Agnostic Learning with Multiple Objectives
Authors: Corinna Cortes, Mehryar Mohri, Javier Gonzalvo, Dmitry Storcheus
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We further implement the algorithm in a popular symbolic gradient computation framework and empirically demonstrate on a number of datasets the benefits of ALMO framework versus learning with a fixed mixture weights distribution. |
| Researcher Affiliation | Collaboration | Corinna Cortes Google Research New York, NY 10011 corinna@google.com; Javier Gonzalvo Google Research New York, NY 10011 xavigonzalvo@google.com; Mehryar Mohri Google & Courant Institute New York, NY 10012 mohri@google.com; Dmitry Storcheus Courant Institute & Google New York, NY 10012 dstorcheus@google.com |
| Pseudocode | Yes | The pseudocode for the ALMO optimization algorithm is given in Algorithm 1. |
| Open Source Code | No | The paper states that the algorithm is implemented in TensorFlow, Keras, and PyTorch, but does not provide a specific link or explicit statement of release for its own source code. |
| Open Datasets | Yes | We conducted a series of experiments on several datasets that demonstrate the benefits of the ALMO framework versus learning with a uniform mixture weights distribution. The models are compared on MNIST [Le Cun and Cortes, 2010], Fashion-MNIST [Xiao et al., 2017] and ADULT [Dua and Graff, 2017] datasets with standard feature preprocessing techniques applied. |
| Dataset Splits | Yes | For both models, we run hyper-parameter tuning with a parameter grid size 50 on a validation set, which is 20% of the training data. |
| Hardware Specification | No | The paper does not specify the hardware used for experiments, such as specific GPU or CPU models. |
| Software Dependencies | No | The paper mentions popular symbolic gradient computation platforms like TENSORFLOW [Abadi et al., 2016], KERAS [Chollet et al., 2015], or PYTORCH [Paszke et al., 2019], but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | For both models, we run hyper-parameter tuning with a parameter grid size 50 on a validation set, which is 20% of the training data. [...] We report results for two model architectures: a logistic regression and a neural network with dimensions 1024-512-128. |