tabular-regression
5 papers with code • 0 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in tabular-regression
SupplyGraph
Most implemented papers
A Framework and Benchmark for Deep Batch Active Learning for Regression
We provide open-source code that includes efficient implementations of all kernels, kernel transformations, and selection methods, and can be used for reproducing our results.
Probabilistic Calibration by Design for Neural Network Regression
vekteur/probabilistic-calibration-study
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To address the miscalibration issue of neural networks, various methods have been proposed to improve calibration, including post-hoc methods that adjust predictions after training and regularization methods that act during training.
Instance-Based Uncertainty Estimation for Gradient-Boosted Regression Trees
We also find that IBUG can achieve improved probabilistic performance by using different base GBRT models, and can more flexibly model the posterior distribution of a prediction than competing methods.
Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery
omron-sinicx/srsd-benchmark
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For each of the 120 SRSD datasets, we carefully review the properties of the formula and its variables to design reasonably realistic sampling ranges of values so that our new SRSD datasets can be used for evaluating the potential of SRSD such as whether or not an SR method can (re)discover physical laws from such datasets.
SRSD: Rethinking Datasets of Symbolic Regression for Scientific Discovery
omron-sinicx/srsd-benchmark
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NeurIPS 2022 AI for Science: Progress and Promises 2022
Symbolic Regression (SR) is a task of recovering mathematical expressions from given data and has been attracting attention from the research community to discuss its potential for scientific discovery.
