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 [1].
Differentially Private Empirical Risk Minimization Revisited: Faster and More General
Authors: Di Wang, Minwei Ye, Jinhui Xu
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Due to space limit, we leave many details, proofs, and experimental studies in the supplement. |
| Researcher Affiliation | Academia | Di Wang Dept. of Computer Science and Engineering State University of New York at Buffalo Buffalo, NY 14260 EMAIL Minwei Ye Dept. of Computer Science and Engineering State University of New York at Buffalo Buffalo, NY 14260 EMAIL Jinhui Xu Dept. of Computer Science and Engineering State University of New York at Buffalo Buffalo, NY 14260 EMAIL |
| Pseudocode | Yes | Algorithm 1 DP-SVRG(F r, x0, T, m, η, σ) Input: f(x, z) is G-Lipschitz and L-smooth. F r(x, D) is µ-strongly convex w.r.t ℓ2-norm. x0 is the initial point, η is the step size, T, m are the iteration numbers. 1: for s = 1, 2, , T do 2: x = xs 1 3: v = F( x) 4: xs 0 = x 5: for t = 1, 2, , m do 6: Pick is t [n] 7: vs t = f(xs t 1, zis t ) f( x, zis t ) + v + us t, where us t N(0, σ2Ip) 8: xs t = proxηr(xs t 1 ηvs t ) 9: end for 10: xs = 1 m Pm k=1 xs k 11: end for 12: return x T |
| 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 | The paper discusses 'dataset D = {z1, z2 , zn}' and 'large datasets' but does not provide specific access information (link, DOI, formal citation with authors/year) for any publicly available or open dataset. |
| Dataset Splits | No | The paper does not provide specific dataset split information (percentages, sample counts, citations to predefined splits, or detailed splitting methodology). |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or detailed computer specifications) used for running experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers). |
| Experiment Setup | No | The paper does not contain specific experimental setup details such as concrete hyperparameter values or training configurations. |