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..
Tailoring: encoding inductive biases by optimizing unsupervised objectives at prediction time
Authors: Ferran Alet, Maria Bauza, Kenji Kawaguchi, Nurullah Giray Kuru, Tomás Lozano-Pérez, Leslie Kaelbling
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Experiments, 5.1 Tailoring to impose symmetries and constraints at prediction time, Table 1: Test MSE loss for different methods; the second column shows the relative improvement over basic inductive supervised learning. |
| Researcher Affiliation | Academia | Ferran Alet, Maria Bauza, Kenji Kawaguchi, Nurullah Giray Kuru, Tomás Lozano-Pérez, Leslie Pack Kaelbling MIT EMAIL |
| Pseudocode | Yes | Algorithm 1 MAMmo Th: Model-Agnostic Meta-Tailoring Subroutine Training(f, Lsup, λsup, Ltailor, λtailor, Dtrain,b), Algorithm 2 CNGRAD for meta-tailoring Subroutine Training(f, Lsup, λsup, Ltailor, λtailor, steps,Dtrain,b) |
| Open Source Code | No | The paper does not explicitly provide a link to its source code or state that it is publicly available. |
| Open Datasets | Yes | We provide experiments on the CIFAR-10 dataset [31] by building on Sim CLR [13]., We apply meta-tailoring to robustly classifying CIFAR-10 [31] and Image Net [15] images, |
| Dataset Splits | No | The paper mentions 'training data' and 'test samples' but does not provide specific percentages or counts for training, validation, and test splits, nor does it specify a cross-validation setup. |
| Hardware Specification | No | The paper mentions leveraging 'the MIT supercloud platform [42]' in the acknowledgements, but does not specify particular GPU models, CPU models, or detailed hardware configurations used for their experiments. |
| Software Dependencies | No | The paper mentions software frameworks like PyTorch [38], TensorFlow [1], and JAX [10], but it does not specify exact version numbers for these or other libraries required for replication. |
| Experiment Setup | Yes | The first-order gave slightly better results, possibly because it was trained with a higher tailor learning rate (10 3) with which the second-order version was unstable (we thus used 10 4)., We use ν = 0.1 for all experiments., Finally, we use σ = σ2 ν2 0.23, 0.49, 0.995 so that the points used in our tailoring loss come from N(x, σ2). |