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..
Automatic Clipping: Differentially Private Deep Learning Made Easier and Stronger
Authors: Zhiqi Bu, Yu-Xiang Wang, Sheng Zha, George Karypis
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate on various language and vision tasks that automatic clipping outperforms or matches the state-of-the-art, and can be easily employed with minimal changes to existing codebases1.We evaluate our automatic DP training on image classification, sentence classification, and table-to-text generation tasks. |
| Researcher Affiliation | Collaboration | Zhiqi Bu AWS AI EMAIL Yu-Xiang Wang AWS AI, UC Santa Barbara EMAIL Sheng Zha AWS AI EMAIL George Karypis AWS AI EMAIL |
| Pseudocode | Yes | Algorithm 1 Automatic Deep Learning with DP Parameters: initial weights w0, learning rate ηt, sampling probability p, number of iterations T. 1: Compute σ such that ϵAccountant(δ, σ, p, T) ϵ from any privacy accountant. 2: for iteration t = 1, , T do 3: Sample a batch Bt by including each data point i.i.d. with probability p 4: Apply automatic clipping to per-sample gradients {gi}i Bt: ˆgi = gi/( gi 2 + 0.01). 5: Add Gaussian noise to the sum of clipped gradients: ˆg = P i ˆgi + σ N(0, I). 6: Update wt by any optimizer on the private gradient ˆg with learning rate ηt. |
| Open Source Code | Yes | Code for our experiments is available at Fast DP library https://github.com/awslabs/fast-differential-privacy. |
| Open Datasets | Yes | For MNIST/Fashion MNIST, we use the same setup as in [56, 68, 64] with a simple CNN. For CIFAR10, we use the same setup as in [68] with pretrained Sim CLRv2 [13]. For Image Nette, a 10-class sub-task of Image Net [18], we use the same setup as in [36] without the learning rate decay. For Celeb A [45], the real human face dataset, we train Res Net9 [32] with group normalization to replace the batch normalization. On five benchmark language datasets (MNLI(m/mm)[72], QQP[34], QNLI[62], SST2[67]), we compare our automatic DP training with re-parameterized gradient perturbation (RGP, [78]) and full-parameter finetuning (full, [41]) using Ro BERTa models [44]. We compare our automatic DP training with a variety of fine-tuning methods, for table-to-text generation task on E2E dataset [23] |
| Dataset Splits | Yes | MNLI(m) MNLI-matched, the matched validation and test splits from Multi-Genre Natural Language Inference Corpus. The datasets are processed and loaded from Huggingface [39], as described in https:// huggingface.co/datasets/glue. We follow the same setup as [78] and [41]. Table 5: Hyperparameters of automatic clipping and Abadi s clipping, for sentence classification in Table 2 and Table 3, using either Ro BERTa base or large. |
| Hardware Specification | No | No specific hardware (GPU/CPU models, memory) used for running the experiments is detailed in the paper. It mentions "large foundation models" like GPT2 and GPT3-175B, implying significant computation but without hardware specifics. |
| Software Dependencies | Yes | For Opacus [77] version 1.1.2 (latest), we can implement the all-layer automatic clipping by changing Line 399-401 in https://github.com/pytorch/opacus/blob/main/opacus/optimizers/ optimizer.py to... For Ob JAX version 1.6.0 (latest), we can implement the automatic clipping in https://github. com/google/objax/blob/master/objax/privacy/dpsgd/gradient.py by changing Line 92 to... |
| Experiment Setup | Yes | Detailed settings including hyperparameters can be found in Appendix G. Table 5: Hyperparameters of automatic clipping and Abadi s clipping, for sentence classification in Table 2 and Table 3, using either Ro BERTa base or large. Table 7: Hyperparameters of automatic clipping and Abadi s clipping, for the E2E generation task in Table 4. |