Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates.
Alternating optimization of decision trees, with application to learning sparse oblique trees
Authors: Miguel A. Carreira-Perpinan, Pooya Tavallali
NeurIPS 2018 | Venue PDF | LLM Run Details | Input Tokens: 23,438 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,603 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments show it consistently outperforms various other algorithms while being highly scalable to large datasets and trees. ... We constructed paths using initial CART trees of depths 4 to 12 (both axis-aligned and oblique) on the MNIST dataset, splitting its training set of 60k points into 48k training and 12k validation (to determine an optimal C or depth), and reporting generalization error on its 10k test points. |
| Researcher Affiliation | Academia | Miguel A. Carreira-Perpi n an Dept. EECS, University of California, Merced EMAIL Pooya Tavallali Dept. EECS, University of California, Merced EMAIL |
| Pseudocode | Yes | TAO pseudocode appears in the supplementary material. |
| Open Source Code | No | The paper states, "The node optimization uses an ℓ1regularized linear SVM with slack hyperparameter C 0 ... implemented with LIBLINEAR [15]. The rest of our code is in Matlab." While it mentions using LIBLINEAR and Matlab, it does not explicitly state that the authors' own implementation code for TAO is open-source or publicly available. |
| Open Datasets | Yes | We constructed paths using initial CART trees of depths 4 to 12 (both axis-aligned and oblique) on the MNIST dataset... [27] MNIST. The MNIST database of handwritten digits. http://yann.lecun.com/exdb/mnist. |
| Dataset Splits | Yes | splitting its training set of 60k points into 48k training and 12k validation (to determine an optimal C or depth), and reporting generalization error on its 10k test points. |
| Hardware Specification | Yes | ran on a laptop with 2 core i5 CPUs and 12GB RAM |
| Software Dependencies | No | The node optimization uses an ℓ1regularized linear SVM with slack hyperparameter C 0 ... implemented with LIBLINEAR [15]. The rest of our code is in Matlab. The paper mentions LIBLINEAR and Matlab but does not provide specific version numbers for either. |
| Experiment Setup | Yes | The node optimization uses an ℓ1regularized linear SVM with slack hyperparameter C 0 (so the TAO sparsity hyperparameter in section 3 is λ = 1/C), implemented with LIBLINEAR [15]. ... We stop TAO when the training misclassification loss decreases but by less than 0.5%, or the number of iterations (passes over all nodes) reaches 14 (in practice TAO stops after around 7 iterations). ... We constructed paths using initial CART trees of depths 4 to 12 (both axis-aligned and oblique) on the MNIST dataset... |