Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Stochastic Optimization Schemes for Performative Prediction with Nonconvex Loss
Authors: Qiang LI, Hoi-To Wai
NeurIPS 2024 | Venue PDF | LLM Run Details | Input Tokens: 27,605 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,512 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments corroborate our theories. We consider two examples of performative prediction with non-convex loss based on synthetic data and real data. All simulations are performed with Pytorch on a server using a Intel Xeon 6318 CPU. |
| Researcher Affiliation | Academia | Qiang Li Hoi-To Wai Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong, Shatin, Hong Kong SAR of China EMAIL |
| Pseudocode | No | The paper describes algorithms verbally and with mathematical equations (e.g., equation (2) for SGD-GD, equation (23) for lazy deployment), but does not include explicit pseudocode or algorithm blocks labeled as such. |
| Open Source Code | Yes | The paper will provide open access to the source code, ensuring that the main experimental results can be faithfully reproduced. |
| Open Datasets | Yes | Our second example deals with the task of training a neural network (NN) on the spambase Hopkins et al. [1999] dataset with m = 4601 samples, each with d = 48 features. |
| Dataset Splits | No | We split the training/test sets as 8 : 2. |
| Hardware Specification | Yes | All simulations are performed with Pytorch on a server using a Intel Xeon 6318 CPU. |
| Software Dependencies | No | All simulations are performed with Pytorch on a server using a Intel Xeon 6318 CPU. |
| Experiment Setup | Yes | For (2), the batch size is b = 1 and the stepsize is γt = γ = 1/√T with T = 10^6. In our experiment, we set ϵNN ∈ {0, 10, 100}, batch size as b = 8. For SGD-GD, we use γt = γ = 200/√T and for lazy deployment, we use γ = 200/(K√T) with T = 10^5. The NN encoded in fθ(x) consists of three fully-connected layers with tanh activation and a sigmoid output layer, i.e., fθ(x) = Sigmoid(θ(1)tanh(θ(2)tanh(θ(3)x))) , where θ(i) := [w(i); b(i)] concatenates the weight and bias for each layer with d1 = 10, d2 = 50, d3 = 57 neurons, making a total of d = 3421 parameters for θ. |