Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Improved Algorithms for Collaborative PAC Learning
Authors: Huy Nguyen, Lydia Zakynthinou
NeurIPS 2018 | Venue PDF | LLM Run Details | Input Tokens: 14,870 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,185 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work, we design new algorithms for both the realizable and the non-realizable setting, having sample complexity only O(ln(k)) times the worst-case sample complexity for learning a single task. The sample complexity upper bounds of our algorithms match previous lower bounds and in some range of parameters are even better than previous algorithms that are allowed to output different classifiers for different tasks. |
| Researcher Affiliation | Academia | Huy Lê Nguy ên College of Computer and Information Science Northeastern University Boston, MA 02115 EMAIL Lydia Zakynthinou College of Computer and Information Science Northeastern University Boston, MA 02115 EMAIL |
| Pseudocode | Yes | Algorithm R1 Initialize: i [k] w(0) i := 1; t := 5 log(k) ; ϵ := ϵ/6; δ := δ/(3t); for r = 1 to t do D(r 1) 1 Φ(r 1) Pk i=1 w(r 1) i Di , where Φ(r 1) = Pk i=1 w(r 1) i ; Draw a sample set S(r) of size mϵ /16,δ from D(r 1); f (r) OF(S(r)); Gr TEST(f (r), k, ϵ , δ ); Update: w(r) i = ( 2w(r 1) i , if i / Gr w(r 1) i , otherwise ; end for return f R1 = maj({f (r)}t r=1) Procedure TEST(f (r), k, ϵ , δ ) for i = 1 to k do Draw a sample set Ti of size O 1 ϵ ln k δ from Di; end for return {i | err Ti(f (r)) 3 |
| Open Source Code | No | No statement or link regarding the release of open-source code for the methodology described in the paper was found. |
| Open Datasets | No | The paper is theoretical and discusses 'drawing from a distribution of samples' and 'sample complexity' in the context of PAC learning, but does not use or provide access information for any specific public or open dataset for training purposes. |
| Dataset Splits | No | The paper is theoretical and does not conduct empirical experiments with data, therefore, it does not specify training, validation, or test dataset splits. It refers to 'samples' in a theoretical sense, not as part of a concrete dataset. |
| Hardware Specification | No | The paper is theoretical and describes algorithms and proofs; it does not mention any specific hardware used for running experiments or computations. |
| Software Dependencies | No | The paper is theoretical, focusing on algorithm design and theoretical sample complexity bounds. It does not mention any specific software components or their versions required for implementation or reproduction. |
| Experiment Setup | No | The paper is theoretical, proposing new algorithms and proving their sample complexity. It does not describe an experimental setup with hyperparameters or system-level training settings, as it does not report empirical results. |