QUIC & DIRTY: A Quadratic Approximation Approach for Dirty Statistical Models
Authors: Cho-Jui Hsieh, Inderjit S Dhillon, Pradeep K Ravikumar, Stephen Becker, Peder A. Olsen
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we show that our approach is more than 10 times faster than state-of-the-art first-order approaches for the latent variable graphical model selection problems and multi-task learning problems when there is more than one regularizer. |
| Researcher Affiliation | Collaboration | Cho-Jui Hsieh, Inderjit S. Dhillon, Pradeep Ravikumar University of Texas at Austin Austin, TX 78712 USA {cjhsieh,inderjit,pradeepr}@cs.utexas.edu Stephen Becker University of Colorado at Boulder Boulder, CO 80309 USA stephen.becker@colorado.edu Peder A. Olsen IBM T.J. Watson Research Center Yorktown Heights, NY 10598 USA pederao@us.ibm.com |
| Pseudocode | Yes | Algorithm 1: QUIC & DIRTY: Quadratic Approximation Framework for Dirty Statistical Models |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating that source code for the described methodology is publicly available. |
| Open Datasets | Yes | We run our algorithm on three gene expression datasets: the ER dataset (p = 692), the Leukemia dataset (p = 1255), and a subset of the Rosetta dataset (p = 2000). Our first dataset is the USPS dataset which was first collected in [25] and subsequently widely used in multi-task papers. Our second dataset is a larger document dataset RCV1 downloaded from LIBSVM Data, which has 53 classes and 47,236 features. |
| Dataset Splits | No | The paper mentions datasets and 'training data' in tables but does not provide specific percentages or counts for training, validation, or test splits, nor does it refer to predefined splits with citations for reproduction. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions other software packages like 'Logdet PPA', 'PGALM', and 'QUIC' for comparison, but does not specify version numbers for its own software dependencies or implementation details to ensure reproducibility. |
| Experiment Setup | Yes | For the parameters, we use λS = 0.5, λL = 50 for the ER and Leukemia datasets, which give us low-rank and sparse results. For the Rosetta dataset, we use the parameters suggested in Logdet PPA, with λS = 0.0313, λL = 0.1565. |