Nonparametric Density Estimation under Adversarial Losses
Authors: Shashank Singh, Ananya Uppal, Boyue Li, Chun-Liang Li, Manzil Zaheer, Barnabas Poczos
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The Appendix provides proofs of our theoretical results, further applications, further discussion of related and future work, and experiments on simulated data that support our theoretical results. |
| Researcher Affiliation | Academia | Shashank Singh1,2, Ananya Uppal3 Boyue Li4 Chun-Liang Li1 Manzil Zaheer1 Barnabás Póczos1 1Machine Learning Department 2Department of Statistics & Data Science 3Department of Mathematical Sciences 4Language Technologies Institute Carnegie Mellon University |
| Pseudocode | No | The paper describes mathematical formulations and estimators but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described, nor does it explicitly state that the code is publicly available. |
| Open Datasets | No | The paper mentions 'experiments on simulated data' in the Appendix but does not provide concrete access information (specific link, DOI, repository name, formal citation with authors/year, or reference to established benchmark datasets) for a publicly available or open dataset. |
| Dataset Splits | No | The paper mentions 'simulated data' for experiments but does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | No | The paper focuses on theoretical analysis and does not contain specific experimental setup details (concrete hyperparameter values, training configurations, or system-level settings) in the main text. |