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..
Lower Bounds on Randomly Preconditioned Lasso via Robust Sparse Designs
Authors: Jonathan Kelner, Frederic Koehler, Raghu Meka, Dhruv Rohatgi
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We prove a stronger lower bound that rules out randomized preconditioners. For an appropriate covariance matrix, we construct a single signal distribution on which any invertibly-preconditioned Lasso program fails with high probability, unless it receives a linear number of samples. Surprisingly, at the heart of our lower bound is a new robustness result in compressed sensing. In particular, we study recovering a sparse signal when a few measurements can be erased adversarially. |
| Researcher Affiliation | Academia | Jonathan A. Kelner MIT Frederic Koehler Stanford Raghu Meka UCLA Dhruv Rohatgi MIT |
| Pseudocode | No | The paper is theoretical and focuses on proofs and lower bounds. It does not provide any pseudocode or algorithm blocks for its own methods. |
| Open Source Code | No | The paper does not contain any statements about releasing code or links to a source code repository. |
| Open Datasets | No | The paper is theoretical and does not involve empirical training or evaluation on datasets. Therefore, no dataset access information is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with data. No dataset split information (training, validation, test) is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe any empirical experiments, therefore no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe any software implementation or dependencies with specific version numbers. |
| Experiment Setup | No | The paper is theoretical and focuses on proofs and lower bounds. It does not describe any experimental setup details such as hyperparameters or training configurations. |