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..
Alleviating Privacy Attacks via Causal Learning
Authors: Shruti Tople, Amit Sharma, Aditya Nori
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate on two types of datasets: 1) Four datasets generated from known Bayesian Networks and 2) Colored images of digits from the MNIST dataset. Code is available at https://github.com/microsoft/robustdg. ... 4.1 Results for Bayesian Network Datasets ... 4.2 Results for Colored MNIST Dataset |
| Researcher Affiliation | Industry | 1Microsoft Research. Correspondence to: Shruti Tople <EMAIL>, Amit Sharma <EMAIL>. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/microsoft/robustdg. |
| Open Datasets | Yes | We select 4 Bayesian network datasets Child, Sachs, Alarm and Water that range from 178 10k parameters (Table 1)2. ... 2www.bnlearn.com/bnrepository. ... For this, we consider colored MNIST images used in a recent work by Arjovsky et al. (2019). ... 3http://yann.lecun.com/exdb/mnist/ 4https://github.com/facebookresearch/Invariant Risk Minimization. |
| Dataset Splits | No | The paper states: 'We sample data using the causal structure and probabilities from the Bayesian network, and use a 60 : 40% split for train-test datasets.' However, it does not explicitly provide information about a separate validation split or its size/percentage for either dataset. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | Yes | To train the causal model, we use the bnlearn library in R language... To train the DNN model and the attacker model, we build custom estimators in Python using Tensor๏ฌow v1.2. |
| Experiment Setup | Yes | The DNN model is a multilayer perceptron (MLP) with 3 hidden layers of 128, 512 and 128 nodes respectively. The learning rate is set to 0.0001 and the model is trained for 10000 steps. The attacker model has 2 hidden layers with 5 nodes each, a learning rate of 0.001, and is trained for 5000 steps. Both models use Adam optimizer, Re LU for the activation function, and cross entropy as the loss function. |