Accurate Inference for Adaptive Linear Models
Authors: Yash Deshpande, Lester Mackey, Vasilis Syrgkanis, Matt Taddy
ICML 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We then demonstrate the empirical benefits of the generic W -decorrelation procedure in two different adaptive data settings: the multi-armed bandit and the autoregressive time series. In this section we empirically validate the decorrelated estimators in two scenarios that involve sequential dependence in covariates. Our first scenario is a simple experiment of multi-armed bandits while the second scenario is autoregressive time series data. In these cases, we compare the empirical coverage and typical widths of confidence intervals for parameters obtained via three methods: (i) classical OLS theory, (ii) concentration inequalities and (iii) decorrelated estimates. Jupyter notebooks reproducing our experiments are available on the first author s Github (Deshpande et al., 2018). |
| Researcher Affiliation | Collaboration | 1Department of Mathematics, MIT 2Microsoft Research New England 3Booth School of Business, University of Chicago. |
| Pseudocode | Yes | Algorithm 1 W -Decorrelation Method |
| Open Source Code | Yes | Jupyter notebooks reproducing our experiments are available on the first author s Github (Deshpande et al., 2018). |
| Open Datasets | No | The paper describes simulated data generation for multi-armed bandits and autoregressive time series, but does not provide access information (link, DOI, formal citation) to a publicly available or open dataset. |
| Dataset Splits | No | The paper describes generating synthetic data for simulations but does not specify any training, validation, or test dataset splits in the conventional sense for pre-existing datasets. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU models, CPU types, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions 'Jupyter notebooks' but does not specify particular software dependencies with version numbers (e.g., Python version, library versions). |
| Experiment Setup | Yes | We sequentially assign one of p = 2 treatments to each of n = 444 patients using one of three policies... For ECB, the treatment administered to patient i is, with probability 1 ε = .9, the treatment with the largest posterior mean; with probability 1 ε, a uniformly random treatment is administered instead... an independent Gaussian prior (with mean µ0 = 0.3 and variance σ2 0 = 0.33)... For the AR(p) model where yi = P ℓ p βℓyi ℓ+ εi... p = 2, n = 50, β = (0.95, 0.2), y0 = 0 and εi Unif([ 1, 1]); all estimates are computed over 4000 monte carlo iterations. |