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..
Differentiable Meta-Learning of Bandit Policies
Authors: Craig Boutilier, Chih-wei Hsu, Branislav Kveton, Martin Mladenov, Csaba Szepesvari, Manzil Zaheer
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments show the versatility of our approach. We also observe that neural network policies can learn implicit biases expressed only through the sampled instances. |
| Researcher Affiliation | Collaboration | Craig Boutilier Google Research Chih-Wei Hsu Google Research Branislav Kveton Google Research Martin Mladenov Google Research Csaba Szepesvári Deep Mind / University of Alberta Manzil Zaheer Google Research |
| Pseudocode | Yes | Algorithm 1 Gradient-based optimization of bandit policies. 1: Inputs: Policy parameters w0 W, number of iterations L, learning rate α, and batch size m 2: w w0 3: for ℓ= 1, . . . , L do 4: for j = 1, . . . , m do 5: Sample P j P; sample Y j P j; and apply policy πw to Y j to get Ij 6: Let ˆg(n; πw) be an estimate of wr(n; πw) from (Y j)m j=1 and (Ij)m j=1 7: w w + α ˆg(n; πw) 8: Output: Learned policy parameters w |
| Open Source Code | No | The paper mentions implementing experiments in TensorFlow and PyTorch, which are third-party tools, but does not state that the authors' own code for the described methodology is open-source or provide a link. |
| Open Datasets | No | The paper describes how problem instances and rewards are sampled or drawn from a prior distribution (e.g., 'prior distribution P is over two problem instances, µ = (0.6, 0.4) and µ = (0.4, 0.6), both with probability 0.5'), but it does not provide concrete access information (like a URL, DOI, or specific citation for a downloadable dataset) for a publicly available or open dataset used for training or general experimentation. |
| Dataset Splits | No | The paper describes generating 'sampled problem instances' for optimization and for estimating regret, but it does not specify explicit training/validation/test dataset splits (e.g., exact percentages or sample counts) for a pre-defined dataset. |
| Hardware Specification | Yes | Our experiments are implemented in Tensor Flow and Py Torch, on 112 cores and with 392 GB RAM. |
| Software Dependencies | No | The paper mentions 'Tensor Flow and Py Torch' but does not specify their version numbers. |
| Experiment Setup | Yes | The policies are optimized by Grad Band with w0 = 1, L = 100 iterations, learning rate α = c 1L 1 2 , and batch size m = 1 000. |