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 [1].
Online stochastic gradient descent on non-convex losses from high-dimensional inference
Authors: Gerard Ben Arous, Reza Gheissari, Aukosh Jagannath
JMLR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We illustrate our approach by applying it to a wide set of inference tasks such as phase retrieval, and parameter estimation for generalized linear models, online PCA, and spiked tensor models, as well as to supervised learning for single-layer networks with general activation functions. ... For an illustration of this discussion, see Figures 2.1 2.2 for numerical experiments in the supervised learning setting. |
| Researcher Affiliation | Academia | Gerard Ben Arous EMAIL Courant Institute of Mathematical Sciences New York University New York, NY, USA; Reza Gheissari EMAIL Departments of Statistics and EECS University of California Berkeley, CA, USA; Aukosh Jagannath EMAIL Departments of Statistics and Actuarial Science and Applied Mathematics University of Waterloo Waterloo, ON, Canada |
| Pseudocode | Yes | Let Xt denote the output of the algorithm at time t, and let δ > 0 denote a step size parameter. The sequence of outputs of the algorithm are then given by the following procedure: X0 = x0 Xt = Xt 1 δ N LN(Xt 1; Y t) Xt = Xt || Xt|| |
| Open Source Code | No | The paper discusses various algorithms and their performance but does not provide any specific links to code repositories or state that code is made available in supplementary materials. |
| Open Datasets | No | The paper primarily uses synthetic data models, for example, assuming features (aℓ) are i.i.d. standard Gaussian vectors. No specific publicly available datasets with access information are mentioned. |
| Dataset Splits | No | The paper focuses on theoretical analysis and numerical experiments with synthetically generated data, and thus does not specify training/test/validation dataset splits. It discusses sample complexity 'M i.i.d. samples'. |
| Hardware Specification | No | The paper mentions 'numerical experiments' but does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used for these experiments. |
| Software Dependencies | No | The paper describes mathematical models and algorithms but does not specify any software libraries, programming languages, or their versions used for implementation or analysis. |
| Experiment Setup | Yes | The algorithm uses a 'step size parameter δ > 0' and the initial point 'x0 is possibly random, x0 µ M1(SN 1)'. In numerical experiments, it specifies 'N = 3000 and α = 100' or 'α = 30,000' and discusses 'random starts' and 'warm start (m0 = 0.5)'. |