Daily TMLR digest for Nov 29, 2025
TMLR
Accepted papers
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Title: TT-TFHE: a Torus Fully Homomorphic Encryption-Friendly Neural Network Architecture
Authors: Adrien Benamira, Tristan Guérand, Thomas Peyrin, Sayandeep Saha
Abstract: This paper presents TT-TFHE, a deep neural network Fully Homomorphic Encryption (FHE) framework that effectively scales Torus FHE (TFHE) usage to tabular and image datasets using the Truth-Table Neural Networks (TTnet) family of Convolutional Neural Networks. The proposed framework provides an easy-to-implement, automated TTnet-based design toolbox with an underlying (python-based) open-source Concrete implementation (CPU-based and implementing lookup tables) for inference over encrypted data. Experimental evaluation shows that TT-TFHE greatly outperforms in terms of time and accuracy all Homomorphic Encryption (HE) set-ups on three tabular datasets, all other features being equal. On image datasets such as MNIST and CIFAR-10, we show that TT-TFHE consistently and largely outperforms other TFHE set-ups and is competitive against other HE variants such as BFV or CKKS (while maintaining the same level of 128-bit encryption security guarantees). In addition, our solutions present a very low memory footprint (down to dozens of MBs for MNIST), which is in sharp contrast with other HE set-ups that typically require tens to hundreds of GBs of memory per user (in addition to their communication overheads). This is the first work presenting a fully practical and production-ready solution of private inference (i.e. a few seconds for inference time and a few dozen MBs of memory) on both tabular datasets and MNIST, that can easily scale to multiple threads and users on server side. We further show that in real-world settings, our proposals reduce costs by one to several orders of magnitude compared to existing solutions.
URL: https://openreview.net/forum?id=tV4ynvae6W
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Title: The Diffusion Process as a Correlation Machine: Linear Denoising Insights
Authors: Dana Weitzner, Mauricio Delbracio, Peyman Milanfar, Raja Giryes
Abstract: Recently, diffusion models have gained popularity due to their impressive generative abilities. These models learn the implicit distribution given by a training dataset, and sample new data by transforming random noise through the reverse process, which can be thought of as gradual denoising. In this work, to shed more light on the evolution of denoisers in the reverse process, we examine the generation process as a ``correlation machine'', where random noise is repeatedly enhanced in correlation with the implicit given distribution.
To this end, we explore the linear case, where the optimal denoiser in the MSE sense is known to be the PCA projection. This enables us to connect the theory of diffusion models to the spiked covariance model, where the dependence of the denoiser on the noise level and the amount of training data can be expressed analytically, in the rank-1 case.
In a series of numerical experiments, we extend this result to general low rank data, and show that low frequencies emerge earlier in the generation process, where the denoising basis vectors are more aligned to the true data with a rate depending on their eigenvalues. This model allows us to show that the linear reverse process is a generalization of the prevalent power iteration method, where the generated distribution is composed of several estimations of the given covariance, in varying stages of convergence.
Finally, we empirically demonstrate the applicability of our findings beyond the linear case, in the Jacobians of a deep, non-linear denoiser, used in general image generation tasks.
URL: https://openreview.net/forum?id=FGDJOc27rt
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Title: PICore: Physics-Informed Unsupervised Coreset Selection for Data Efficient Neural Operator Training
Authors: Anirudh Satheesh, Anant Khandelwal, Mucong Ding, Radu Balan
Abstract: Neural operators offer a powerful paradigm for solving partial differential equations (PDEs) that cannot be solved analytically by learning mappings between function spaces. However, there are two main bottlenecks in training neural operators: they require a significant amount of training data to learn these mappings, and this data needs to be labeled, which can only be accessed via expensive simulations with numerical solvers. To alleviate both of these issues simultaneously, we propose PICore, an unsupervised coreset selection framework that identifies the most informative training samples without requiring access to ground-truth PDE solutions. PICore leverages a physics-informed loss to select unlabeled inputs by their potential contribution to operator learning. After selecting a compact subset of inputs, only those samples are simulated using numerical solvers to generate labels, reducing annotation costs. We then train the neural operator on the reduced labeled dataset, significantly decreasing training time as well. Across four diverse PDE benchmarks and multiple coreset selection strategies, PICore achieves up to 78% average increase in training efficiency relative to supervised coreset selection methods with minimal changes in accuracy.
URL: https://openreview.net/forum?id=l0VSewTJCI
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New submissions
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Title: ClimateAgent: Multi-Agent Orchestration for Complex Climate Data Science Workflows
Abstract: Climate science demands automated workflows to transform comprehensive questions into data-driven statements across massive, heterogeneous datasets. However, generic LLM agents and static scripting pipelines lack climate-specific context and flexibility, thus, perform poorly in practice. We present ClimateAgent, an autonomous multi-agent framework that orchestrates end-to-end climate data analytic workflows. ClimateAgent decomposes user questions into executable sub-tasks coordinated by an Orchestrate-Agent and a Plan-Agent; acquires data via specialized Data-Agents that dynamically introspect APIs to synthesize robust download scripts; and completes analysis and reporting with a Coding-Agent that generates Python code, visualizations, and a final report with a built-in self-correction loop. To enable systematic evaluation, we introduce Climate-Agent-Bench-85, a benchmark of 85 real-world tasks spanning atmospheric rivers, drought, extreme precipitation, heat waves, sea surface temperature, and tropical cyclones. On Climate-Agent-Bench-85, ClimateAgent achieves $100\%$ task completion and a report quality score of $8.32$, outperforming GitHub-Copilot ($6.27$) and a GPT-5 baseline ($3.26$). These results demonstrate that our multi-agent orchestration with dynamic API awareness and self-correcting execution substantially advances reliable, end-to-end automation for climate science analytic tasks.
URL: https://openreview.net/forum?id=XLWvXNumGa
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Title: Linear Convergence and Generalization of FedAvg Under Constrained PL-Type Assumptions: A Single Hidden Layer Neural Network Analysis
Abstract: In this work, we study the generalization performance of the widely adopted FedAvg algorithm for solving Federated Learning (FL) problems. FedAvg has been extensively studied from an optimization perspective under different settings; however, analyzing the generalization performance of FedAvg is particularly challenging under practical settings since it involves simultaneously bounding (1) the optimization error and (2) the Rademacher complexity of the model to be learned, which are often contradictory. Specifically, obtaining optimization guarantees for FedAvg relies on restrictive assumptions on the loss landscape, such as (strong) convexity or Polyak-{\L}ojasiewicz (PL) inequality to be satisfied over the entire parameter space. However, for an unbounded space, it is challenging to control the Rademacher complexity, leading to worse generalization guarantees. In this work, we address this challenge by proposing novel {\em constrained PL-type} conditions on the {objective function} that ensure the existence of a global optimal to which {FedAvg converges} linearly after $\mathcal{O}( \log ({1}/{\epsilon}))$ rounds of communication, where $\epsilon$ is the desired optimality gap. Importantly, we demonstrate that a class of single hidden layer neural networks satisfies the proposed {\em constrained PL-type} conditions
% required to establish the linear convergence of FedAvg
as long as $m > {nK}/{d}$, where $m$ is the width of the neural network, $K$ is the number of clients, $n$ is the number of samples at each client, and $d$ is the feature dimension. Finally, we bound the Rademacher complexity for this class of neural networks and establish that the generalization error of FedAvg diminishes at the rate of $\mathcal{O}({1}/{\sqrt{n}})$.
URL: https://openreview.net/forum?id=a4UgLxAix0
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Title: Diffusion Models for Solving Inverse Problems via Posterior Sampling with Piecewise Guidance
Abstract: Diffusion models are powerful tools for sampling from high-dimensional distributions by progressively transforming pure noise into structured data through a denoising process. When equipped with a guidance mechanism, these models can also generate samples from conditional distributions. In this paper, a novel diffusion-based framework is introduced for solving inverse problems using a piecewise guidance scheme. The guidance term is defined as a piecewise function of the diffusion timestep, facilitating the use of different approximations during high-noise and low-noise phases. This design is shown to effectively balance computational efficiency with the accuracy of the guidance term.
Unlike task-specific approaches that require retraining for each problem, the proposed method is problem-agnostic and readily adaptable to a variety of inverse problems. Additionally, it explicitly incorporates measurement noise into the reconstruction process.
The effectiveness of the proposed framework is demonstrated through extensive experiments on image restoration tasks, specifically image inpainting and super-resolution.
Using a class conditional diffusion model for recovery, compared to the $\Pi$GDM baseline, the proposed framework achieves a reduction in inference time of $25\%$ for inpainting with both random and center masks, and $23\%$ and $24\%$ for $4\times$ and $8\times$ super-resolution tasks, respectively, while incurring only negligible loss in PSNR and SSIM.
URL: https://openreview.net/forum?id=nvw3XfvBi7
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Title: DOME: Distributed Online Learning based Multi-Estimate Fusion for Cooperative Predictive Target Tracking Using a Robotic Swarm
Abstract: This paper investigates cooperative predictive target tracking using a robotic swarm operating under high prediction bias and communication uncertainty. The robots interact over a randomly time-varying communication network and exhibit heterogeneity in onboard sensors and prediction algorithms. To address these challenges, a Distributed Online learning-based Multi-Estimate (DOME) fusion algorithm is proposed, which performs a collaborative weighted fusion of local and socially shared predictions. The fusion weights are adapted online using feedback from a prediction loss. Theoretical analysis establishes that conditional expectations of the fusion weights converge under reasonable assumptions. Simulation studies demonstrate that DOME outperforms both covariance-based and online learning-based decentralized fusion baselines, achieving $84.15\%$ and $78.12\%$ lower prediction loss in performance and scalability tests, respectively -- particularly under conditions involving significant model drift and communication unreliability. Further, DOME fusion is implemented in a ROS-Gazebo simulation environment.
URL: https://openreview.net/forum?id=aF5PHD6vll
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Title: Revisit, Extend, and Enhance Hessian-Free Influence Functions
Abstract: Influence functions serve as crucial tools for assessing sample influence. By employing the first-order Taylor expansion, sample influence can be estimated without the need for expensive model retraining. However, applying influence functions directly to deep models presents challenges, primarily due to the non-convex nature of the loss function and the large size of model parameters. This difficulty not only makes computing the inverse of the Hessian matrix costly but also renders it non-existent in some cases. In this paper, we revisit a Hessian-free method, which substitutes the inverse of the Hessian matrix with an identity matrix, and offer deeper insights into why this straightforward approximation method is effective. Furthermore, we extend its applications beyond measuring model utility to include considerations of fairness and robustness. Finally, we enhance this method through an ensemble strategy. To validate its effectiveness, we conduct experiments on synthetic data and extensive evaluations on noisy label detection, sample selection for large language model fine-tuning, and defense against adversarial attacks.
URL: https://openreview.net/forum?id=ijL2681Tau
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