System

9 Mar 201618 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.


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 reenter the system at a costlier level.

Exploring Algorithmic Fairness in Robust Graph Covering Problems

NeurIPS 2019 1 code implementation

Motivated by real-world deployment of AI driven, social-network based suicide prevention and landslide risk management interventions, this paper focuses on a robust graph covering problem subject to group fairness constraints.

Reinforcement Learning for Market Making in a Multi-agent Dealer Market

14 Nov 20191 code implementation

Market makers play an important role in providing liquidity to markets by continuously quoting prices at which they are willing to buy and sell, and managing inventory risk.

Quasi-Monte Carlo for multivariate distributions via generative neural networks

1 Nov 20181 code implementation

So far, QRNGs for multivariate distributions required a careful design, exploiting specific properties (such as conditional distributions) of the implied copula or the underlying quasi-Monte Carlo (QMC) point set, and were only tractable for a small number of models.

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.