Daily TMLR digest for Jan 20, 2026
TMLR
New certifications
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J2C Certification: Bi-level Hierarchical Neural Contextual Bandits for Online Recommendation
Yunzhe Qi, Yao Zhou, Yikun Ban, Allan Stewart, Chuanwei Ruan, Jiachuan He, Shishir Kumar Prasad, Haixun Wang, Jingrui He
https://openreview.net/forum?id=k3XsA75SGv
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Expert Certification: DREAMS: Preserving both Local and Global Structure in Dimensionality Reduction
Noël Kury, Dmitry Kobak, Sebastian Damrich
https://openreview.net/forum?id=xpGu3Sichc
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Accepted papers
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Title: Bi-level Hierarchical Neural Contextual Bandits for Online Recommendation
Authors: Yunzhe Qi, Yao Zhou, Yikun Ban, Allan Stewart, Chuanwei Ruan, Jiachuan He, Shishir Kumar Prasad, Haixun Wang, Jingrui He
Abstract: Contextual bandit algorithms aim to identify the optimal choice among a set of candidate arms, based on their contextual information. Among others, neural contextual bandit algorithms have demonstrated generally superior performance compared to conventional linear and kernel-based methods. Nevertheless, neural methods can be inherently unsuitable for handling a large number of candidate arms due to their high computational cost when performing principled exploration. Motivated by the widespread availability of arm category information (e.g., movie genres, retailer types), we formulate contextual bandits as a bi-level online recommendation problem, and propose a novel neural bandit framework, named $\text{H}_{2}\text{N-Bandit}$, which utilizes a bi-level hierarchical neural architecture to mitigate the substantial computational cost found in conventional neural bandit methods. To demonstrate its theoretical effectiveness, we provide regret analysis under general over-parameterization settings, along with a guarantee for category-level recommendation. To illustrate its effectiveness and efficiency, we conduct extensive experiments on multiple real-world data sets, highlighting that $\text{H}_{2}\text{N-Bandit}$ can significantly reduce the computational cost over existing strong non-linear baselines, while achieving better or comparable performance under online recommendation settings.
URL: https://openreview.net/forum?id=k3XsA75SGv
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Title: Causal Graph Learning via Distributional Invariance of Cause-Effect Relationship
Authors: Nang Hung Nguyen, Phi Le Nguyen, Thao Nguyen Truong, Trong Nghia Hoang, Masashi Sugiyama
Abstract: This paper introduces a new framework for recovering causal graphs from observational data, leveraging the fact that the distribution of an effect, conditioned on its causes, remains invariant to changes in the prior distribution of those causes. This insight enables a direct test for potential causal relationships by checking the variance of their corresponding effect-cause conditional distributions across multiple downsampled subsets of the data. These subsets are selected to reflect different prior cause distributions while preserving the effect-cause conditional relationships. Using this invariance test and exploiting an (empirical) sparsity of most causal graphs, we develop an algorithm that efficiently uncovers causal relationships with quadratic complexity in the number of observational variables, reducing the processing time by up to $25\times$ compared to state-of-the-art methods. Our empirical experiments on a varied benchmark of large-scale datasets show superior or equivalent performance compared to existing works, while achieving enhanced scalability.
URL: https://openreview.net/forum?id=Ey38Q882Xe
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Title: Accounting for Missing Covariates in Heterogeneous Treatment Estimation
Authors: Khurram Yamin, Vibhhu Sharma, Edward Kennedy, Bryan Wilder
Abstract: Many applications of causal inference require using treatment effects estimated on a study population to then make decisions for a separate target population that lacks treatment and outcome data. We consider the challenging setting where there are important covariates that are observed in the target population but are missing from the original study. Our goal is to estimate the tightest possible bounds on heterogeneous treatment effects conditioned on such newly observed covariates. We introduce a novel partial identification strategy based on ideas from ecological inference; the main idea is that estimates of conditional treatment effects for the full covariate set must marginalize correctly when restricted to only the covariates observed in both populations. Furthermore, we introduce a bias-corrected estimator for these bounds and prove that it enjoys fast convergence rates and statistical guarantees (e.g., asymptotic normality). Experimental results on both real and synthetic data demonstrate that our framework can produce bounds that are much tighter than would otherwise be possible.
URL: https://openreview.net/forum?id=05AIXzU4HV
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Title: Decoding Safety Feedback from Diverse Raters: A Data-driven Lens on Responsiveness to Severity
Authors: Pushkar Mishra, Charvi Rastogi, Stephen R Pfohl, Alicia Parrish, Tian Huey Teh, Roma Patel, Mark Diaz, Ding Wang, Michela Paganini, Vinodkumar Prabhakaran, Lora Aroyo, Verena Rieser
Abstract: Ensuring the safety of Generative AI requires a nuanced understanding of pluralistic viewpoints. In this paper, we introduce a novel data-driven approach for analyzing ordinal safety ratings in pluralistic settings. Specifically, we address the challenge of interpreting nuanced differences in safety feedback from a diverse population expressed via ordinal scales (e.g., a Likert scale). We define non-parametric responsiveness metrics that quantify how raters convey broader distinctions and granular variations in the severity of safety violations. Leveraging publicly available datasets of pluralistic safety feedback as our case studies, we investigate how raters from different demographic groups use an ordinal scale to express their perceptions of the severity of violations. We apply our metrics across violation types, demonstrating their utility in extracting nuanced insights that are crucial for aligning AI systems reliably in multi-cultural contexts. We show that our approach can inform rater selection and feedback interpretation by capturing nuanced viewpoints across different demographic groups, hence improving the quality of pluralistic data collection and in turn contributing to more robust AI alignment.
URL: https://openreview.net/forum?id=vx6qrM7VB5
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Title: SpikingMamba: Towards Energy-Efficient Large Language Models via Knowledge Distillation from Mamba
Authors: Yulong Huang, Jianxiong Tang, Chao Wang, Ziyi Wang, Jianguo Zhang, Zhichao Lu, Bojun Cheng, Luziwei Leng
Abstract: Large Language Models (LLMs) have achieved remarkable performance across tasks but remain energy-intensive due to dense matrix operations. Spiking neural networks (SNNs) improve energy efficiency by replacing dense matrix multiplications with sparse accumulations. Their sparse spike activity enables efficient LLMs deployment on edge devices. However, prior SNN-based LLMs often sacrifice performance for efficiency, and recovering accuracy typically requires full pretraining, which is costly and impractical. To address this, we propose SpikingMamba, an energy-efficient SNN-based LLMs distilled from Mamba that improves energy efficiency with minimal accuracy sacrifice. SpikingMamba integrates two key components: (a) SI-LIF, a signed-integer spiking neuron that preserves semantic polarity through signed multi-level spike representations. (b) A training-exclusive Smoothed Gradient Compensation (SGC) path mitigating quantization loss while preserving spike-driven efficiency. We employ a single-stage distillation strategy to transfer the zero-shot ability of pretrained Mamba and further enhance it via reinforcement learning (RL). Experiments show that SpikingMamba-1.3B achieves a 4.76$\times$ energy benefit, with only a 4.78\% zero-shot accuracy gap compared to the original Mamba. The model achieves a further 2.55\% accuracy improvement after RL, narrowing the performance gap from 4.78\% to 2.23\%.
URL: https://openreview.net/forum?id=uxb2jcCLxt
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Title: A Unified Framework for Tabular Generative Modeling: Loss Functions, Benchmarks, and Improved Multi-objective Bayesian Optimization Approaches
Authors: Minh Hoang Vu, Daniel Edler, Carl Wibom, Tommy Löfstedt, Beatrice Melin, Martin Rosvall
Abstract: Deep learning (DL) models require extensive data to achieve strong performance and generalization. Deep generative models (DGMs) offer a solution by synthesizing data. Yet current approaches for tabular data often fail to preserve feature correlations and distributions during training, struggle with multi-metric hyperparameter selection, and lack comprehensive evaluation protocols. We address this gap with a unified framework that integrates training, hyperparameter tuning, and evaluation. First, we introduce a novel correlation- and distribution-aware loss function that regularizes DGMs, enhancing their ability to generate synthetic tabular data that faithfully represents the underlying data distributions. Theoretical analysis establishes stability and consistency guarantees. To enable principled hyper-parameter search via Bayesian optimization (BO), we also propose a new multi-objective aggregation strategy based on iterative objective refinement Bayesian optimization (IORBO), along with a comprehensive statistical testing framework. We validate the proposed approach using a benchmarking framework with twenty real-world datasets and ten established tabular DGM baselines. The correlation-aware loss function significantly improves the synthetic data fidelity and downstream machine learning (ML) performance, while IORBO consistently outperforms standard Bayesian optimization (SBO) in hyper-parameter selection. The unified framework advances tabular generative modeling beyond isolated method improvements. Code is available at: https://github.com/vuhoangminh/TabGen-Framework.
URL: https://openreview.net/forum?id=RPZ0EW0lz0
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Title: Budget-Optimized Crowdworker Allocation
Authors: Sha Lai, Prakash Ishwar, Margrit Betke
Abstract: Due to concerns about human error in crowdsourcing, it is standard practice to collect labels for the same data point from multiple internet workers. We show that the resulting budget can be used more effectively with a flexible worker assignment strategy that asks fewer workers to analyze data that are easy to label and more workers to analyze data that requires extra scrutiny. Our main contribution is to show how the worker label aggregation can be formulated using a probabilistic approach, and how the allocations of the number of workers to a task can be computed optimally based on task difficulty alone, without using worker profiles. Our representative target task is identifying entailment between sentences. To illustrate the proposed methodology, we conducted simulation experiments that utilize a machine learning system as a proxy for workers and demonstrate its advantages over a state-of-the-art commercial optimizer.
URL: https://openreview.net/forum?id=hVpAgznRmp
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Title: GraphGini: Fostering Individual and Group Fairness in Graph Neural Networks
Authors: Anuj Kumar Sirohi, Anjali Gupta, Sandeep Kumar, Amitabha Bagchi, Sayan Ranu
Abstract: Graph Neural Networks (GNNs) have demonstrated impressive performance across various tasks, leading to their increased adoption in high-stakes decision-making systems. However, concerns have arisen about GNNs potentially generating unfair decisions for underprivileged groups or individuals when lacking fairness constraints. This work addresses this issue by introducing GraphGini, a novel approach that incorporates the Gini coefficient to enhance both individual and group fairness within the GNN framework. We rigorously establish that the Gini coefficient offers greater robustness and promotes equal opportunity among GNN outcomes, advantages not afforded by the prevailing Lipschitz constant methodology. Additionally, we employ the Nash social welfare program to ensure our solution yields a Pareto optimal distribution of group fairness. Extensive experimentation on real-world datasets demonstrates GraphGini's efficacy in significantly improving individual fairness compared to state-of-the-art methods while maintaining utility and group fairness.
URL: https://openreview.net/forum?id=IEVGBI9MiL
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Title: DREAMS: Preserving both Local and Global Structure in Dimensionality Reduction
Authors: Noël Kury, Dmitry Kobak, Sebastian Damrich
Abstract: Dimensionality reduction techniques are widely used for visualizing high-dimensional data in two dimensions. Existing methods are typically designed to preserve either local (e.g., $t$-SNE, UMAP) or global (e.g., MDS, PCA) structure of the data, but none of the established methods can represent both aspects well. In this paper, we present DREAMS (Dimensionality Reduction Enhanced Across Multiple Scales), a method that combines the local structure preservation of $t$-SNE with the global structure preservation of PCA via a simple regularization term. Our approach generates a spectrum of embeddings between the locally well-structured $t$-SNE embedding and the globally well-structured PCA embedding, efficiently balancing both local and global structure preservation. We benchmark DREAMS across eleven real-world datasets, showcasing qualitatively and quantitatively its superior ability to preserve structure across multiple scales compared to previous approaches.
URL: https://openreview.net/forum?id=xpGu3Sichc
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New submissions
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Title: What Constrains Adaptation After Pretraining? Generalization and Specialization Under Inherited Data Manifolds
Abstract: Large language models are often adapted to new tasks through supervised fine tuning. In deployment, however, their generalization can be unreliable and hard to anticipate. We examine whether such failures arise from limitations in optimization and supervision, or from geometric constraints inherited from pretraining, noting that data organization in representation space is rarely treated as an explicit control variable. Using controlled sampling from a large text distribution drawn from the web, we treat training samples as structured populations in representation space. We then compare data drawn from central and peripheral regions of the inherited manifold under identical architectures and training procedures. We find that data location in representation space strongly constrains what can be learned, frequently necessitating specialization across both general and domain-specific settings. Models trained on data drawn from peripheral or highly overlapping regions tend to generalize poorly, even when the training setup is otherwise unchanged. This pattern points to the need for principled specialization to meet practical demands on reliability, efficiency, and deployment.
URL: https://openreview.net/forum?id=9bRPGNcNLy
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Title: Querying Kernel Methods Suffices for Reconstructing their Training Data
Abstract: Over-parameterized models have raised concerns about their potential to memorize training data, even when achieving strong generalization. The privacy implications of such memorization are generally unclear, particularly in scenarios where only model outputs are accessible. We study this question in the context of kernel methods, and demonstrate both empirically and theoretically that querying kernel models at various points suffices to reconstruct their training data, even without access to model parameters. Our results hold for a range of kernel methods, including kernel regression, support vector machines, and kernel density estimation. Our hope is that this work can shed light on potential privacy concerns associated with such models.
URL: https://openreview.net/forum?id=qikuoG0Th2
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Title: From Mice to Trains: Amortized Bayesian Inference on Graph Data
Abstract: Graphs arise across diverse domains, from biology and chemistry to social and information networks, as well as in transportation and logistics. Inference on graph-structured data requires methods that are permutation-invariant, scalable across varying sizes and sparsities, and capable of capturing complex long-range dependencies, making posterior estimation on graph parameters particularly challenging.
Amortized Bayesian Inference (ABI) is a simulation-based framework that employs generative neural networks to enable fast, likelihood-free posterior inference. We adapt ABI to graph data to address these challenges to perform inference on node-, edge-, and graph-level parameters. Our approach couples permutation-invariant graph encoders with flexible neural posterior estimators in a two-module pipeline: a summary network maps attributed graphs to fixed-length representations, and an inference network approximates the posterior over parameters. In this setting, several neural architectures can serve as the summary network. In this work we evaluate multiple architectures and assess their performance on controlled synthetic settings and two real-world domains - biology and logistics - in terms of recovery and calibration.
URL: https://openreview.net/forum?id=vpIeCm7YEA
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Title: A Case for Vanilla SWD: New Perspectives on Informative Slices, Sliced-Wasserstein Distances, and Learning Rates
Abstract: The practical applications of Wasserstein distances (WDs) are constrained by their sample and computational complexities. Sliced-Wasserstein distances (SWDs) provide a workaround by projecting distributions onto one-dimensional subspaces, leveraging the more efficient, closed-form WDs for 1D distributions. However, in high dimensions, most random projections become uninformative due to the concentration of measure phenomenon. Although several SWD variants have been proposed to focus on informative slices, they often introduce additional complexity, numerical instability, and compromise desirable theoretical (metric) properties of SWD. Amid the growing literature that focuses on directly modifying the slicing distribution, which often face challenges, we revisit the standard, "vanilla" Sliced-Wasserstein and propose instead to rescale the 1D Wasserstein to make all slices equally informative. Importantly, we show that with an appropriate notion of slice informativeness, rescaling for all individual slices simplifies to a single global scaling factor on the SWD. This, in turn, translates to the standard learning rate search for gradient-based learning in common ML workflows. We perform extensive experiments across various machine learning tasks showing that vanilla SWD, when properly configured, can often match or surpass the performance of more complex variants.
URL: https://openreview.net/forum?id=li8D5pxczd
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Title: Neurons Speak in Ranges: Breaking Free from Discrete Neuronal Attribution
Abstract: Pervasive polysemanticity in large language models (LLMs) undermines discrete neuron–concept attribution, posing a significant challenge for model interpretation and control. We systematically analyze both encoder and decoder based LLMs across diverse datasets, and observe that even highly salient neurons for specific semantic concepts consistently exhibit polysemantic behavior.
Importantly, we uncover a consistent pattern: concept-conditioned activation magnitudes of neurons form distinct, often Gaussian-like distributions with minimal overlap. Building on this observation, we hypothesize that interpreting and intervening on concept-specific activation ranges can enable more precise interpretability and targeted manipulation in LLMs. To this end, we introduce NeuronLens, a novel range-based interpretation and manipulation framework, that localizes concept attribution to activation ranges within a neuron.
Extensive empirical evaluations show that range-based interventions enable effective manipulation of target concepts while causing substantially less collateral degradation to auxiliary concepts and overall model performance compared to neuron-level masking.
URL: https://openreview.net/forum?id=AukyIhfBuW
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Title: It depends: Incorporating correlations for joint aleatoric and epistemic uncertainties of high-dimensional output spaces
Abstract: Uncertainty Quantification plays a vital role in enhancing the reliability of deep learning model predictions, especially in scenarios with high-dimensional output spaces. This paper addresses the dual nature of uncertainty — aleatoric and epistemic — focusing on their joint integration in high-dimensional regression tasks. For example, in applications like medical image segmentation or restoration, aleatoric uncertainty captures inherent data noise, while epistemic uncertainty quantifies the model's confidence in unfamiliar conditions. Modeling both jointly enables more reliable predictions by reflecting both unavoidable variability and knowledge gaps, whereas modeling only one limits transparency and robustness. We propose a novel approach that approximates the resulting joint uncertainty using a low-rank plus diagonal covariance structure, capturing essential output correlations while avoiding the computational burdens of full covariance matrices. Unlike prior work, our method explicitly combines aleatoric and epistemic uncertainties into a unified second-order distribution that supports robust downstream analyses like sampling and log-likelihood evaluation. We further introduce stabilization strategies for efficient training and inference, achieving superior Uncertainty Quantification in the tasks of image inpainting, colorization, and optical flow estimation.
URL: https://openreview.net/forum?id=zw5EuUnBny
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Title: What One View Reveals, Another Conceals: 3D-Consistent Visual Reasoning with LLMs
Abstract: Maintaining semantic label consistency across multiple views is a persistent challenge in 3D semantic object detection. Existing zero-shot approaches that combine 2D detections with vision-language features often suffer from bias toward non-descriptive viewpoints and require a fixed label list to operate on. We propose a truly open-vocabulary algorithm that uses large language model (LLM) reasoning to relabel multi-view detections, mitigating errors from poor, ambiguous viewpoints and occlusions. Our method actively samples informative views based on feature diversity and uncertainty, generates new label hypotheses via LLM reasoning, and recomputes confidences to build a spatial-semantic representation of objects. Experiments on controlled single-object and multi-object scenes show double digit improvement, in accuracy and sampling rate over ubiquitous fusion methods using YOLO, and CLIP. We demonstrate in multiple cases that \textbf{L}LM-guided \textbf{A}ctive \textbf{D}etection and \textbf{R}easoning (LADR) balances detail preservation with reduced ambiguity and low sampling rate. We provide theoretical convergence analysis showing exponential convergence to a stable and correct semantic label.
URL: https://openreview.net/forum?id=w8rMmoynWi
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Title: Resolving Disagreement Problems in Explainable Artifi- cial Intelligence Through Multi-Criteria Decision Analysis
Abstract: Post-hoc explanation methods are critical for building trust in complex black-box artificial intelligence (AI) models; however, they often suffer from the disagreement problem, which provides conflicting explanations for the same prediction. This inconsistency undermines reliability and poses a significant barrier to adoption in high-stakes domains that demand trustworthiness and transparency. To address this, we move beyond the search for a single best method and instead propose a principled, preference-driven framework for selecting the best suitable explanation technique for a given context: \emph{which specific post-hoc explanation methods to use and when?} We formalize this selection process as a Multi-Criteria Decision Analysis (MCDA) problem. Our framework evaluates a set of state-of-the-art post-hoc explanation methods (e.g., LIME, SHAP, and Anchor) against six explanation evaluation metrics: fidelity, identity, stability, separability, faithfulness, and consistency. We then apply a suite of established MCDA techniques such as Simple Additive Weighting (SAW), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and Elimination and Choice Translating Reality (ELECTRE I) to aggregate these evaluations based on user-defined priorities. By comparing the rankings produced by these diverse decision logics across multiple predictive models and real-world datasets, we demonstrate not only how to select the optimal explanation method under different priority scenarios (e.g., favoring fidelity vs. stability) but also how to expose critical trade-offs that are invisible to simpler aggregation approaches. Our work provides a robust, transparent, and adaptable methodology for resolving explanation disagreement, enabling practitioners to make more justifiable choices about explainability.
URL: https://openreview.net/forum?id=9mTedhz3iG
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Title: Multimodal Deception in Explainable AI: Concept-Level Backdoor Attacks on Concept Bottleneck Models
Abstract: Deep learning has demonstrated transformative potential across domains, yet its inherent opacity has driven the development of Explainable Artificial Intelligence (XAI). Concept Bottleneck Models (CBMs), which enforce interpretability through human-understandable concepts, represent a prominent advancement in XAI. However, despite their semantic transparency, CBMs remain vulnerable to security threats such as backdoor attacks—malicious manipulations that induce controlled misbehaviors during inference. While CBMs leverage multimodal representations (visual inputs and textual concepts) to enhance interpretability, their dual-modality structure introduces unique, unexplored attack surfaces. To address this risk, we propose CAT (Concept-level Backdoor ATtacks), a methodology that injects stealthy triggers into conceptual representations during training. Unlike naive attacks that randomly corrupt concepts, CAT employs a sophisticated filtering mechanism to enable precise prediction manipulation without compromising clean-data performance. We further propose CAT+, an enhanced variant incorporating a concept correlation function to iteratively optimize trigger-concept associations, thereby maximizing attack effectiveness and stealthiness. Crucially, we validate our approach through a rigorous two-stage evaluation framework. First, we establish the fundamental vulnerability of the concept bottleneck layer in a controlled setting, showing that CAT+ achieves high attack success rates (ASR) while remaining statistically indistinguishable from natural data. Second, we demonstrate practical end-to-end feasibility via our proposed Image2Trigger_c method, which translates visual perturbations into concept-level triggers, achieving an end-to-end ASR of 53.29%. Extensive experiments show that CAT outperforms random-selection baselines significantly, and standard defenses like Neural Cleanse fail to detect these semantic attacks. This work highlights critical security risks in interpretable AI systems and provides a robust methodology for future security assessments of CBMs.
URL: https://openreview.net/forum?id=bntZBG9fBY
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Title: Kernel Neural Operators (KNOs) for Scalable, Memory-efficient, Geometrically-flexible Operator Learning
Abstract: This paper introduces the Kernel Neural Operator (KNO), a provably convergent operator-learning architecture that utilizes compositions of deep kernel-based integral operators for function-space approximation of operators (maps from functions to functions). The KNO decouples the choice of kernel from the numerical integration scheme (quadrature), thereby naturally allowing for operator learning with explicitly-chosen trainable kernels on irregular geometries. On irregular domains, this allows the KNO to utilize domain-specific quadrature rules. To help ameliorate the curse of dimensionality, we also leverage an efficient dimension-wise factorization algorithm on regular domains. More importantly, the ability to explicitly specify kernels also allows the use of highly expressive, non-stationary, neural anisotropic kernels whose parameters are computed by training neural networks. We present universal approximation theorems showing that both the continuous and fully discretized KNO are universal approximators on operator learning problems. Numerical results demonstrate that on existing benchmarks the training and test accuracy of KNOs is comparable to or higher than popular operator learning techniques while typically using an order of magnitude fewer trainable parameters, with the more expressive kernels proving important to attaining high accuracy. KNOs thus facilitate low-memory, geometrically-flexible, deep operator learning, while retaining the implementation simplicity and transparency of traditional kernel methods from both scientific computing and machine learning.
URL: https://openreview.net/forum?id=Q0C4jYZQ7x
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Title: Deep sprite-based image models: an analysis
Abstract: While foundation models drive steady progress in image segmentation and diffusion algorithms compose always more realistic images, the seemingly simple problem of identifying recurrent patterns in a collection of images remains very much open. In this paper, we focus on sprite-based image decomposition models, which have shown some promise for clustering and image decomposition and are appealing because of their high interpretability. These models come in different flavors, need to be tailored to specific datasets, and struggle to scale to images with many objects. We dive into the details of their design, identify their core components, and perform an extensive analysis on clustering benchmarks. We leverage this analysis to propose a deep sprite-based image decomposition method that performs on par with state-of-the-art unsupervised image segmentation methods on the standard CLEVR benchmark, scales linearly with the number of objects, identifies explicitly object categories, and fully models images in an easily interpretable way. Our code will be made publicly available.
URL: https://openreview.net/forum?id=pXuxMLFo9g
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Title: Implicit geometric regularization in flow matching via density weighted Stein operators
Abstract: Flow Matching (FM) has emerged as a powerful paradigm for continuous normalizing flows, yet standard FM implicitly performs an unweighted $L^2$ regression over the entire ambient space. In high dimensions, this leads to a fundamental inefficiency: the vast majority of the integration domain consists of low-density ``void'' regions where the target velocity fields are often chaotic or ill-defined.
In this paper, we propose {$\gamma$-Flow Matching ($\gamma$-FM)}, a density-weighted variant that aligns the regression geometry with the underlying probability flow.
While density weighting is desirable, naive implementations would require evaluating the intractable target density.
We circumvent this by introducing a Dynamic Density-Weighting strategy that estimates the target density directly from training particles.
This approach allows us to dynamically downweight the regression loss in void regions without compromising the simulation-free nature of FM.
Theoretically, we establish that $\gamma$-FM minimizes the transport cost on a statistical manifold endowed with the $\gamma$-Stein metric. Spectral analysis further suggests that this geometry induces an implicit Sobolev regularization, effectively damping high-frequency oscillations in void regions.
Empirically, $\gamma$-FM significantly improves vector field smoothness and sampling efficiency on high-dimensional latent datasets, while demonstrating intrinsic robustness to outliers.
URL: https://openreview.net/forum?id=LBlkVBDRdu
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Title: Graph-Based Operator Learning from Limited Data on Irregular Domains
Abstract: Operator learning seeks to approximate mappings from input functions to output solutions, particularly in the context of partial differential equations (PDEs). While recent advances such as DeepONet and Fourier Neural Operator (FNO) have demonstrated strong performance, they often rely on regular grid discretizations, limiting their applicability to complex or irregular domains. In this work, we propose a \textbf{G}raph-based \textbf{O}perator \textbf{L}earning with \textbf{A}ttention (GOLA) framework that addresses this limitation by constructing graphs from irregularly sampled spatial points and leveraging attention-enhanced Graph Neural Netwoks (GNNs) to model spatial dependencies with global information. To improve the expressive capacity, we introduce a Fourier-based encoder that projects input functions into a frequency space using learnable complex coefficients, allowing for flexible embeddings even with sparse or nonuniform samples. We evaluated our approach across a range of 2D PDEs, including Darcy Flow, Advection, Eikonal, and Nonlinear Diffusion, under varying sampling densities. Our method consistently outperforms baselines, particularly in data-scarce regimes, demonstrating strong generalization and efficiency on irregular domains.
URL: https://openreview.net/forum?id=H18BcuIq1i
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Title: FL-Sailer: Efficient and Privacy-Preserving Federated Learning for Scalable Single-Cell Epigenetic Data Analysis via Adaptive Sampling
Abstract: Single-cell ATAC-seq (scATAC-seq) enables high-resolution mapping of chromatin accessibility, yet privacy regulations and data size constraints hinder multi-institutional sharing. Federated learning (FL) offers a privacy-preserving alternative, but faces three fundamental barriers in scATAC-seq analysis: ultra-high dimensionality, extreme sparsity, and severe cross-institutional heterogeneity. We propose FL-Sailer, the first FL framework designed for scATAC-seq data. FL-Sailer integrates two key innovations: (i) adaptive leverage score sampling, which selects biologically interpretable features while reducing dimensionality by 80%, and (ii) an invariant VAE architecture, which disentangles biological signals from technical confounders via mutual information minimization. We provide a convergence guarantee, showing that FL-Sailer converges to an approximate solution of the original high-dimensional problem with bounded error. Extensive experiments on synthetic and real epigenomic datasets demonstrate that FL-Sailer not only enables previously infeasible multi-institutional collaborations but also surpasses centralized methods by leveraging adaptive sampling as an implicit regularizer to suppress technical noise. Our work establishes that federated learning, when tailored to domain-specific challenges, can become a superior paradigm for collaborative epigenomic research.
URL: https://openreview.net/forum?id=2vNebz5r4b
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Title: SAMAT: A Stereotype-Aware Multimodal Transformer for Interpretable Misogynistic Meme Detection
Abstract: This paper introduces SAMAT, a Stereotype-Aware Multimodal Alignment Transformer for detecting and explaining implicit misogyny in memes, where harm arises from subtle visual-textual incongruity and cultural stereotypes. SAMAT integrates three components: a Stereotype Subspace Projection Module (SSPM) that structures representations; a fidelity-based retrieval mechanism aligned with a curated Rationale Bank; and an evidence-conditioned explanation generator. For evaluation, we extend the MEE corpus with 8,000 explanations and define Stereotype Alignment (SAS) and Contextual Faithfulness (CFS) scores. Experiments show that SAMAT achieves a Macro-F1 of 87.3\%, surpassing MLLM baselines, while improving retrieval faithfulness (SAS: 0.78) and explanation grounding (CFS: 0.68). Ablations confirm gains stem from structured stereotype projection and evidential retrieval, not scale. SAMAT offers a transparent, culturally grounded framework for accountable content moderation, aligning with Responsible AI objectives.
URL: https://openreview.net/forum?id=1DJBFS7rLW
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Title: Explainable Error Detection in Integrated Circuits Image Segmentation via Graph Neural Networks
Abstract: Automated IC image segmentation for hardware assurance remains challenging due to nanoscale complexity, low error tolerance, and the limited interpretability of current deep-learning–based segmentation methods. Existing CNN-based error detectors analyze whole images, making it difficult to localize specific faults. We introduce an explainable GNN-based framework that converts each connected component of a segmentation mask into a feature-annotated graph, enabling localized reasoning and component-level error classification. This graph formulation allows the model to detect outlier components and precisely highlight erroneous regions. Experiments across diverse IC layouts and imaging conditions show that the method is robust, generalizable, and provides accurate, interpretable error detection.
URL: https://openreview.net/forum?id=q68B9mTom5
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Title: Drawback of Enforcing Equivariance and its Compensation via the Lens of Expressive Power
Abstract: Equivariant neural networks encode the intrinsic symmetry of data as an inductive bias, which has achieved impressive performance in wide domains. However, the understanding to their expressive power remains premature. Focusing on 2-layer ReLU networks, this paper investigates the impact of enforcing equivariance constraints on the expressive power. By examining the boundary hyperplanes and the channel vectors, we constructively demonstrate that enforcing equivariance constraints could undermine the expressive power. Naturally, this drawback can be compensated for by enlarging the model size -- we further prove upper bounds on the required enlargement for compensation. Surprisingly, we show that the enlarged neural architectures have reduced hypothesis space dimensionality, implying even better generalizability.
URL: https://openreview.net/forum?id=z5bJ44Brc4
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