Customizing ML Predictions for Online Algorithms
Authors: Keerti Anand, Rong Ge, Debmalya Panigrahi
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We support this fnding both through the oretical bounds and numerical simulations. We use numerical simulations to evaluate the algorithms that we designed for the learning-to-rent prob lem: the black box algorithm (Algorithm 3), the marginbased algorithm (Algorithm 4), and the algorithm for a noisy classifer (Algorithm 5). |
| Researcher Affiliation | Academia | Department of Computer Science, Duke University, Durham NC, United States. |
| Pseudocode | Yes | Algorithm 1 Outputs θA for a given distribution on y. Algorithm 2 Outputs θA(x) for multi-dimensional x. Algorithm 3 Black box learning-to-rent algorithm. Algorithm 4 Margin-based learning-to-rent algorithm. Algorithm 5 Learning-to-rent with a noisy classifer. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | No | Experimental Setup. We frst describe the joint distribu tion (x, y) K used in the experiments. We choose a random vector W Rd as W N(0, I/d). We view W as a hyper-plane passing through the origin (W T x = 0). ...The input x is drawn from a mixture distribution, where with probability 1/2 we sample x from a Gaussian x N(0, I/d), and with probability 1/2, we sample x as x = αW + η, here α N(0, 1) is a coeffcient in the direction 1 of W and η N(0, I). Choosing x from the Gaussian d distribution ensures that the data-set has no margin; however, in high dimensions, W T x will concentrate in a small region, which makes all the label y very close to 1. We address this issue by mixing in the second component which ensures that the distribution of y is diverse. No direct access (link, DOI, or specific citation for public availability) is provided for the custom-generated dataset used in the experiments. |
| Dataset Splits | Yes | Training and Validation. For a given training set, we split it in two equal halves, the frst half is used to train our PAC learner and the second half is used as a validation set to optimize the design parameters in the algorithms, namely τ in Algorithm 3 and γ in Algorithm 4. |
| Hardware Specification | No | The paper does not specify the exact hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions, or specific libraries). |
| Experiment Setup | Yes | Experimental Setup. We frst describe the joint distribu tion (x, y) K used in the experiments. We choose a random vector W Rd as W N(0, I/d). ... The input x is drawn from a mixture distribution, where with probability 1/2 we sample x from a Gaussian x N(0, I/d), and with probability 1/2, we sample x as x = αW + η, here α N(0, 1) is a coeffcient in the direction 1 of W and η N(0, I). ...For a given training set, we split it in two equal halves, the frst half is used to train our PAC learner and the second half is used as a validation set to optimize the design parameters in the algorithms, namely τ in Algorithm 3 and γ in Algorithm 4. |