Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Fast Rates for Exp-concave Empirical Risk Minimization
Authors: Tomer Koren, Kfir Levy
NeurIPS 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We establish the first evidence that ERM is able to attain fast generalization rates, and show that the expected loss of the ERM solution in d dimensions converges to the optimal expected loss in a rate of d/n. Our convergence analysis relies on stability arguments introduced by Bousquet and Elisseeff [4]. We prove that the expected loss of the regularized ERM solution does not change significantly when a single instance, picked uniformly at random from the training sample, is discarded. Then, the technique of Bousquet and Elisseeff [4] allows us to translate this average stability property into a generalization guarantee. Our proof of Theorem 1 proceeds as follows. First, we relate the expected excess risk of the ERM estimator bw to its average leave-one-out stability [4]. Then, we bound this stability in terms of certain local properties of the empirical risk at the point bw. The remainder of the section is devoted to the proof of Theorem 3. |
| Researcher Affiliation | Academia | Tomer Koren Technion Haifa 32000, Israel EMAIL Kfir Y. Levy Technion Haifa 32000, Israel EMAIL |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not describe the use of specific datasets in experiments. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments or dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe the use of specific hardware for experiments. |
| Software Dependencies | No | The paper is theoretical and does not describe software dependencies with version numbers for experimental replication. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or training configurations. |