Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Trees with Attention for Set Prediction Tasks
Authors: Roy Hirsch, Ran Gilad-Bachrach
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the new method empirically on a wide range of problems ranging from making predictions on sub-atomic particle jets to estimating the redshift of galaxies. The new method outperforms existing tree-based methods consistently and significantly. Moreover, it is competitive and often outperforms Deep Learning. We also discuss the theoretical properties of Set-Trees and explain how they enable item-level explainability. |
| Researcher Affiliation | Academia | 1Department of EE, Tel-Aviv University, Israel 2Department of Bio-Medical Engineering, Tel-Aviv University, Israel and the Edmond J. Safra Center for Bioinformatics. Correspondence to: Roy Hirsch <EMAIL>, Ran Gilad-Bachrach <EMAIL>. |
| Pseudocode | No | The paper does not contain any clearly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | Yes | We release an implementation of our proposed method for the community and for reproducibility. The code is available at: https://github.com/TAU-MLwell/ Set-Tree. |
| Open Datasets | Yes | Data were generated using the MIMIC-III database (Johnson et al., 2016)... We used two popular jet classification datasets: Quark Gluon tagging (Komiske et al., 2019) and Top Tagging (Kasieczka et al., 2019)... We experimented with a multi-class point cloud classification task based on the Model Net40 dataset (Wu et al., 2015)... We used the poker hands dataset introduced in Cattral et al. (2002). |
| Dataset Splits | Yes | For all the tree-based models (GBT and GBe ST) and for all the tasks, we scanned a pre-defined hyperparameter search space. we used 10% of the training data as validation for the hyperparameters tuning... The training set consisted of 100K records... The data were split into 1.6M/200k/200k records for train/validation/test. The Top Tagging dataset included jets derived from hadronically decaying top quarks... The data were split into 1.2M/400K/400K for train/validation/test. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, memory, or specific computer specifications) used for running the experiments. |
| Software Dependencies | No | The paper mentions software like XGBoost, Catboost, Light GBM, and Adam optimizer but does not provide specific version numbers for these dependencies. |
| Experiment Setup | Yes | For all the tree-based models (GBT and GBe ST) and for all the tasks, we scanned a pre-defined hyperparameter search space... The trees maximal depth was chosen within {5, 6, 8, 10}, the number of estimators was chosen within {50, 100, 200, 300} and the learning rate was chosen within {0.2, 0.1, 0.05}. We also applied known tree regularization techniques, the fraction of train records sampled per tree was chosen within {1, 0.8, 0.5} and the fraction of features sampled per tree was chosen within {1, 0.8, 0.5} (where 1 is using all the records). All DNN-based models were trained using Adam optimizer (Kingma & Ba, 2014) and a learning rate of 1e-3. We used early stopping while monitoring the validation loss with a patience of 3 epochs. |