Minimax Estimation of Conditional Moment Models
Authors: Nishanth Dikkala, Greg Lewis, Lester Mackey, Vasilis Syrgkanis
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conclude with an extensive experimental analysis of the proposed methods. and 9 Experimental Analysis |
| Researcher Affiliation | Collaboration | Nishanth Dikkala MIT nishanthd@csail.mit.edu Greg Lewis Microsoft Research glewis@microsoft.com Lester Mackey Microsoft Research lmackey@microsoft.com Vasilis Syrgkanis Microsoft Research vasy@microsoft.com |
| Pseudocode | Yes | Theorem 4. Consider the algorithm where for t = 1, . . . , T: let , ft = Oracle F (z1:n, ut i = 1{ft(zi) > 0}, wt i = |ft(zi)| ht = Oracle H |
| Open Source Code | Yes | Associated code can be found in https://github.com/microsoft/Adversarial GMM. |
| Open Datasets | Yes | MNIST dataset consisting of grayscale images of 28 28 pixels. |
| Dataset Splits | No | The paper does not explicitly state specific training, validation, or test dataset splits (e.g., percentages or sample counts) needed for reproduction. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions software components like 'Elastic Net CV' and 'neural networks' but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | We consider the following data generating processes: for nx = 1 and nz 1 y = h0(x[0]) + e + δ, δ N(0, .1) x = γ z[0] + (1 γ) e + γ, z N(0, 2 Inz), e N(0, 2), γ N(0, .1) ... We consider several functional forms for h0 including absolute value, sigmoid and sin functions... We consider as classic benchmarks 2SLS with a polynomial features of degree 3 (2SLS) and a regularized version of 2SLS where Elastic Net CV is used in both stages (Reg2SLS). |