12 papers with code • 1 benchmarks • 1 datasets

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Most implemented papers

Convex Total Least Squares

SlavovLab/STLS 1 Jun 2014

The special case when all dependent and independent variables have the same level of uncorrelated Gaussian noise, known as ordinary TLS, can be solved by singular value decomposition (SVD).

Spike-based causal inference for weight alignment

nasiryahm/STDWI ICLR 2020

Here we show how the discontinuity introduced in a spiking system can lead to a solution to this problem.

A Locally Adaptive Interpretable Regression

lhagiimn/A-Locally-Adaptive-Regression 7 May 2020

Therefore, LoAIR is a step towards bridging the gap between econometrics, statistics, and machine learning by improving the predictive ability of linear regression without depreciating its interpretability.

On a computationally-scalable sparse formulation of the multidimensional and non-stationary maximum entropy principle

Ganna85/TV-Entropy 7 May 2020

Data-driven modelling and computational predictions based on maximum entropy principle (MaxEnt-principle) aim at finding as-simple-as-possible - but not simpler then necessary - models that allow to avoid the data overfitting problem.

Instrument variable detection with graph learning : an application to high dimensional GIS-census data for house pricing

isaac2math/solar_graph_learning 30 Jul 2020

In this paper, we merge two well-known tools from machine learning and biostatistics---variable selection algorithms and probablistic graphs---to estimate house prices and the corresponding causal structure using 2010 data on Sydney.

Estimating Structural Target Functions using Machine Learning and Influence Functions

AliciaCurth/IF-learn 14 Aug 2020

Within this framework, we propose two general learning algorithms that build on the idea of nonparametric plug-in bias removal via IFs: the 'IF-learner' which uses pseudo-outcomes motivated by uncentered IFs for regression in large samples and outputs entire target functions without confidence bands, and the 'Group-IF-learner', which outputs only approximations to a function but can give confidence estimates if sufficient information on coarsening mechanisms is available.

Tail-risk protection: Machine Learning meets modern Econometrics

quantlet/mlvsgarch 7 Oct 2020

Tail risk protection is in the focus of the financial industry and requires solid mathematical and statistical tools, especially when a trading strategy is derived.

Standing on the Shoulders of Machine Learning: Can We Improve Hypothesis Testing?

DataScienceForPublicPolicy/hypML 2 Mar 2021

In this paper we have updated the hypothesis testing framework by drawing upon modern computational power and classification models from machine learning.

Deep Learning Macroeconomics

rrsguim/phd_economics 31 Jan 2022

We explore the proposed strategy empirically, showing that data from different but related domains, a type of transfer learning, helps identify the business cycle phases when there is no business cycle dating committee and to quick estimate a economic-based output gap.

Causal Imitation Learning under Temporally Correlated Noise

gkswamy98/causal_il 2 Feb 2022

We develop algorithms for imitation learning from policy data that was corrupted by temporally correlated noise in expert actions.