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..
Coping with Label Shift via Distributionally Robust Optimisation
Authors: Jingzhao Zhang, Aditya Krishna Menon, Andreas Veit, Srinadh Bhojanapalli, Sanjiv Kumar, Suvrit Sra
ICLR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, through experiments on CIFAR-100 and Image Net, we show that our technique can significantly improve performance over a number of baselines in settings where label shift is present. |
| Researcher Affiliation | Collaboration | Jingzhao Zhang Massachusetts Institute of Technology EMAIL Aditya Krishna Menon & Andreas Veit & Srinadh Bhojanapalli & Sanjiv Kumar Google Research EMAIL Suvrit Sra Massachusetts Institute of Technology EMAIL |
| Pseudocode | Yes | Algorithm 1 ADVSHIFT(θ0, γc, λ, NNOpt, pemp, ηπ) |
| Open Source Code | No | The paper does not explicitly state that code is released or provide a link to a code repository. |
| Open Datasets | Yes | Finally, through experiments on CIFAR-100 and Image Net, we show that our technique can significantly improve performance over a number of baselines in settings where label shift is present. |
| Dataset Splits | Yes | First, we train a model on the training set and compute its error distribution on the validation set. Next, we pick a threshold τ on the allowable KL divergence between the train and target distribution and find the adversarial distribution within this threshold which achieves the worst-possible validation error. |
| Hardware Specification | No | The paper mentions training a 'Res Net-50' model, but it does not specify any particular hardware components like GPU models (e.g., NVIDIA A100), CPU types, or cloud computing instances with their specifications. |
| Software Dependencies | No | The paper mentions 'Tensorflow' in Appendix E ('Given that our implementation is based on Tensorflow...'), but it does not specify a version number for Tensorflow or any other key software libraries or dependencies used in the experiments. |
| Experiment Setup | Yes | To evaluate the proposed method, we use the standard image classification setup of training a Res Net50 on Image Net using SGD with momentum as the neural network optimiser. All algorithms are run for 90 epochs... We set 2γcλ = 1 in Algorithm 1 for simplicity. For learning the adversarial distribution, we only tune the adversarial learning rate ηπ. ... we clip the label-wise loss at value 2. Second, we add a constant ϵ term on the adversarial distribution to avoid the adversarial distribution reaching any of the vertices on the simplex. |