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 [1].
Conditional Dependence via Shannon Capacity: Axioms, Estimators and Applications
Authors: Weihao Gao, Sreeram Kannan, Sewoong Oh, Pramod Viswanath
ICML 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we demonstrate an application to single-cell flowcytometry, where the proposed estimators significantly reduce sample complexity. and 5. Numerical Experiments and Both figures illustrate that the proposed estimators significantly improves over the state-of-the-art partition based methods, in terms of sample complexity. |
| Researcher Affiliation | Academia | Weihao Gao EMAIL CSL and Dept. of ECE, University of Illinois, Urbana-Champaign, USA Sreeram Kannan EMAIL Dept. of EE, University of Washington, Seattle, USA Sewoong Oh EMAIL CSL and Dept. of IESE, University of Illinois, Urbana-Champaign, USA Pramod Viswanath EMAIL CSL and Dept. of ECE, University of Illinois, Urbana-Champaign, USA |
| Pseudocode | No | The paper describes the estimators using mathematical formulas and text, but it does not include a clearly labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | No | The paper does not provide any links to open-source code for their methodology, nor does it state that code is available in supplementary materials or upon request. |
| Open Datasets | Yes | Finally, we demonstrate an application to single-cell flowcytometry and We briefly describe the setup of (Krishnaswamy et al., 2014) to motivate our numerical experiments. and subsample the original data from (Krishnaswamy et al., 2014) |
| Dataset Splits | No | To study this, we subsample the original data from (Krishnaswamy et al., 2014) multiple times (100 in the experiments) at each subsampling ratio and compute the fraction of times we recover the true biological trend. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper mentions statistical methods and algorithms but does not specify any software names with version numbers for reproducibility. |
| Experiment Setup | Yes | The estimator has only a single hyper parameter (the number of nearest-neighbors considered, set to 4 or 5 in practice) and Our estimator has only one hyperparameter k, the number of nearest neighbors to consider. and Standard (stochastic) gradient decent is used in our experiments. |