Pinned Repositories
bmdal_reg
Deep Batch Active Learning for Regression
dholzmueller.github.io
Website of David Holzmüller
fast_sparse_interpolation
Code accompanying the paper "Fast Sparse Grid Operations using the Unidirectional Principle: A Generalized and Unified Framework"
LAMDA-TALENT
A comprehensive toolkit and benchmark for tabular data learning, featuring over 20 deep methods, more than 10 classical methods, and 300 diverse tabular datasets.
nn_inconsistency
Code for Monte Carlo analysis of two-layer ReLU neural networks.
pytabkit
ML models + benchmark for tabular data classification and regression
realmlp-td-s_standalone
Standalone implementation of RealMLP-TD-S and its data preprocessing for tabular data classification and regression
sampling_experiments
Code for reproducing the plots in our paper "Convergence Rates for Non-Log-Concave Sampling and Log-Partition Estimation"
sfcpp
A library with space-filling curve algorithms (analysis, neighbor-finding, visualization) and other utilities (math, geometry, image processing, LaTeX generation...)
universal_double_descent
Code to replicate figures and results in my paper "On the Universality of the Double Descent Peak in Ridgeless Regression"
dholzmueller's Repositories
dholzmueller/bmdal_reg
Deep Batch Active Learning for Regression
dholzmueller/sfcpp
A library with space-filling curve algorithms (analysis, neighbor-finding, visualization) and other utilities (math, geometry, image processing, LaTeX generation...)
dholzmueller/pytabkit
ML models + benchmark for tabular data classification and regression
dholzmueller/universal_double_descent
Code to replicate figures and results in my paper "On the Universality of the Double Descent Peak in Ridgeless Regression"
dholzmueller/nn_inconsistency
Code for Monte Carlo analysis of two-layer ReLU neural networks.
dholzmueller/realmlp-td-s_standalone
Standalone implementation of RealMLP-TD-S and its data preprocessing for tabular data classification and regression
dholzmueller/dholzmueller.github.io
Website of David Holzmüller
dholzmueller/fast_sparse_interpolation
Code accompanying the paper "Fast Sparse Grid Operations using the Unidirectional Principle: A Generalized and Unified Framework"
dholzmueller/LAMDA-TALENT
A comprehensive toolkit and benchmark for tabular data learning, featuring over 20 deep methods, more than 10 classical methods, and 300 diverse tabular datasets.
dholzmueller/sampling_experiments
Code for reproducing the plots in our paper "Convergence Rates for Non-Log-Concave Sampling and Log-Partition Estimation"
dholzmueller/tensorforce
TensorForce: A TensorFlow library for applied reinforcement learning