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Conformalized Quantile Regression
Conformal prediction is a technique for constructing prediction intervals that attain valid coverage in finite samples, without making distributional assumptions.
Sequential estimation of quantiles with applications to A/B-testing and best-arm identification
We propose confidence sequences -- sequences of confidence intervals which are valid uniformly over time -- for quantiles of any distribution over a complete, fully-ordered set, based on a stream of i. i. d.
DoWhy: An End-to-End Library for Causal Inference
In addition to efficient statistical estimators of a treatment's effect, successful application of causal inference requires specifying assumptions about the mechanisms underlying observed data and testing whether they are valid, and to what extent.
DoubleML -- An Object-Oriented Implementation of Double Machine Learning in R
This paper serves as an introduction to the double machine learning framework and the R package DoubleML.
Crystal Diffusion Variational Autoencoder for Periodic Material Generation
Generating the periodic structure of stable materials is a long-standing challenge for the material design community.
A framework for benchmarking clustering algorithms
The evaluation of clustering algorithms can involve running them on a variety of benchmark problems, and comparing their outputs to the reference, ground-truth groupings provided by experts.
Large Language Models Are State-of-the-Art Evaluators of Translation Quality
We describe GEMBA, a GPT-based metric for assessment of translation quality, which works both with a reference translation and without.
Grammar Variational Autoencoder
mkusner/grammarVAE
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ICML 2017
Crucially, state-of-the-art methods often produce outputs that are not valid.
A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network
daiquocnguyen/ConvKB
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NAACL 2018
This 3-column matrix is then fed to a convolution layer where multiple filters are operated on the matrix to generate different feature maps.
An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls
We introduce new inference procedures for counterfactual and synthetic control methods for policy evaluation.
