Daily TMLR digest for Dec 14, 2025
TMLR
New certifications
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J2C Certification: Designing a Conditional Prior Distribution for Flow-Based Generative Models
Noam Issachar, Mohammad Salama, Raanan Fattal, Sagie Benaim
https://openreview.net/forum?id=Teh9Bq4giF
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Accepted papers
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Title: Hypergraphs as Weighted Directed Self-Looped Graphs: Spectral Properties, Clustering, Cheeger Inequality
Authors: Zihao Li, Dongqi Fu, Hengyu Liu, Jingrui He
Abstract: Hypergraphs naturally arise when studying group relations and have been widely used in the field of machine learning.
To the best of our knowledge, the recently proposed edge-dependent vertex weights (EDVW) modeling is one of the most generalized modeling methods of hypergraphs, i.e., most existing hypergraph conceptual modeling methods can be generalized as EDVW hypergraphs without information loss.
However, the relevant algorithmic developments on EDVW hypergraphs remain nascent: compared to the spectral theories for graphs, its formulations are incomplete, the spectral clustering algorithms are not well-developed, and the hypergraph Cheeger Inequality is not well-defined.
To this end, deriving a unified random walk-based formulation, we propose our definitions of hypergraph Rayleigh Quotient, NCut, boundary/cut, volume, and conductance, which are consistent with the corresponding definitions on graphs.
Then, we prove that the normalized hypergraph Laplacian is associated with the NCut value, which inspires our proposed HyperClus-G algorithm for spectral clustering on EDVW hypergraphs.
Finally, we prove that HyperClus-G can always find an approximately linearly optimal partitioning in terms of both NCut and conductance.
Additionally, we provide extensive experiments to validate our theoretical findings from an empirical perspective.
URL: https://openreview.net/forum?id=xLWhuCXWiM
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Title: Designing a Conditional Prior Distribution for Flow-Based Generative Models
Authors: Noam Issachar, Mohammad Salama, Raanan Fattal, Sagie Benaim
Abstract: Flow-based generative models have recently shown impressive performance for conditional generation tasks, such as text-to-image generation. However, current methods transform a general unimodal noise distribution to a specific mode of the target data distribution. As such, every point in the initial source distribution can be mapped to every point in the target distribution, resulting in long average paths. To this end, in this work, we tap into a non-utilized property of conditional flow-based models: the ability to design a non-trivial prior distribution. Given an input condition, such as a text prompt, we first map it to a point lying in data space, representing an “average" data point with the minimal average distance to all data points of the same conditional mode (e.g., class). We then utilize the flow matching formulation to map samples from a parametric distribution centered around this point to the conditional target distribution. Experimentally, our method significantly improves training times and generation efficiency (FID, KID and CLIP alignment scores) compared to baselines, producing high quality samples using fewer sampling steps. Code is available at https://github.com/MoSalama98/conditional-prior-flow-matching.
URL: https://openreview.net/forum?id=Teh9Bq4giF
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Title: Towards Efficient Training of Graph Neural Networks: A Multiscale Approach
Authors: Eshed Gal, Moshe Eliasof, Carola-Bibiane Schönlieb, Ivan Kyrchei, Eldad Haber, Eran Treister
Abstract: Graph Neural Networks (GNNs) have become powerful tools for learning from graph-structured data, finding applications across diverse domains. However, as graph sizes and connectivity increase, standard GNN training methods face significant computational and memory challenges, limiting their scalability and efficiency.
In this paper, we present a novel framework for efficient multiscale training of GNNs. Our approach leverages hierarchical graph representations and subgraphs, enabling the integration of information across multiple scales and resolutions. By utilizing coarser graph abstractions and subgraphs, each with fewer nodes and edges, we significantly reduce computational overhead during training. Building on this framework, we propose a suite of scalable training strategies, including coarse-to-fine learning, subgraph-to-full-graph transfer, and multiscale gradient computation.
We also provide some theoretical analysis of our methods and demonstrate their effectiveness across various datasets and learning tasks. Our results show that multiscale training can substantially accelerate GNN training for large scale problems while maintaining, or even improving, predictive performance.
URL: https://openreview.net/forum?id=2eZ8xkL2ZB
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Title: Incorporating Interventional Independence Improves Robustness against Interventional Distribution Shift
Authors: Gautam Sreekumar, Vishnu Boddeti
Abstract: We consider the problem of learning robust discriminative representations of causally related latent variables given the underlying directed causal graph and a training set comprising passively collected observational data and interventional data obtained through targeted interventions on some of these latent variables. We desire to learn representations that are robust against the resulting interventional distribution shifts. Existing approaches treat interventional data like observational data and ignore the independence relations that arise from these interventions, even when the underlying causal model is known. As a result, their representations lead to large disparities in predictive performance between observational and interventional data. This performance disparity worsens when interventional training samples are scarce. In this paper, (1) we first identify a strong correlation between this performance disparity and the representations' violation of statistical independence induced during interventions. (2) For linear models, we derive sufficient conditions on the proportion of interventional training data, for which enforcing statistical independence between representations of the intervened node and its non-descendants during interventions lowers the test-time error on interventional data. Combining these insights, (3) we propose RepLIn, a training algorithm that explicitly enforces this statistical independence between representations during interventions. We demonstrate the utility of RepLIn on a synthetic dataset, and on real image and text datasets on facial attribute classification and toxicity detection, respectively, with semi-synthetic causal structures. Our experiments show that RepLIn is scalable with the number of nodes in the causal graph and is suitable to improve robustness against interventional distribution shifts of both continuous and discrete latent variables compared to the ERM baselines.
URL: https://openreview.net/forum?id=kXfcEyNIrf
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Title: Consistency Aware Robust Learning under Noisy Labels
Authors: Fahad Sarfraz, Bahram Zonooz, Elahe Arani
Abstract: Deep neural networks (DNNs) often struggle with noisy supervision, a common challenge in real-world datasets where high-quality annotations are scarce. While DNNs tend to memorize noisy labels, the human brain excels at learning in noisy environments by modulating sensitivity to errors based on their magnitude and consistency. Inspired by this, we propose Consistency-Aware Robust Learning (CARoL), which maintains a memory of past predictions and errors to quantify consistency and guide the learning process. CARoL employs a principled mechanism to distinguish clean from noisy samples and modulates rate of adaptation based on prediction consistency. Furthermore, it integrates multiple learning pathways to fully utilize the dataset, adapting to sample characteristics as training progresses. Our empirical evaluation shows that CARoL achieves high precision in noisy label detection, enhances robustness, and performs reliably under severe noise, highlighting the potential of biologically inspired approaches for robust learning.
URL: https://openreview.net/forum?id=pZulfLkARr
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New submissions
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Title: Scalable Constrained Multi-Agent Reinforcement Learning via State Augmentation and Consensus for Separable Dynamics
Abstract: We present a distributed approach for constrained Multi-Agent Reinforcement Learning (MARL) which combines learning of policies with augmented state and distributed coordination of dual variables through consensus. Our method addresses a specific class of problems in which the agents have separable dynamics and local observations, but need to collectively satisfy constraints on global resources. The main technical contribution of the paper consists of the integration of constrained single agent RL (with state augmentation) in a multi-agent environment, through a distributed consensus over the Lagrange multipliers. This enables independent training of policies while maintaining coordination during execution. Unlike other centralized training with decentralized execution (CTDE) approaches that scale sub optimally with the number of agents, our method achieves a linear scaling both in training and execution by exploiting the separable structure of the problem. Each agent trains an augmented policy with local estimates of the global dual variables, and then coordinates through neighbor to neighbor communication on an undirected graph to reach consensus on constraint satisfaction. We show that, under mild connectivity assumptions, the agents obtain a bounded consensus error, ensuring a collective near-optimal behaviour. Experiments on demand response in smart grids show that our consensus mechanism is critical for feasibility: without it, the agents postpone demand indefinitely despite meeting consumption constraints.
URL: https://openreview.net/forum?id=whihxstZcO
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Title: Interactive Query Answering on Knowledge Graphs with Soft Entity Constraints
Abstract: Methods for query answering over incomplete knowledge graphs retrieve entities that are \emph{likely} to be answers, which is particularly useful when such answers cannot be reached by direct graph traversal due to missing edges. However, existing approaches have focused on queries formalized using first-order-logic. In practice, many real-world queries involve constraints that are inherently vague or context-dependent, such as preferences for attributes or related categories. Addressing this gap, we introduce the problem of query answering with soft constraints. We formalize the problem and introduce two efficient methods designed to adjust query answer scores by incorporating soft constraints without disrupting the original answers to a query. These methods are lightweight, requiring tuning only two parameters or a small neural network trained to capture soft constraints while maintaining the original ranking structure. To evaluate the task, we extend existing QA benchmarks by generating datasets with soft constraints. Our experiments demonstrate that our methods can capture soft constraints while maintaining robust query answering performance and adding very little overhead. With our work, we explore a new and flexible way to interact with graph databases that allows users to specify their preferences by providing examples interactively.
URL: https://openreview.net/forum?id=Qb6vIM7MxE
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