Daily TMLR digest for May 24, 2025
TMLR
New certifications
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Expert Certification: Latent mixed-effect models for high-dimensional longitudinal data
Priscilla Ong, Manuel Haussmann, Otto Lönnroth, Harri Lähdesmäki
https://openreview.net/forum?id=7A96yteeF9
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Survey Certification: Conditional Image Synthesis with Diffusion Models: A Survey
Zheyuan Zhan, Defang Chen, Jian-Ping Mei, Zhenghe Zhao, Jiawei Chen, Chun Chen, Siwei Lyu, Can Wang
https://openreview.net/forum?id=ewwNKwh6SK
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Accepted papers
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Title: Latent mixed-effect models for high-dimensional longitudinal data
Authors: Priscilla Ong, Manuel Haussmann, Otto Lönnroth, Harri Lähdesmäki
Abstract: Modelling longitudinal data is an important yet challenging task. These datasets can be high-dimensional, contain non-linear effects and feature time-varying covariates. Gaussian process (GP) prior-based variational autoencoders (VAEs) have emerged as a promising approach due to their ability to model time-series data. However, they are costly to train and struggle to fully exploit the rich covariates characteristic of longitudinal data, making them difficult for practitioners to use effectively. In this work, we leverage linear mixed models (LMMs) and amortized variational inference to provide conditional priors for VAEs, and propose LMM-VAE, a scalable, interpretable and identifiable model. We highlight theoretical connections between it and GP-based techniques, providing a unified framework for this class of methods. Our proposal performs competitively compared to existing approaches across simulated and real-world datasets.
URL: https://openreview.net/forum?id=7A96yteeF9
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Title: Group Fair Federated Learning via Stochastic Kernel Regularization
Authors: Huzaifa Arif, Pin-Yu Chen, Keerthiram Murugesan, Alex Gittens
Abstract: Ensuring \textbf{group fairness} in federated learning (FL) presents unique challenges due to data heterogeneity and communication constraints. We propose Kernel Fair Federated Learning (\texttt{KFFL}), a novel framework that incorporates group fairness into FL models using the Kernel Hilbert-Schmidt Independence Criterion (KHSIC) as a fairness regularizer. To address scalability, \texttt{KFFL} approximates KHSIC with Random Feature Maps (RFMs), significantly reducing computational and communication overhead while achieving \textit{group fairness}.
To address the resulting non-convex optimization problem, we propose \texttt{FedProxGrad}, a federated proximal gradient algorithm that guarantees convergence. Through experiments on standard benchmark datasets across both IID and Non-IID settings for regression and classification tasks, \texttt{KFFL} demonstrates its ability to balance accuracy and fairness effectively, outperforming existing methods by comprehensively exploring the Pareto Frontier. Furthermore, we introduce \texttt{KFFL-TD}, a time-delayed variant that further reduces communication rounds, enhancing efficiency in decentralized environments.
URL: https://openreview.net/forum?id=k8x44wVIs1
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Title: Conditional Image Synthesis with Diffusion Models: A Survey
Authors: Zheyuan Zhan, Defang Chen, Jian-Ping Mei, Zhenghe Zhao, Jiawei Chen, Chun Chen, Siwei Lyu, Can Wang
Abstract: Conditional image synthesis based on user-specified requirements is a key component in creating complex visual content. In recent years, diffusion-based generative modeling has become a highly effective way for conditional image synthesis, leading to exponential growth in the literature. However, the complexity of diffusion-based modeling, the wide range of image synthesis tasks, and the diversity of conditioning mechanisms present significant challenges for researchers to keep up with rapid developments and to understand the core concepts on this topic. In this survey, we categorize existing works based on how conditions are integrated into the two fundamental components of diffusion-based modeling, i.e., the denoising network and the sampling process. We specifically highlight the underlying principles, advantages, and potential challenges of various conditioning approaches during the training, re-purposing, and specialization stages to construct a desired denoising network. We also summarize six mainstream conditioning mechanisms in the sampling process. All discussions are centered around popular applications. Finally, we pinpoint several critical yet still unsolved problems and suggest some possible solutions for future research.
URL: https://openreview.net/forum?id=ewwNKwh6SK
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New submissions
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Title: Balancing Utility and Privacy: Dynamically Private SGD with Random Projection
Abstract: Stochastic optimization is a pivotal enabler in modern machine learning, producing effective models for various tasks. However, several existing works have shown that model parameters and gradient information are susceptible to privacy leakage. Although Differentially Private SGD (DPSGD) addresses privacy concerns, its static noise mechanism impacts the error bounds for model performance. Additionally, with the exponential increase in model parameters, efficient learning of these models using stochastic optimizers has become more challenging. To address these concerns, we introduce the Dynamically Differentially Private Projected SGD (D2P2-SGD) optimizer. In D2P2-SGD, we combine two important ideas: (i) dynamic differential privacy (DDP) with automatic gradient clipping and (ii) random projection with SGD, allowing dynamic adjustment of the tradeoff between utility and privacy of the model. It exhibits provably sub-linear convergence rates across different objective functions, matching the best available rate. The theoretical analysis further suggests that DDP leads to better utility at the cost of privacy, while random projection enables more efficient model learning. Extensive experiments across diverse datasets show that D2P2-SGD remarkably enhances accuracy while maintaining privacy. Our code is available here.
URL: https://openreview.net/forum?id=u6OSRdkAwl
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Title: Robustness in Large Language Models: A Survey of Mitigation Strategies and Evaluation Metrics
Abstract: Large Language Models (LLMs) have emerged as a promising cornerstone for the development of natural language processing (NLP) and artificial intelligence (AI). However, ensuring the robustness of LLMs remains a critical challenge. To address these challenges and advance the field, this survey provides a comprehensive overview of current studies in this area. First, we systematically examine the nature of robustness in LLMs, including its conceptual foundations, the importance of consistent performance across diverse inputs, and the implications of failure modes in real-world applications. Next, we analyze the sources of non-robustness, categorizing intrinsic model limitations, data-driven vulnerabilities, and external adversarial factors that compromise reliability. Following this, we review state-of-the-art mitigation strategies, and then we discuss widely adopted benchmarks, emerging metrics, and persistent gaps in assessing real-world reliability. Finally, we synthesize findings from existing surveys and interdisciplinary studies to highlight trends, unresolved issues, and pathways for future research.
URL: https://openreview.net/forum?id=Bchvaaod6g
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Title: Robust Invariant Representation Learning by Distribution Extrapolation
Abstract: Invariant risk minimization (IRM) aims to enable out-of-distribution (OOD) generalization in deep learning by learning invariant representations. As IRM poses an inherently challenging bi-level optimization problem, most existing approaches---including IRMv1---adopt penalty-based single-level approximations. However, empirical studies consistently show that these methods often fail to outperform well-tuned empirical risk minimization (ERM), highlighting the need for more robust IRM implementations. This work theoretically identifies a key limitation common to many IRM variants: their penalty terms are highly sensitive to limited environment diversity and over-parameterization, resulting in performance degradation. To address this issue, a novel extrapolation-based framework is proposed that enhances environmental diversity by augmenting the IRM penalty through synthetic distributional shifts. Extensive experiments---ranging from synthetic setups to realistic, over-parameterized scenarios---demonstrate that the proposed method consistently outperforms state-of-the-art IRM variants, validating its effectiveness and robustness.
URL: https://openreview.net/forum?id=CkzV8PBYaX
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Title: Spurious Privacy Leakage in Neural Networks
Abstract: Neural networks are vulnerable to privacy attacks aimed at stealing sensitive data.
The risks can be amplified in a real-world scenario, particularly when models are trained on limited and biased data.
In this work, we investigate the impact of spurious correlation bias on privacy vulnerability.
We introduce _spurious privacy leakage_, a phenomenon where spurious groups are significantly more vulnerable to privacy attacks than non-spurious groups.
We further show that group privacy disparity increases in tasks with simpler objectives (e.g. fewer classes) due to the persistence of spurious features.
Surprisingly, we find that reducing spurious correlation using spurious robust methods does not mitigate spurious privacy leakage.
This leads us to introduce a perspective on privacy disparity based on memorization, where mitigating spurious correlation does not mitigate the memorization of spurious data, and therefore, neither the privacy level.
Lastly, we compare the privacy of different model architectures trained with spurious data, demonstrating that, contrary to prior works, architectural choice can affect privacy outcomes.
URL: https://openreview.net/forum?id=tRXDCIgvTT
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Title: TT-TFHE: a Torus Fully Homomorphic Encryption-Friendly Neural Network Architecture
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 Convolutionnal 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: Efficient Uncertainty Estimation via Sensitivity-Guided Subnetwork Selection for Scalable Variational Inference
Abstract: Quantifying predictive uncertainty with minimal computational overhead remains a significant challenge for reliable deep learning applications in safety-critical systems. While Bayesian neural networks (BNNs) are the gold standard for uncertainty quantification, they require considerable training time and computational resources. Although a body of work has focused on mitigating the computational cost of BNN inference via post-hoc approaches, efforts to accelerate training and convergence remain limited. This paper proposes a partial Bayesian training approach via mean-field variational inference (VI), enabling controllable uncertainty modeling through sparse gradient representations. The selection of the variational Bayesian subnetwork is guided by a first-order gradient sensitivity analysis, which is grounded in uncertainty propagation theory. Under mean-field assumptions, we demonstrate how this framework effectively informs the selection of parameters that represent the network's predictive uncertainty. This criterion is also efficiently integrated into auto-differentiation tools avoiding additional computational burdens. The resulting model consists of a combination of deterministic and Bayesian parameters, facilitating an effective, yet efficient, representation of uncertainty. We investigate the effects of varying the proportion of Bayesian parameters (ranging from 1\% to 95\%) across diverse tasks, including regression, classification, and semantic segmentation. Experimental results in MNIST, CIFAR-10, ImageNet, and Cityscapes demonstrate that our approach achieves competitive performance and uncertainty estimates compared to ensemble methods. While maintaining substantially fewer parameters, approximately 50\%, 80\% less than full VI and ensembles, our approach offers reduced training costs with faster convergence compared to full or partial VI trained from scratch. Furthermore, we assess the robustness of predictive uncertainty in the presence of covariate shifts and out-of-distribution data, demonstrating that our method effectively captures uncertainty and exhibits robustness to image corruptions.
URL: https://openreview.net/forum?id=fJzQbSgEem
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Title: Learning Reward Machines from Partially Observed Policies
Abstract: Inverse reinforcement learning is the problem of inferring a reward function from an optimal policy {or demonstrations by an expert}. In this work, it is assumed that the reward is expressed as a reward machine whose transitions depend on atomic propositions associated with the state of a Markov Decision Process (MDP). Our goal is to identify the true reward machine using finite information. To this end, we first introduce the notion of a prefix tree policy which associates a distribution of actions to each state of the MDP and each attainable finite sequence of atomic propositions. Then, we characterize an equivalence class of reward machines that can be identified given the prefix tree policy. Finally, we propose a SAT-based algorithm that uses information extracted from the prefix tree policy to solve for a reward machine. It is proved that if the prefix tree policy is known up to a sufficient (but finite) depth, our algorithm recovers the exact reward machine up to the equivalence class. This sufficient depth is derived as a function of the number of MDP states and (an upper bound on) the number of states of the reward machine.{These results are further extended to the case where we only have access to demonstrations from an optimal policy. Several examples, including discrete grid and block worlds, a continuous state-space robotic arm, and real data from experiments with mice, are used to demonstrate the effectiveness and generality of the approach.
URL: https://openreview.net/forum?id=7bbYYNvhTE
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Title: QC-BERT: A Quantum-Classical hybrid framework for Efficient Sentiment Analysis and Question Answering
Abstract: Transformers have revolutionized NLP but are constrained by their massive parameter counts, posing challenges for edge deployment. Quantum computing, leveraging superposition and entanglement, promises exponential efficiency gains, yet practical, scalable QNLP applications remain scarce. In this pioneering work, we propose QuantumDistilBERT (ours) and HybridTinyBERTQC (ours), the first scalable, hybrid quantum-classical transformer models designed for both core NLP tasks and resource-constrained environments. QuantumDistilBERT achieves 91.36% accuracy on IMDB—just 1.46% below DistilBERT—while reducing trainable parameters by 89.4%, demonstrating strong edge applicability.HybridTinyBERTQC, enhanced with quantum self-attention mechanisms, achieves 82.31% F1 and 73.10% EM on SQuAD 1.1, and 32.86% F1 on Adversarial QA, outperforming TinyBERT (undistilled on task-specific datasets) by over 1% (p < 0.05) on SQuAD and 3.55% on AQA. A novel complexity scoring mechanism reduces quantum circuit overhead by 20%, generalizing well to other text classification tasks. Notably, our hybrid model exhibits a 41.3% reduction in loss variance (0.1329 vs. 0.2265) and uniquely achieves perfect reproducibility across runs with the same random seed—producing identical metrics and loss values every time. This unprecedented consistency underscores the model’s reliability, a critical requirement for edge deployment. Extensive evaluations on IMDB, SQuAD, Adversarial QA, and SST-2 demonstrate the scalability and robustness of our approach. While quantum noise in NISQ hardware still limits subjective task performance, our work lays foundational groundwork for practical, reproducible, and deployable QNLP systems on edge devices
URL: https://openreview.net/forum?id=EPm2AOD9bd
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Title: Emergent Neural Network Mechanisms for Generalization to Objects in Novel Orientations
Abstract: The capability of Deep Neural Networks (DNNs) to recognize objects in orientations outside the training data distribution is not well understood. We investigate the limitations of DNNs’ generalization capacities by systematically inspecting DNNs' patterns of success and failure across out-of-distribution (OoD) orientations. We present evidence that DNNs (across architecture types, including convolutional neural networks and transformers) are capable of generalizing to objects in novel orientations, and we describe their generalization behaviors. Specifically, generalization strengthens when training the DNN with an increasing number of familiar objects, but only in orientations that involve 2D rotations of familiar orientations. We also hypothesize how this generalization behavior emerges from internal neural mechanisms – that neurons tuned to common features between familiar and unfamiliar objects enable out of distribution generalization – and present supporting data for this theory. The reproducibility of our findings across model architectures, as well as analogous prior studies on the brain, suggests that these orientation generalization behaviors, as well as the neural mechanisms that drive them, may be a feature of neural networks in general.
URL: https://openreview.net/forum?id=4wBQTZVSHU
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Title: Goal-Conditioned Data Augmentation for Offline Reinforcement Learning
Abstract: Offline reinforcement learning (RL) enables policy learning from pre-collected offline datasets, relaxing the need to interact directly with the environment. However, limited by the quality of offline datasets, it generally fails to learn well-qualified policies in suboptimal datasets. To address datasets with insufficient optimal demonstrations, we introduce Goal-cOnditioned Data Augmentation (GODA), a novel goal-conditioned diffusion-based method for augmenting samples with higher quality. Leveraging recent advancements in generative modelling, GODA incorporates a novel return-oriented goal condition with various selection mechanisms. Specifically, we introduce a controllable scaling technique to provide enhanced return-based guidance during data sampling. GODA learns a comprehensive distribution representation of the original offline datasets while generating new data with selectively higher-return goals, thereby maximizing the utility of limited optimal demonstrations. Furthermore, we propose a novel adaptive gated conditioning method for processing noisy inputs and conditions, enhancing the capture of goal-oriented guidance. We conduct experiments on the D4RL benchmark and real-world challenges, specifically traffic signal control (TSC) tasks, to demonstrate GODA's effectiveness in enhancing data quality and superior performance compared to state-of-the-art data augmentation methods across various offline RL algorithms.
URL: https://openreview.net/forum?id=8K16dplpE0
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Title: Adapting Chat Language Models Using Only Target Unlabeled Language Data
Abstract: Vocabulary expansion (VE) is the de-facto approach to language adaptation of large language models (LLMs) by adding new tokens and continuing pre-training on target data. While this is effective for base models trained on unlabeled data, it poses challenges for chat models trained to follow instructions through labeled conversation data. Directly adapting the latter with VE on target unlabeled data may result in forgetting chat abilities. While ideal, target chat data is often unavailable or costly to create for low-resource languages, and machine-translated alternatives are not always effective. To address this issue, previous work proposed using a base and chat model from the same family. This method first adapts the base LLM with VE on target unlabeled data and then converts it to a chat model by adding a chat vector (CV) derived from the weight difference between the source base and chat models. We propose ElChat, a new language adaptation method for chat LLMs that adapts a chat model directly on target unlabeled data, without a base model. It elicits chat abilities by injecting information from the source chat model. ElChat offers more robust and competitive target language and safety performance while achieving superior English, chat, and instruction-following abilities compared to CV.
URL: https://openreview.net/forum?id=6IdoIKowfe
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Title: CLoQ: Enhancing Fine-Tuning of Quantized LLMs via Calibrated LoRA Initialization
Abstract: Fine-tuning large language models (LLMs) using low-rank adaptation (LoRA) has become a highly efficient approach for downstream tasks, particularly in scenarios with limited computational resources. However, applying LoRA techniques to quantized LLMs poses unique challenges due to the reduced representational precision of quantized weights. In this paper, we introduce CLoQ (Calibrated LoRA initialization for Quantized LLMs), a simplistic initialization strategy designed to overcome these challenges. Our approach focuses on minimizing the layer-wise discrepancy between the original LLM and its quantized counterpart with LoRA components during initialization. By leveraging a small calibration dataset, CLoQ quantizes a pre-trained LLM and determines the optimal LoRA components for each layer, ensuring a strong foundation for subsequent fine-tuning.
A key contribution of this work is a novel theoretical result that enables the accurate and closed-form construction of these optimal LoRA components. We validate the efficacy of CLoQ across multiple tasks such as language generation, arithmetic reasoning, and commonsense reasoning, demonstrating that it consistently outperforms existing LoRA fine-tuning methods for quantized LLMs, especially at 2-bit.
URL: https://openreview.net/forum?id=FHnTRAAdAZ
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Title: On Time Series Clustering with Graph Neural Networks
Abstract: Graph clustering and pooling operators have been adopted in graph-based architectures to capture meaningful patterns in time series data by leveraging both temporal and relational structures. However, the contribution of each design choice and the behavior of different operators remain underexplored. This work introduces a streamlined deep learning framework based on a spatio-temporal graph neural network (STGNN) for clustering time series, which can leverage prior knowledge on the spatial structure of the data. The STGNN-based model flexibly identifies clusters in various data settings through an encoder-decoder architecture with a bottleneck, showing that a spatio-temporal approach can identify meaningful clusters even in datasets that do not explicitly include spatial relations. We validate the framework's qualitative performance through experiments on synthetic and real-world data, showing its effectiveness in different scenarios. We also provide a heuristic for model selection in unsupervised settings via a self-supervised forecasting loss. Code available at https://anonymous.4open.science/r/Time-Series-Clustering-with-GNNs-AB11
URL: https://openreview.net/forum?id=MHQXfiXsr3
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Title: Bayesian information theoretic model-averaging stochastic item selection for computer adaptive testing
Abstract: he goal of Computer Adaptive Testing (CAT) is to reliably estimate an individual's ability as modeled by an item response theory (IRT) instrument using only a subset of the instrument's items. A secondary goal is to vary the items presented across different testing sessions so that the sequence of items does not become overly stereotypical -- we want all items to have an exposure rate sufficiently far from zero.
We formulate the optimization problem for CAT in terms of Bayesian information theory, where one chooses the item at each step based on the criterion of the ability model discrepancy -- the statistical distance between the ability estimate at the next step and the full-test ability estimate. This viewpoint of CAT naturally motivates a stochastic selection procedure that equates sampling the next item to Bayesian model averaging in the space of ability estimates. Using the NIH Work Disability Functional Assessment Battery (WD-FAB), we evaluate our new methods in comparison to pre-existing methods found in the literature. We find that our stochastic selector has superior properties in terms of both item exposure and test accuracy/efficiency.
URL: https://openreview.net/forum?id=5tMpMfxzCR
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Title: Solving Quadratic Programs via Deep Unrolled Douglas-Rachford Splitting
Abstract: Convex quadratic programs (QPs) are fundamental to numerous applications, including finance, engineering, and energy systems. Among the various methods for solving them, the Douglas-Rachford (DR) splitting algorithm is notable for its robust convergence properties. Concurrently, the emerging field of Learning-to-Optimize offers promising avenues for enhancing algorithmic performance, with algorithm unrolling receiving considerable attention due to its computational efficiency and interpretability. In this work, we propose an approach that unrolls a modified DR splitting algorithm to efficiently learn solutions for convex QPs. Specifically, we introduce a tailored DR splitting algorithm that replaces the computationally expensive linear system-solving step with a simplified gradient-based update, while retaining convergence guarantees. Consequently, we unroll the resulting DR splitting method and present a well-crafted neural network architecture to predict QP solutions. Our method achieves up to 50% reductions in iteration counts and 40% in solve time across benchmarks on both synthetic and real-world QP datasets, demonstrating its scalability and superior performance in enhancing computational efficiency across varying sizes.
URL: https://openreview.net/forum?id=xOfOgPnbtF
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Title: Prior Specification for Exposure-based Bayesian Matrix Factorization
Abstract: The rapid development of the Internet has resulted in a surge of information, particularly with the rise of recommender systems (RSs). One of the most significant challenges facing existing RS models is data sparsity. To address problems related to sparse data, Bayesian models have been applied to RS systems because of their effectiveness with small sample sizes. However, the performance of Bayesian models is heavily influenced by the choice of prior distributions and hyperparameters. Recent research has introduced an analytical method for specifying prior distributions in generic Bayesian models. The major concept is a statistical technique called Prior Predictive Matching~(PPM), which optimizes hyperparameters by aligning virtual statistics generated by the prior with observed data. This approach aims to reduce the need for repeated and costly posterior inference and enhance overall Bayesian model performance. However, our evaluation of this theoretical method reveals considerable deviations in prior specification estimates as data sparsity increases. In this study, we present an enhanced method for specifying priors in Bayesian matrix factorization models. We improve the estimators by implementing an exposure-based model to better simulate data scarcity. Our method demonstrates significant accuracy improvements in hyperparameter estimation during synthetic experiments. We also explore the feasibility of applying this method to real-world datasets and provide insights into how the model's behavior adapts to varying levels of data sparsity.
URL: https://openreview.net/forum?id=o5R4Hv9XqC
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Title: nnActive: A Framework for Evaluation of Active Learning in 3D Biomedical Segmentation
Abstract: Semantic segmentation is crucial for various biomedical applications, yet its reliance on large, annotated datasets presents a significant bottleneck due to the high cost and specialized expertise required for manual labeling. Active Learning (AL) aims to mitigate this challenge by selectively querying the most informative samples, thereby reducing annotation effort. However, in the domain of 3D biomedical imaging, there remains no consensus on whether AL consistently outperforms Random sampling strategies. Current methodological assessment is hindered by the wide-spread occurrence of four pitfalls with respect to AL method evaluation. These are (1) restriction to too few datasets and annotation budgets, (2) training 2D models on 3D images and not incorporating partial annotations, (3) Random baseline not being adapted to the task and (4) measuring annotation cost only in voxels. In this work, we introduce nnActive, an open-source AL framework that systematically overcomes the aforementioned pitfalls by (1) means of a large scale study evaluating 8 QMs on four biomedical imaging datasets and three label regimes, accompanied by four large-scale ablation studies, (2) extending the state-of-the-art 3D medical segmentation method nnU-Net by using partial annotations for training with 3D patch-based query selection, (3) proposing Foreground Aware Random sampling strategies tackling the foreground-background class imbalance commonly encountered in 3D medical images and (4) propose the foreground efficiency metric, which captures that the annotation cost for background- compared to foreground-regions is very low. We reveal the following key findings: (1) while all AL methods outperform standard Random sampling, none reliably surpasses an improved Foreground Aware Random sampling; (2) the benefits of AL dependend on task specific parameters like number of classes and their locations; (3) Predictive Entropy is overall the best performing AL method, but likely requires the most annotation effort; (4) AL performance can be improved with more compute intensive design choices like longer training and smaller query sizes. As a holistic, open source framework nnActive has the potential to act as a catalyst for research and application of AL in 3D biomedical imaging. Code is at: https://anonymous.4open.science/r/nnactive-815F
URL: https://openreview.net/forum?id=AJAnmRLJjJ
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Title: MGPATH: A Vision-Language Model with Multi-Granular Prompt Learning for Few-Shot Whole Slide Pathology Classification
Abstract: Whole slide pathology image classification presents challenges due to gigapixel image sizes and limited annotation labels, hindering model generalization. This paper introduces a prompt learning method to adapt large vision-language models for few-shot pathology classification. We first extend the Prov-GigaPath vision foundation model, pre-trained on 1.3 billion pathology image tiles, into a vision-language model by adding adaptors and aligning it with medical text encoders via contrastive learning on 923K image-text pairs. The model is then used to extract visual features and text embeddings from few-shot annotations and fine-tunes with learnable prompt embeddings. Unlike prior methods that combine prompts with frozen features using prefix embeddings or self-attention, we propose multi-granular attention that compares interactions between learnable prompts with individual image patches and groups of them. This approach improves the model’s ability to capture both fine-grained details and broader context, enhancing its recognition of complex patterns across sub-regions. To further improve accuracy, we leverage (unbalanced) optimal transport-based visual-text distance to secure model robustness by mitigating perturbations that might occur during the data augmentation process. Empirical experiments on lung, kidney, and breast pathology modalities validate the effectiveness of our approach; thereby, we surpass several of the latest competitors and consistently improve performance across diverse architectures, including CLIP, PLIP, and Prov-GigaPath integrated PLIP.
URL: https://openreview.net/forum?id=u7U81JLGjH
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Title: Hyperspectral Gaussian Splatting
Abstract: Hyperspectral imaging (HSI) has been widely used in agricultural applications for non-destructive estimation of plant nutrient composition and precise determination of nutritional elements of samples. Recently, 3D reconstruction methods have been used to create implicit neural representations of HSI scenes, which can help localize the target object's nutrient composition spatially and spectrally. Neural Radiance Field (NeRF) is a cutting-edge implicit representation that can be used to render hyperspectral channel compositions of each spatial location from any viewing direction. However, it faces limitations in training time and rendering speed. In this paper, we propose Hyperspectral Gaussian Splatting (HS-GS), which combines the state-of-the-art 3D Gaussian Splatting (3DGS) with a diffusion model to enable 3D explicit reconstruction of the hyperspectral scenes and novel view synthesis for the entire spectral range. To enhance the model's ability to capture fine-grained reflectance variations across the light spectrum and leverage correlations between adjacent wavelengths for denoising, we introduce a wavelength encoder to generate wavelength-specific spherical harmonics offsets. We also introduce a novel Kullback–Leibler divergence-based loss to mitigate the spectral distribution gap between the rendered image and the ground truth. A diffusion model is further applied for denoising the rendered images and generating photorealistic hyperspectral images. We present extensive evaluations on five diverse hyperspectral scenes from the Hyper-NeRF dataset to show the effectiveness of our proposed HS-GS framework. The results demonstrate that HS-GS has achieved the new state-of-the-art performance among all the previously published methods. Code will be released upon publication.
URL: https://openreview.net/forum?id=MI5eUe8ZdB
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Title: Tree Search for Language Model Agents
Abstract: Autonomous agents powered by language models (LMs) have demonstrated promise in their ability to perform decision-making tasks such as web automation. However, a key limitation remains: LMs, primarily optimized for natural language understanding and generation, struggle with multi-step reasoning, planning, and using environmental feedback when attempting to solve realistic computer tasks. Towards addressing this, we propose an inference-time search algorithm for LM agents to explicitly perform exploration and multi-step planning in interactive web environments. Our approach is a form of best-first tree search that operates within the actual environment space, and is complementary with most existing state-of-the-art agents. It is the first tree search algorithm for LM agents that shows effectiveness on realistic web tasks. On the challenging VisualWebArena benchmark, applying our search algorithm on top of a GPT-4o agent yields a 39.7% relative increase in success rate compared to the same baseline without search, setting a state-of-the-art success rate of 26.4%. On WebArena, search also yields a 28.0% relative improvement over a baseline agent, setting a competitive success rate of 19.2%. Our experiments showcase the effectiveness of search for web agents, and we demonstrate that performance scales with increased test-time compute.
URL: https://openreview.net/forum?id=QF0N3x2XVm
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Title: GPT Carry-On: Language Model Customization Made Scalable by Growing-In-Depth
Abstract: Modern large language foundation models (LLM) have now entered the daily lives of millions of users. We ask a natural question whether it is possible to customize LLM for every user or every task. From system and industrial economy consideration, general continue-training or fine-tuning still require substantial computation and memory of training GPU nodes, whereas most inference nodes under deployment, possibly with lower-end GPUs, are configured to make forward pass fastest possible.
We propose a framework to take full advantages of existing LLMs and systems of online service. We train an additional branch of transformer blocks on the final-layer embedding of pretrained LLMs, which is the base, then a carry-on module merge the base models to compose a customized LLM. We can mix multiple layers, or multiple LLMs specialized in different domains such as chat, coding, math, to form a new mixture of LLM that best fit a new task. As the base model don't need to update parameters, we are able to outsource most computation of the training job on inference nodes, and only train a lightweight carry-on on training nodes, where we consume less than 1GB GPU memory to train a 100M carry-on layer on 30B LLM. We tested Qwen and DeepSeek opensourced models
for continue-pretraining and got faster loss convergence. We use it to improve solving math questions with extremely small computation and model size, with 1000 data samples of chain-of-thoughts, and as small as 1 MB parameters of two layer layer carry-on, and the results are promising.
URL: https://openreview.net/forum?id=LDAvIGZuFy
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Title: TP‑Blend: Textual‑Prompt Attention Pairing for Precise Object‑Style Blending in Diffusion Models
Abstract: Current text–conditioned diffusion editors handle single object replacement well but struggle when a new object and a new style must be introduced simultaneously. We present Twin‑Prompt Attention Blend (TP‑Blend), a lightweight training‑free framework that receives two separate textual prompts, one specifying a blend object and the other defining a target style, and injects both into a single denoising trajectory. TP‑Blend is driven by two complementary attention processors. Cross‑Attention Object Fusion (CAOF) first averages head‑wise attention to locate spatial tokens that respond strongly to either prompt, then solves an entropy‑regularised optimal transport problem that reassigns complete multi‑head feature vectors to those positions. CAOF updates feature vectors at the full combined dimensionality of all heads (e.g., 640 dimensions in SD‑XL), preserving rich cross‑head correlations while keeping memory low. Self‑Attention Style Fusion (SASF) injects style at every self‑attention layer through Detail‑Sensitive Instance Normalization. A lightweight one‑dimensional Gaussian filter separates low‑ and high‑frequency components; only the high‑frequency residual is blended back, imprinting brush‑stroke‑level texture without disrupting global geometry. SASF further swaps the Key and Value matrices with those derived from the style prompt, enforcing context‑aware texture modulation that remains independent of object fusion. Extensive experiments show that TP‑Blend produces high‑resolution, photo‑realistic edits with precise control over both content and appearance, surpassing recent baselines in quantitative fidelity, perceptual quality, and inference speed.
URL: https://openreview.net/forum?id=q6M73uOBZE
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Title: A Dynamical Clipping Approach with Task Feedback for Proximal Policy Optimization
Abstract: Proximal Policy Optimization (PPO) has been broadly applied to robotics learning, showcasing stable training performance. However, the fixed clipping bound setting may limit the performance of PPO. Specifically, there is no theoretical proof that the optimal clipping bound remains consistent throughout the entire training process. Meanwhile, previous researches suggest that a fixed clipping bound restricts the policy's ability to explore. Therefore, many past studies have aimed to dynamically adjust the PPO clipping bound to enhance PPO's performance. However, the objective of these approaches are not directly aligned with the objective of reinforcement learning (RL) tasks, which is to maximize the cumulative Return. Unlike previous clipping approaches, we propose a bi-level proximal policy optimization objective that can dynamically adjust the clipping bound to better reflect the preference (maximizing Return) of these RL tasks. Based on this bi-level proximal policy optimization paradigm, we introduce a new algorithm named Preference based Proximal Policy Optimization (Pb-PPO). Pb-PPO utilizes a multi-armed bandit approach to refelect RL preference, recommending the clipping bound for PPO that can maximizes the current Return. Therefore, Pb-PPO results in greater stability and improved performance compared to PPO with a fixed clipping bound. We test Pb-PPO on locomotion benchmarks across multiple environments, including Gym-Mujoco and legged-gym. Additionally, we validate Pb-PPO on customized navigation tasks. Meanwhile, we conducted comparisons with PPO using various fixed clipping bounds and various of clipping approaches. The experimental results indicate that Pb-PPO demonstrates superior training performance compared to PPO and its variants.
URL: https://openreview.net/forum?id=xOnAIaIgmC
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Title: MVGaussian: High-Fidelity text-to-3D Content Generation with Multi-View Guidance and Surface Densification
Abstract: The field of text-to-3D content generation has made significant progress in generating realistic 3D objects, with existing methodologies like Score Distillation Sampling (SDS) offering promising guidance. However, these methods often encounter the Janus problem—multi-face ambiguities due to imprecise guidance. Additionally, while recent advancements in 3D Gaussian splatting have shown its efficacy in representing 3D volumes, optimization of this representation remains largely unexplored. This paper introduces a unified framework for text-to-3D content generation that addresses these critical gaps. Our approach utilizes multi-view guidance to iteratively form the structure of the 3D model, progressively enhancing detail and accuracy. We also introduce a novel densification algorithm that aligns Gaussians close to the surface, optimizing the structural integrity and fidelity of the generated models. Extensive experiments validate our approach, demonstrating that it produces high-quality visual outputs with minimal time cost. Notably, our method achieves high-quality results within half an hour of training, offering a substantial efficiency gain over most existing methods, which require hours of training time to achieve comparable results.
URL: https://openreview.net/forum?id=dhduuUN6vD
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