Reliable learning in challenging environments
Authors: Maria-Florina F. Balcan, Steve Hanneke, Rattana Pukdee, Dravyansh Sharma
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work, we consider the design and analysis of reliable learners in challenging test-time environments as encountered in modern machine learning problems: namely adversarial test-time attacks (in several variations) and natural distribution shifts. In this work, we provide a reliable learner with provably optimal guarantees in such settings. We discuss computationally feasible implementations of the learner and further show that our algorithm achieves strong positive performance guarantees on several natural examples: for example, linear separators under log-concave distributions or smooth boundary classifiers under smooth probability distributions. |
| Researcher Affiliation | Academia | Maria-Florina Balcan Carnegie Mellon University ninamf@cs.cmu.edu Steve Hanneke Purdue University steve.hanneke@gmail.com Rattana Pukdee Carnegie Mellon University rpukdee@cs.cmu.edu Dravyansh Sharma Carnegie Mellon University dravyans@cs.cmu.edu |
| Pseudocode | No | The paper describes algorithms conceptually and provides mathematical formulations, but it does not include a clearly labeled 'Pseudocode' or 'Algorithm' block with structured steps. |
| Open Source Code | No | The paper does not contain any statements about releasing open-source code for the methodology described, nor does it provide any links to a code repository. |
| Open Datasets | No | The paper is theoretical, discussing concepts like 'sample S = {(xi, yi)}m' and 'distribution D over X Y' without referring to specific named public datasets (e.g., CIFAR-10, MNIST) or providing access information for any data. |
| Dataset Splits | No | The paper is theoretical and does not mention any training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not mention any specific hardware specifications (e.g., GPU models, CPU types) used for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not list any specific software dependencies or version numbers. |
| Experiment Setup | No | The paper is theoretical and does not provide specific experimental setup details, hyperparameters, or system-level training settings. |