Daily TMLR digest for Jun 26, 2024
TMLR
Accepted papers
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Title: On the Unreasonable Effectiveness of Federated Averaging with Heterogeneous Data
Authors: Jianyu Wang, Rudrajit Das, Gauri Joshi, Satyen Kale, Zheng Xu, Tong Zhang
Abstract: Existing theoretical results (such as (Woodworth et al., 2020a)) predict that the performance of federated averaging (FedAvg) is exacerbated by high data heterogeneity. However, in practice, FedAvg converges pretty well on several naturally heterogeneous datasets. In order to explain this seemingly unreasonable effectiveness of FedAvg that contradicts previous theoretical predictions, this paper introduces the client consensus hypothesis: on certain federated datasets, the average of local models updates on clients starting from the optimum is close to zero. We prove that under this hypothesis, data heterogeneity does not exacerbate the convergence of FedAvg. Moreover, we show that this hypothesis holds for a linear regression problem and some naturally heterogeneous datasets such as FEMNIST and StackOverflow. Therefore, we believe that this hypothesis can better explain the performance of FedAvg in practice.
URL: https://openreview.net/forum?id=zF76Ga4EPs
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Title: Reproducibility Study of "ITI-GEN: Inclusive Text-to-Image Generation"
Authors: Daniel Gallo Fernández, Răzvan-Andrei Matișan, Alejandro Monroy Muñoz, Janusz Partyka
Abstract: Text-to-image generative models often present issues regarding fairness with respect to certain sensitive attributes, such as gender or skin tone. This study aims to reproduce the results presented in "ITI-GEN: Inclusive Text-to-Image Generation" by Zhang et al. (2023), which introduces a model to improve inclusiveness in these kinds of models. We show that most of the claims made by the authors about ITI-GEN hold: it improves the diversity and quality of generated images, it is scalable to different domains, it has plug-and-play capabilities, and it is efficient from a computational point of view. However, ITI-GEN sometimes uses undesired attributes as proxy features and it is unable to disentangle some pairs of (correlated) attributes such as gender and baldness. In addition, when the number of considered attributes increases, the training time grows exponentially and ITI-GEN struggles to generate inclusive images for all elements in the joint distribution. To solve these issues, we propose using Hard Prompt Search with negative prompting, a method that does not require training and that handles negation better than vanilla Hard Prompt Search. Nonetheless, Hard Prompt Search (with or without negative prompting) cannot be used for continuous attributes that are hard to express in natural language, an area where ITI-GEN excels as it is guided by images during training. Finally, we propose combining ITI-GEN and Hard Prompt Search with negative prompting.
URL: https://openreview.net/forum?id=d3Vj360Wi2
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New submissions
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Title: Strategies for Pretraining Neural Operators
Abstract: Pretraining for partial differential equation (PDE) modeling has recently shown promise in scaling neural operators across datasets to improve generalizability and performance. Despite these advances, our understanding of how pretraining affects neural operators is still limited; studies generally propose tailored architectures and datasets that make it challenging to compare or examine different pretraining frameworks. To address this, we compare various pretraining methods without optimizing architecture choices to characterize pretraining dynamics on different models and datasets as well as to understand its scaling and generalization behavior. We find that pretraining is highly dependent on model and dataset choices, but in general transfer learning or physics-based pretraining strategies work best. In addition, pretraining performance can be further improved by using data augmentations. Lastly, pretraining is additionally beneficial when fine-tuning in scarce data regimes or when generalizing to downstream data similar to the pretraining distribution. Through providing insights into pretraining neural operators for physics prediction, we hope to motivate future work in developing and evaluating pretraining methods for PDEs.
URL: https://openreview.net/forum?id=9vEVeX9oIv
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Title: Undetectable Steganography for Language Models
Abstract: We introduce a cryptographic method to hide an arbitrary secret payload in the response of a Large Language Model (LLM). A secret key is required to extract the payload from the model's response, and without the key it is provably impossible to distinguish between the responses of the original LLM and the LLM that hides a payload. In particular, the quality of generated text is not affected by the payload.
Our approach extends a recent result of Christ, Gunn and Zamir (2023) who introduced an undetectable watermarking scheme for LLMs.
URL: https://openreview.net/forum?id=fq6aQoMSHz
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