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..
Sequential Attention for Feature Selection
Authors: Taisuke Yasuda, Mohammadhossein Bateni, Lin Chen, Matthew Fahrbach, Gang Fu, Vahab Mirrokni
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This work introduces the Sequential Attention algorithm for supervised feature selection... Empirically, Sequential Attention achieves state-of-the-art feature selection results for neural networks on standard benchmarks. The code for our algorithm and experiments is publicly available.1 |
| Researcher Affiliation | Collaboration | Taisuke Yasuda* Carnegie Mellon University EMAIL Mohammad Hossein Bateni, Lin Chen, Matthew Fahrbach, Gang Fu*, and Vahab Mirrokni Google Research EMAIL |
| Pseudocode | Yes | Algorithm 1 Sequential Attention for feature selection. Algorithm 2 Orthogonal Matching Pursuit (Pati et al., 1993). Algorithm 3 Sequential LASSO (Luo & Chen, 2014). |
| Open Source Code | Yes | The code for our algorithm and experiments is publicly available.1 The code is available at: github.com/google-research/google-research/tree/master/sequential attention |
| Open Datasets | Yes | In these experiments, we consider six datasets used in experiments in Lemhadri et al. (2021); Balın et al. (2019), and select 𝑘= 50 features... Table 1: Statistics about benchmark datasets. Dataset # Examples # Features # Classes Type Mice Protein 1,080 77 8 Biology MNIST 60,000 784 10 Image MNIST-Fashion 60,000 784 10 Image ISOLET 7,797 617 26 Speech COIL-20 1,440 400 20 Image Activity 5,744 561 6 Sensor |
| Dataset Splits | No | The paper mentions 'test data' and 'prediction accuracies' but does not explicitly specify the training/test/validation dataset splits (e.g., percentages or counts) or reference predefined splits with citations for reproducibility. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments, such as GPU models, CPU specifications, or cloud computing instance types. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions, or specific library versions). |
| Experiment Setup | Yes | In these experiments, we consider six datasets used in experiments in Lemhadri et al. (2021); Balın et al. (2019), and select 𝑘= 50 features using a one-layer neural network with hidden width 67 and Re LU activation... Table 4: Epochs and batch size used to compare the efficiency of feature selection algorithms... For this experiment, we use a dense neural network with 768, 256, and 128 neurons in each of the three hidden layers with Re LU activations. |