In this paper we address the problem of discovering causal relationships from observational event sequence data. Existing methods typically assume that events are instantaneous point events, however in many real-world settings, events have duration. For example, in healthcare, a patient's symptoms may persist over a time interval and influence clinical actions while ongoing. To address this, we introduce a causal model for interval-based event sequences that captures rich causal structures, including interactions between events and causal mechanisms that depend on whether other events are ongoing. We prove that our model is identifiable in the limit and present a practical causal discovery algorithm, Niagara, grounded in the algorithmic Markov condition. To select among candidate models, we employ a minimum description length (MDL) criterion, enabling robust inference even with limited data. We validate our approach on synthetic and real data and demonstrate its utility on a real-world medical case study, where it uncovers meaningful causal relationships from noisy, interval-based event data.
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CATAL: Causally Disentangled Task Representation Learning for Offline Meta-Reinforcement Learning
PDF ↗Context-based Offline Meta Reinforcement Learning (COMRL) has shown promising results in improving the cross-task generalization ability of meta-policies. However, current methods often lead to entangled task representations, in which each latent dimension is influenced by multiple causal factors that govern variations in environment dynamics and reward mechanisms. This entanglement can degrade generalization performance, particularly when multiple causal factors vary simultaneously across tasks. To address this limitation, we propose CAusally disentangled TAsk representation Learning (CATAL) method for COMRL that aims to improve the generalization ability of the meta-policy, where each latent dimension in the task representations aligns to a single causal factor.Theoretically, we show that under mild conditions, the task representations learned by CATAL are causally disentangled. Empirically, extensive results on multi-task MuJoCo benchmarks show that CATAL consistently outperforms existing COMRL baselines in both in-distribution and out-of-distribution generalization.
Gradient Boosting Decision Trees (GBDTs) are widely used in industry and academia for their high accuracy and efficiency, particularly on structured data. However, the subject of watermarking GBDT models remains underexplored, especially compared to neural networks. In this work, we present the first robust watermarking framework tailored to GBDT models, utilizing in-place fine-tuning to embed imperceptible and resilient watermarks. We propose four embedding strategies, each designed to minimize impact on model accuracy while ensuring watermark robustness. Through experiments across diverse datasets, we demonstrate that our methods achieve high watermark embedding rates, low accuracy degradation, and strong resistance to post-deployment fine-tuning.
Continual test-time adaptation (CTTA) enables online model adjustment under dynamic distribution shifts in real-world environments. However, most existing CTTA frameworks adopt fixed model architectures, lacking the structural flexibility required for deployment across heterogeneous edge devices with varying computational capacities. To address this, we propose an elastic framework for edge CTTA that performs resource-aware dynamic model search based on a pre-trained binary Supernet. This enables architectural flexibility by generating personalized models tailored to the resource constraints of different edge devices. Considering the evolving distribution of unlabeled data on edge devices during deployment, we introduce a pluggable lightweight fine-tuning mechanism. By inserting low-rank adapters into the frozen binary backbone, the model enables continual self-supervised adaptation with minimal computational overhead. In addition, we propose a structure-aware knowledge reflux mechanism that transfers the adaptation experience from fine-tuned edge models back into the Supernet. By distilling knowledge into structurally aligned Supernet paths, future architecture search is improved without requiring retraining. Experiments on multiple benchmarks validate that our method achieves state-of-the-art performance while significantly reducing resource consumption, with re-searched models after knowledge reflux showing further improvements.
Clustering approaches that utilize convex loss functions have recently attracted growing interest in the formation of compact data clusters. Although classical methods like kmeans and its wide family of variants are still widely used, all of them require the number of clusters (k) to be supplied as input, and many are notably sensitive to initialization. Convex clustering provides a more stable alternative by formulating the clustering task as a convex optimization problem, ensuring a unique global solution. However, it faces challenges in handling high dimensional data, especially in the presence of noise and outliers. Additionally, strong fusion regularization, controlled by the tuning parameter, can hinder effective cluster formation within a convex clustering framework. To overcome these challenges, we introduce a robust approach that integrates convex clustering with the Median of Means (MoM) estimator, thus developing an outlier resistant and efficient clustering framework that does not necessitate a prior knowledge of the number of clusters. By leveraging the robustness of MoM alongside the stability of convex clustering, our method enhances both performance and efficiency, especially on large scale datasets. Theoretical analysis demonstrates weak consistency under specific conditions, while experiments on synthetic and real world datasets validate the method’s superior performance compared to existing approaches.
T3Time: Tri-Modal Time Series Forecasting via Adaptive Multi-Head Alignment and Residual Fusion
PDF ↗Multivariate time series forecasting (MTSF) seeks to model temporal dynamics among variables to predict future trends. Transformer-based models and large language models (LLMs) have shown promise due to their ability to capture long-range dependencies and patterns. However, current methods often rely on rigid inductive biases, ignore intervariable interactions, or apply static fusion strategies that limit adaptability across forecast horizons. These limitations create bottlenecks in capturing nuanced, horizon-specific relationships in time-series data. To solve this problem, we propose T3Time, a novel trimodal framework consisting of time, spectral, and prompt branches, where the dedicated frequency encoding branch captures the periodic structures along with a gating mechanism that learns prioritization between temporal and spectral features based on the prediction horizon. We also proposed a mechanism which adaptively aggregates multiple cross-modal alignment heads by dynamically weighting the importance of each head based on the features. Extensive experiments on benchmark datasets demonstrate that our model consistently outperforms state-of-the-art baselines, achieving an average reduction of 3.28% in MSE and 2.29% in MAE. Furthermore, it shows strong generalization in few-shot learning settings: with 5% training data, we see a reduction in MSE and MAE by 4.13% and 1.91%, respectively; and with 10% data, by 3.62% and 1.98% on average. Code will be released upon publication.
Deploying Vision Transformers (ViTs) in real-world multi-task learning remains challenging due to their massive computational costs and the difficulty of pruning shared backbones without harming task performance. Single-task pruning often causes destructive interference by discarding weights critical to other tasks, while existing multi-task pruning strategies remain costly and unscalable for billion-parameter models. We propose Multi-LoRA Guided Importance Consensus (LoGIC), a unified framework for efficient and robust multi-task ViT pruning. LoGIC follows a two-phase procedure: (i) task-consistent pruning of LoRA modules, guided by a task-adaptive gating mechanism that balances shared and task-specific contributions while enforcing structured sparsity for deployment; and (ii) cross-task consensus pruning of the frozen ViT backbone, which retains both universally shared and task-specialized capabilities, enabling aggressive sparsity without sacrificing accuracy. Across five diverse vision benchmarks, LoGIC achieves up to 50% structured sparsity while maintaining competitive accuracy and surpassing all baselines.
Open-set semi-supervised learning (OSSL) leverages unlabeled data containing both in-distribution (ID) and unknown out-of-distribution (OOD) samples, aiming simultaneously to improve closed-set accuracy and detect novel OOD instances. Existing methods either discard valuable information from uncertain samples or force-align every unlabeled sample into one or a few synthetic “catch-all” representations, resulting in geometric collapse and overconfidence on only seen OODs. To address the limitations, we introduce selective non-alignment, adding a novel “skip” operator into conventional pull and push operations of contrastive learning. Our framework, SkipAlign, selectively skips alignment (pulling) for low-confidence unlabeled samples, retaining only gentle repulsion against ID prototypes. This approach transforms uncertain samples into a pure repulsion signal, resulting in tighter ID clusters and naturally dispersed OOD features. Extensive experiments demonstrate that SkipAlign significantly outperforms state-of-the-art methods in detecting unseen OOD data without sacrificing ID classification accuracy.
Over-smoothing in Graph Neural Networks (GNNs) causes collapse in distinct node features, particularly on heterophilic graphs where adjacent nodes often have dissimilar labels. Although sheaf neural networks partially mitigate this problem, they typically rely on static or heavily parameterized sheaf structures that hinder generalization and scalability. Existing sheaf-based models either predefine restriction maps or introduce excessive complexity, yet fail to provide rigorous stability guarantees. In this paper, we introduce a novel scheme called SGPC (Sheaf GNNs with PAC-Bayes Calibration), a unified architecture that combines cellular-sheaf message passing with several mechanisms, including optimal transport-based lifting, variance-reduced diffusion, and PAC-Bayes spectral regularization for robust semi-supervised node classification. We establish performance bounds theoretically and demonstrate that end-to-end training in linear computational complexity can achieve the resulting bound-aware objective. Experiments on nine homophilic and heterophilic benchmarks show that SGPC outperforms state-of-the-art spectral and sheaf-based GNNs while providing certified confidence intervals on unseen nodes.
Tabular data synthesis is a key technique for protecting data privacy and addressing class imbalance, yet existing generative models struggle to capture the complex intrinsic structure of the data. To overcome this limitation, we propose TabGeoFlow, a novel geometric flow matching model for tabular data synthesis. The core innovation of TabGeoFlow is the injection of an explicit geometric inductive bias into the conditional flow matching framework. We decompose the learned vector field into local tangent and normal components of the data manifold. By dynamically suppressing the predicted normal component via a controlling loss function, we constrain the generative path to follow the data's intrinsic structure. Implemented with a shared backbone for parameter efficiency, TabGeoFlow achieves competitive or better fidelity and utility, while exhibiting near random black box MIA accuracy and DCR ≈ 50%, suggesting reduced memorization without sacrificing quality.
Are Graph Transformers Necessary? Efficient Long-Range Message Passing with Fractal Nodes in MPNNs
PDF ↗Graph Neural Networks (GNNs) have emerged as powerful tools for learning on graph-structured data, but often struggle to balance local and global information. While graph Transformers aim to address this by enabling long-range interactions, they often overlook the inherent locality and efficiency of Message Passing Neural Networks (MPNNs). We propose a new concept called 'fractal nodes', inspired by the fractal structure observed in real-world networks. Our approach is based on the intuition that graph partitioning naturally induces fractal structure, where subgraphs often reflect the connectivity patterns of the full graph. Fractal nodes are designed to coexist with the original nodes and adaptively aggregate subgraph-level feature representations, thereby enforcing feature similarity within each subgraph. We show that fractal nodes alleviate the over-squashing problem by providing direct shortcut connections that enable long-range propagation of subgraph-level representations. Experiment results show that our method improves the expressive power of MPNNs and achieves comparable or better performance to graph Transformers while maintaining the computational efficiency of MPNN by improving the long-range dependencies of MPNN.
Departures: Distributional Transport for Single-Cell Perturbation Prediction with Neural Schrödinger Bridges
PDF ↗Predicting single-cell perturbation outcomes directly advances gene function analysis and facilitates drug candidate selection, making it a key driver of both basic and translational biomedical research. However, a major bottleneck in this task is the unpaired nature of single-cell data, as the same cell cannot be observed both before and after perturbation due to the destructive nature of sequencing. Although some neural generative transport models attempt to tackle unpaired single-cell perturbation data, they either lack explicit conditioning or depend on prior spaces for indirect distribution alignment, limiting precise perturbation modeling. In this work, we approximate Schrödinger Bridge (SB), which defines stochastic dynamic mappings recovering the entropy-regularized optimal transport (OT), to directly align the distributions of control and perturbed single-cell populations across different perturbation conditions. Unlike prior SB approximations that rely on bidirectional modeling to infer optimal source-target sample coupling, we leverage Minibatch-OT based pairing to avoid such bidirectional inference and the associated ill-posedness of defining the reverse process. This pairing directly guides bridge learning, yielding a scalable approximation to the SB. We approximate two SB models, one modeling discrete gene activation states and the other continuous expression distributions. Joint training enables accurate perturbation modeling and captures single-cell heterogeneity. Experiments on public genetic and drug perturbation datasets show that our model effectively captures heterogeneous single-cell responses and achieves state-of-the-art performance.
We study active mitigation of selection bias in statistical learning. That is sequential maximization over a set A of the expectation of a reward function R(a,X) w.r.t. a r.v. X drawn from a target distribution PT possibly different from the (supposedly dominating) source distribution PS under which rewards are observed. The importance function dPT/dPS (x) with which the sequentially observed biased rewards should be ideally weighted being unknown in practice, auxiliary information is assumed to be available in the form of known moments of the target distribution PT for debiasing purposes. In the batch setting, this problem has already been studied and can be solved under certain conditions in two successive steps: 1) identify a weight function so as to approximate the moments 2) maximize the resulting (empirical version of the) weighted reward. In the active setting, if the problem boils down to identifying the best arm in a stochastic multi armed bandit (MAB) model, the presence of selection bias strongly affects the complexity of the sequential optimization problem and requires the development of a new algorithmic approach, as we show here. In a fixed confidence setting, we introduce a novel notion of complexity, which accounts for the balance between arm evaluation and (parametric) weight function estimation, establish lower bounds and propose an algorithm proved to be near optimal. Theoretical guarantees are backed up by numerical results.
Low-rank tensor decompositions (TDs) provide an effective framework for multiway data analysis. Traditional TD methods rely on predefined structural assumptions, such as CP or Tucker decompositions. From a probabilistic perspective, these methods effectively model the relationships between latent factors and the low-rank tensor using Dirac delta distributions. However, tensor low-rank decomposition is inherently non-unique, leading to a multimodal distribution over possible solutions. Critically, such prior knowledge is rarely available in practical scenarios, particularly regarding the optimal rank structure and contraction rules. To address this issue, we propose a score-based model that eliminates the need for predefined structural or distributional assumptions, enabling the learning of compatibility between tensors and latent factors. Specifically, a neural network is designed to learn the energy function, which is optimized via score matching to capture the gradient of the joint log-probability of tensor entries and latent factors. Our method allows for modeling structures and distributions beyond the Dirac delta assumption. Moreover, integrating the block coordinate descent (BCD) algorithm with the proposed smooth regularization enables the model to perform both tensor completion and denoising. Experimental results demonstrate significant performance improvements across various tensor types, including sparse and continuous-time tensors, as well as visual data.
Large language models performing chain-of-thought (CoT) reasoning generate extensive intermediate sequences that consume substantial memory through key-value (KV) cache storage. Unlike conventional text generation, reasoning sequences exhibit unique characteristics, including repetitive logic patterns and low information density, making existing KV cache compression methods suboptimal. We propose DesireKV, a novel compression framework that first constructs a two-dimensional coordinate system based on attention-derived importance and outlier-based quantization sensitivity. It then applies a dedicated protection mechanism for tokens critical to the reasoning process itself. Our approach makes differentiated compression decisions: retaining important and sensitive tokens, quantizing important but insensitive tokens, and evicting unimportant tokens. Through comprehensive evaluation on reasoning benchmarks, we demonstrate that DesireKV achieves up to 2.93× throughput improvement while maintaining nearly 99% of original reasoning accuracy.
Sample-and-Search: An Effective Algorithm for Learning-Augmented k-Median Clustering in High Dimensions
PDF ↗In this paper, we investigate the learning-augmented k-median clustering problem, which aims to improve the performance of traditional clustering algorithms by preprocessing the point set with a predictor of error rate α ∈ [0,1). This preprocessing step assigns potential labels to the points before clustering. We introduce an algorithm for this problem based on a simple yet effective sampling method, which substantially improves upon the time complexities of existing algorithms. Moreover, we mitigate their exponential dependency on the dimensionality of the Euclidean space. Lastly, we conduct experiments to compare our method with several state-of-the-art learning-augmented k-median clustering methods. The experimental results suggest that our proposed approach can significantly reduce the computational complexity in practice, while achieving a lower clustering cost.
Personalized Federated Learning with Bidirectional Communication Compression via One-Bit Random Sketching
PDF ↗Federated Learning (FL) enables collaborative training across decentralized data, but faces key challenges of bidirectional communication overhead and client-side data heterogeneity. To address communication costs while embracing data heterogeneity, we propose pFed1BS, a novel personalized federated learning framework that achieves extreme communication compression through one-bit random sketching. In personalized FL, the goal shifts from training a single global model to creating tailored models for each client. In our framework, clients transmit highly compressed one-bit sketches, and the server aggregates and broadcasts a global one-bit consensus. To enable effective personalization, we introduce a sign-based regularizer that guides local models to align with the global consensus while preserving local data characteristics. To mitigate the computational burden of random sketching, we employ the Fast Hadamard Transform for efficient projection. Theoretical analysis guarantees that our algorithm converges to a stationary neighborhood of the global potential function. Numerical simulations demonstrate that pFed1BS substantially reduces communication costs while achieving competitive performance compared to advanced communication-efficient FL algorithms.
LSHFed: Robust and Communication-Efficient Federated Learning with Locally-Sensitive Hashing Gradient Mapping
PDF ↗Federated learning (FL) enables collaborative model training across distributed nodes without exposing raw data, but its decentralized nature makes it vulnerable in trust-deficient environments. Inference attacks may recover sensitive information from gradient updates, while poisoning attacks can degrade model performance or induce malicious behaviors. Existing defenses often suffer from high communication and computation costs, or limited detection precision. To address these issues, we propose LSHFed, a robust and communication-efficient FL framework that simultaneously enhances aggregation robustness and privacy preservation. At its core, LSHFed incorporates LSHGM, a novel gradient verification mechanism that projects high-dimensional gradients into compact binary representations via multi-hyperplane locality-sensitive hashing. This enables accurate detection and filtering of malicious gradients using only their irreversible hash forms, thus mitigating privacy leakage risks and substantially reducing transmission overhead. Extensive experiments demonstrate that LSHFed maintains high model performance even when up to 50% of participants are collusive adversaries, while achieving up to a 1000× reduction in gradient verification communication compared to full-gradient methods.
Self-supervised graph representation learning (GRL) typically generates paired graph augmentations from each graph to infer similar representations for augmentations of the same graph, but distinguishable representations for different graphs. While effective augmentation requires both semantics-preservation and data-perturbation, most existing GRL methods focus solely on data-perturbation, leading to suboptimal solutions. To fill the gap, in this paper, we propose a novel method, Explanation-Preserving Augmentation (EPA), which leverages graph explanation for semantics-preservation. EPA first uses a small number of labels to train a graph explainer, which infers the subgraphs that explain the graph’s label. Then these explanations are used for generating semantics-preserving augmentations for boosting self-supervised GRL. Thus, the entire process, namely EPA-GRL, is semi-supervised. We demonstrate theoretically, using an analytical example, and through extensive experiments on a variety of benchmark datasets, that EPA-GRL outperforms the state-of-the-art (SOTA) GRL methods that use semantics-agnostic augmentations.
The goal of this work is to adapt Segment Anything Models (SAM) into crack segmentation tasks via automatic label generation, thus eliminating manual annotation cost. In this regard, an intuitive approach is to extract edges of crack samples and generate labels via the dilation and erosion processes for fine-tuning SAM. However, this simple solution cannot guarantee the quality of generated labels, as crack regions will be corrupted due to the imperfect edge detection. To this end, this paper proposes CoGenSAM, a novel Codebook-interactive Generative Labeling framework that enables an annotation-free SAM fine-tuning. To achieve this, in the first stage, we pre-train a vector-quantized variational auto-encoder (VQVAE) by reconstructing the synthesized crack-like structures for learning crack-aware priors within the codebook. In the second stage, these priors help another VQVAE serve as the restoration model to restore the randomly corrupted structures into uncorrupted ones. Specifically, we propose the crack-aware contrastive-interaction to maximize the mutual information with the above priors via codebook interaction. Then, high-quality labels can be generated by restoring corrupted labels from edge detection, contributing to an annotation-free SAM fine-tuning. We collect a new dataset, Bridge2025, to address the limited availability of related bridge-oriented benchmarks. Experiments show that our performance is close to fully-supervised methods.