Risk Bounds and Calibration for a Smart Predict-then-Optimize Method
Authors: Heyuan Liu, Paul Grigas
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform experiments to empirically demonstrate the strength of the SPO+ surrogate, as compared to standard ℓ1 and squared ℓ2 prediction error losses, on portfolio allocation and cost-sensitive multi-class classification problems. |
| Researcher Affiliation | Academia | Heyuan Liu University of California, Berkeley Berkeley, CA 94720 heyuan_liu@berkeley.edu Paul Grigas University of California, Berkeley Berkeley, CA 94720 pgrigas@berkeley.edu |
| Pseudocode | No | The paper describes mathematical formulations and theoretical results but does not include any pseudocode or algorithm blocks. |
| Open Source Code | Yes | 3. If you ran experiments... (a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] Please refer Appendix D. |
| Open Datasets | No | We present computational results of synthetic dataset experiments wherein we empirically examine the performance of the SPO+ loss function... In our simulations, the relationship between the true cost vector c and its auxiliary feature vector x is given by c = φdeg(Bx) ϵ, where φdeg is a polynomial kernel mapping of degree deg, B is a fixed weight matrix, and ϵ is a multiplicative noise term. The features are generated from a standard multivariate normal distribution, we consider d = 50 assets, and further details of the synthetic data generation process are provided in Appendix D. |
| Dataset Splits | No | We set the size of the test set to 10000. ... Figure 2 shows a detailed comparison between these alternative SPO+ surrogates as we vary the training set size. (No explicit mention of a validation set split.) |
| Hardware Specification | Yes | All experiments were run on a single machine with a 3.0 GHz Intel Xeon W-2145 CPU and 128 GB of RAM. |
| Software Dependencies | No | We use Pytorch [Paszke et al., 2019] for all of our experiments. (No version number provided for PyTorch, nor for Adam optimizer or other potential dependencies). |
| Experiment Setup | Yes | We focus on two classes of prediction models: (i) linear models, and (ii) two-layer neural networks with 256 neurons in the hidden layer. ... For all loss functions, we use the Adam method of Kingma and Ba [2015] to train the parameters of the prediction models. ... The learning rate is initialized at 1e-3 and decays by 0.5 every 20 epochs. For both linear models and neural networks, we train for 100 epochs. |