Differentially Private Empirical Risk Minimization Revisited: Faster and More General
Authors: Di Wang, Minwei Ye, Jinhui Xu
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | 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 dwang45@buffalo.edu Minwei Ye Dept. of Computer Science and Engineering State University of New York at Buffalo Buffalo, NY 14260 minweiye@buffalo.edu Jinhui Xu Dept. of Computer Science and Engineering State University of New York at Buffalo Buffalo, NY 14260 jinhui@buffalo.edu |
| 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. |