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..
Learning Low-dimensional Latent Dynamics from High-dimensional Observations: Non-asymptotics and Lower Bounds
Authors: Yuyang Zhang, Shahriar Talebi, Na Li
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We simulate our algorithm for a set of simple systems where r = m = 1, Σw = 0 and ση = 1. We use A = 0.9, B = 1 and randomly sample C with orthonormal columns. We choose inputs with covariance Σu = 0.1. The choice of n will be clear in the context. The results are reported in Figure 1. |
| Researcher Affiliation | Academia | Yuyang Zhang 1 Shahriar Talebi 1 Na Li 1 1SEAS, Harvard University, Cambridge, USA. |
| Pseudocode | Yes | Algorithm 1 Column Space Projection SYSID (Col-SYSID) Algorithm 2 Column Space Approximation (Col-Approx) Algorithm 3 Meta Column Space Projection SYSID (Meta-Col-SYSID) Algorithm 4 Ho-Kalman |
| Open Source Code | No | The paper does not contain any statements about making its source code publicly available, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper uses simulated data for its experiments, stating, 'We simulate our algorithm for a set of simple systems where r = m = 1, Σw = 0 and ση = 1.' It does not refer to any pre-existing public datasets with access information. |
| Dataset Splits | Yes | We consider system M and datasets D1 = U1 Y1, D2 = U2 Y2 (with lengths T1, T2 respectively) in HDSYSID. ... If T1 κ3 n2r3, T2 κ1 poly (r, m)... |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory, or cluster specifications) used to run the experiments. |
| Software Dependencies | No | The paper does not list any specific software dependencies, libraries, or solvers with version numbers. |
| Experiment Setup | Yes | We simulate our algorithm for a set of simple systems where r = m = 1, Σw = 0 and ση = 1. We use A = 0.9, B = 1 and randomly sample C with orthonormal columns. We choose inputs with covariance Σu = 0.1. ... We first simulate Col-Approx (Algorithm 2) with n = 40, 80, 160, 320 and a single trajectory data of length T = 10000 separately (Figure 1.Left). Next, we simulate Col-SYSID (Algorithm 1) with T = 10000 and n = 320 (Figure 1.Center). |