Daily TMLR digest for Nov 07, 2025
TMLR
Accepted papers
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Title: GenOL: Generating Diverse Examples for Name-only Online Learning
Authors: Minhyuk Seo, Seongwon Cho, Minjae Lee, Diganta Misra, Hyeonbeom Choi, Seon Joo Kim, Jonghyun Choi
Abstract: Online learning methods often rely on supervised data. However, under data distribution shifts, such as in continual learning (CL), where continuously arriving online data streams incorporate new concepts (e.g., classes), real-time manual annotation is impractical due to its costs and latency, which hinder real-time adaptation. To alleviate this, `name-only' setup has been proposed, requiring only the name of concepts, not the supervised samples. A recent approach tackles this setup by supplementing data with web-scraped images, but such data often suffers from issues of data imbalance, noise, and copyright. To overcome the limitations of both human supervision and webly supervision, we propose GenOL using generative models for name-only training. But naive application of generative models results in limited diversity of generated data. Here, we enhance (i) intra-diversity, the diversity of images generated by a single model, by proposing a diverse prompt generation method that generates diverse text prompts for text-to-image models, and (ii) inter-diversity, the diversity of images generated by multiple generative models, by introducing an ensemble strategy that selects minimally overlapping samples. We empirically validate that the proposed \frameworkname outperforms prior arts, even a model trained with fully supervised data by large margins, in various tasks, including image recognition and multi-modal visual reasoning.
URL: https://openreview.net/forum?id=QPfVoTMLWq
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Title: Robustness in Large Language Models: A Survey of Mitigation Strategies and Evaluation Metrics
Authors: PANKAJ KUMAR, Subhankar Mishra
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: The inexact power augmented Lagrangian method for constrained nonconvex optimization
Authors: Alexander Bodard, Konstantinos Oikonomidis, Emanuel Laude, Panagiotis Patrinos
Abstract: This work introduces an unconventional inexact augmented Lagrangian method where the augmenting term is a Euclidean norm raised to a power between one and two. The proposed algorithm is applicable to a broad class of constrained nonconvex minimization problems that involve nonlinear equality constraints. In a first part of this work, we conduct a full complexity analysis of the method under a mild regularity condition, leveraging an accelerated first-order algorithm for solving the Hölder-smooth subproblems. Interestingly, this worst-case result indicates that using lower powers for the augmenting term leads to faster constraint satisfaction, albeit with a slower decrease of the dual residual. Notably, our analysis does not assume boundedness of the iterates. Thereafter, we present an inexact proximal point method for solving the weakly-convex and Hölder-smooth subproblems, and demonstrate that the combined scheme attains an improved rate that reduces to the best-known convergence rate whenever the augmenting term is a classical squared Euclidean norm. Different augmenting terms, involving a lower power, further improve the primal complexity at the cost of the dual complexity. Finally, numerical experiments validate the practical performance of unconventional augmenting terms.
URL: https://openreview.net/forum?id=63ANb4r7EM
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New submissions
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Title: Correctness-Aware Knowledge Distillation for Enhanced Student Learning
Abstract: In real-world learning, students rely on their mentors for guidance but must also develop the ability to recognize and learn from their mentors' mistakes. Inspired by this mentor-critic dynamic, we propose Mentor-Critic Distillation (MCD), a novel framework for knowledge distillation in machine learning. Traditional distillation methods risk transferring both correct insights and errors from the mentor (teacher model) to the student model, which can hinder student performance. Notably, previous state-of-the-art approaches fail to account for scenarios where the teacher is incorrect, often leaving the student model vulnerable to inheriting these errors. To address this limitation, MCD introduces a weighted knowledge transfer mechanism that decouples the learning process based on the mentor's correctness. When the mentor model is correct, the student model follows the mentor's guidance with a large weight on knowledge transfer. However, when the mentor is incorrect, the student relies more on the ground truth but still learns inter-class relationships from the mentor, adjusting the weight toward task-specific losses such as cross-entropy. This mentor-critic approach ensures that the student model benefits from the mentor's expertise without inheriting its mistakes. We provide theoretical analysis proving that MCD strictly generalizes vanilla KD and guarantees reduced negative transfer. We evaluate our Mentor-Critic Distillation across diverse teacher-student configurations on benchmark datasets, including CIFAR-100, ImageNet, and MedMNIST. Notably, MCD requires no architectural modifications or additional parameters, making it a practical drop-in replacement for standard knowledge distillation. These results highlight MCD's effectiveness in optimizing knowledge transfer and its robustness across diverse domains and data regimes, particularly in data-scarce scenarios typical of specialized domains such as medical imaging.
URL: https://openreview.net/forum?id=XpRXmzd2sF
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Title: Relative Geometry of Neural Forecasters: Linking Accuracy and Alignment in Learned Dynamics
Abstract: Neural networks can accurately forecast complex dynamical systems, yet how they internally represent underlying dynamics remains poorly understood. We study neural forecasters through the lens of representational alignment, introducing anchor-based, geometry-agnostic relative embeddings that remove rotational and scaling ambiguities in latent spaces. Applying this framework across seven canonical dynamical systems—ranging from periodic to chaotic—we reveal reproducible family-level structure: multilayer perceptrons align with other MLPs, recurrent networks with RNNs, while transformers and echo-state networks achieve strong forecasts despite weaker alignment. Alignment generally correlates with forecasting accuracy, yet high accuracy can coexist with low alignment.
Relative geometry thus provides a simple, reproducible foundation for comparing how model families internalize and represent dynamical structure.
URL: https://openreview.net/forum?id=t4stf5Gafz
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Title: Facial Counterfactual Generation via Causal Mask-Guided Editing
Abstract: Generating counterfactual facial images is an important tool for interpretable machine learning, fairness analysis, and understanding the causal relationships among facial attributes. In this work, we propose a novel neuro-symbolic framework for causal editing, which integrates causal graph discovery, mask-guided counterfactual generation, and semantic interpretation to produce facial images that are both realistic and causally consistent. We first employ the Fast Causal Inference (FCI) algorithm to uncover latent causal relationships among facial attributes, enabling the identification of direct and indirect factors for target interventions. Using these causal graphs, we construct spatially informed masks that guide a DDPM-based generative model, ensuring that only regions relevant to the causal factors are modified. Finally, we leverage CLIP-based embeddings to provide logical, human-understandable explanations of the semantic changes in the counterfactuals. Experiments on CelebA and CelebA-HQ demonstrate that our approach produces high-fidelity counterfactuals, achieves superior performance on sparsity and realism metrics, and mitigates bias compared to state-of-the-art methods. This framework offers a principled approach to causally grounded, interpretable facial image editing.
URL: https://openreview.net/forum?id=ssamEGQj0C
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Title: Diverse Image Priors for Black-box Data-free Knowledge Distillation
Abstract: Knowledge distillation (KD) is a well-known technique for effectively transferring knowledge from an expert network (teacher) to a smaller network (student) with little sacrifice in performance. However, most KD methods require extensive access to the teacher or even its original training set, which are unachievable due to intellectual property or security concerns. These challenges have inspired black-box data-free KD, in which only the teacher's top-1 predictions and no real data are available. While recent approaches tend to synthetic data, they largely overlook data diversity, which is crucial for effective knowledge transfer. We propose Diverse Image Priors Knowledge Distillation (DIP-KD) to address this problem. We first synthesize image priors --- semantically diverse synthetic images, then further optimize them to a diversity objective via contrastive learning, and finally extract soft knowledge to distill the student. We achieve state-of-the-art KD performance for the black-box data-free settings on eight image benchmarks. This is backed by our deep analysis, showing that data diversity is effectively improved, and how it facilitates KD performance. We publish the source code at https://osf.io/5mry8/?view_only=dee9e8fbcd114c34b45aa958a3aa32fa.
URL: https://openreview.net/forum?id=9biXMYLFXn
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Title: Beyond Anonymization: Object Scrubbing for Privacy-Preserving 2D and 3D Vision Tasks
Abstract: We introduce ROAR (Robust Object Removal and Re-annotation), a scalable framework for privacy-preserving dataset obfuscation that removes sensitive objects instead of modifying them. Designed for practical deployment, our method integrates instance segmentation with generative inpainting to eliminate identifiable entities while preserving scene integrity. Extensive evaluations on 2D COCO-based object detection show that ROAR achieves 87.5% of baseline average precision (AP), whereas image dropping achieves only 74.2%, highlighting the advantage of scrubbing in preserving dataset utility. In NeRF-based 3D reconstruction, our method incurs a PSNR loss of at most 1.66,dB while maintaining SSIM and improving LPIPS, demonstrating superior perceptual quality. ROAR follows a structured pipeline of detection, inpainting-based removal, re-annotation, and evaluation. We systematically evaluate the privacy-utility trade-off across both 2D and 3D tasks, showing that object removal offers a more effective balance than traditional methods. Our findings establish ROAR as a practical privacy framework, achieving strong guarantees with minimal performance trade-offs. The results highlight challenges in generative inpainting, occlusion-robust segmentation, and task-specific scrubbing, laying the groundwork for real-world privacy-preserving vision systems.
URL: https://openreview.net/forum?id=RVht55LRWP
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Title: On-Policy Policy Gradient Reinforcement Learning Without On-Policy Sampling
Abstract: On-policy reinforcement learning (RL) algorithms are typically characterized as algorithms that perform policy updates using i.i.d.\@ trajectories collected by the agent's current policy. However, after observing only a finite number of trajectories, such on-policy sampling may produce data that fails to match the expected on-policy data distribution. This \textit{sampling error} leads to high-variance gradient estimates that yield data inefficient on-policy learning. Recent work in the policy evaluation setting has shown that non-i.i.d.\@, off-policy sampling can produce data with lower sampling error w.r.t. the expected on-policy distribution than on-policy sampling can produce~\citep{zhong2022robust}. Motivated by this observation, we introduce an adaptive, off-policy sampling method to reduce sampling error during on-policy policy gradient RL training. Our method, Proximal Robust On-Policy Sampling (PROPS), reduces sampling error by collecting data with a \textit{behavior policy} that increases the probability of sampling actions that are under-sampled w.r.t. the current policy. We empirically evaluate PROPS on both continuous-action MuJoCo benchmark tasks as well as discrete-action tasks and demonstrate that (1) PROPS decreases sampling error throughout training and (2) increases the data efficiency of on-policy policy gradient algorithms.
URL: https://openreview.net/forum?id=nCoyFp8uO1
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Title: Variational Visual Question Answering for Uncertainty-Aware Selective Prediction
Abstract: Despite remarkable progress in recent years, vision language models (VLMs) remain prone to overconfidence and hallucinations on tasks such as Visual Question Answering (VQA) and Visual Reasoning. Bayesian methods can potentially improve reliability by helping models selectively predict, that is, models respond only when they are sufficiently confident. Unfortunately, Bayesian methods are often assumed to be costly and ineffective for large models, and there exists little evidence to show otherwise for multimodal applications. Here, we show the effectiveness and competitive edge of variational Bayes for selective prediction in VQA for the first time. We build on recent advances in variational methods for deep learning and propose an extension called "Variational VQA". This method improves calibration and yields significant gains for selective prediction on VQA and Visual Reasoning, particularly when the error tolerance is low (≤ 1%). Often, just one posterior sample can yield more reliable answers than those obtained by models trained with AdamW. In addition, we propose a new risk-averse selector that outperforms standard sample averaging by considering the variance of predictions. Overall, we present compelling evidence that variational learning is a viable option to make large VLMs safer and more trustworthy.
URL: https://openreview.net/forum?id=jtnMIbJIso
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Title: Benchmarking Missing Data Imputation Methods in Socioeconomic Surveys
Abstract: Missing data imputation is a core challenge in socioeconomic surveys, where data is often longitudinal, hierarchical, high-dimensional, not independent and identically distributed, and missing under complex mechanisms. Socioeconomic datasets like the Consumer Pyramids Household Survey (CPHS)-the largest continuous household survey in India since 2014, covering 174,000 households-highlight the importance of robust imputation, which can reduce survey costs, preserve statistical power, and enable timely policy analysis. This paper systematically evaluates these methods under three missingness mechanisms: missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR), across five missingness ratios ranging from 10% to 50%. We evaluate imputation performance on both continuous and categorical variables, assess the impact on downstream tasks, and compare the computational efficiency of each method. Our results indicate that classical machine learning methods such as MissForest and HyperImpute remain strong baselines with favorable trade-offs between accuracy and efficiency, while deep learning methods perform better under complex missingness patterns and higher missingness ratios, but face scalability challenges. We ran experiments on CPHS and multiple synthetic survey datasets, and found consistent patterns across them. Our framework aims to provide a reliable benchmark for structured socioeconomic surveys, and addresses the critical gap in reproducible, domain-specific evaluation of imputation methods. The open-source code is provided.
URL: https://openreview.net/forum?id=HLhi9xhRw6
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Title: CAE: Repurposing the Critic as an Explorer in Deep Reinforcement Learning
Abstract: Exploration remains a fundamental challenge in reinforcement learning, as many existing methods either lack theoretical guarantees or fall short in practical effectiveness. In this paper, we propose CAE, i.e., the Critic as an Explorer, a lightweight approach that repurposes the value networks in standard deep RL algorithms to drive exploration, without introducing additional parameters. CAE leverages multi-armed bandit techniques combined with a tailored scaling strategy, enabling efficient exploration with provable sub-linear regret bounds and strong empirical stability. Remarkably, it is simple to implement, requiring only about 10 lines of code. For complex tasks where learning reliable value networks is difficult, we introduce CAE+, an extension of CAE that incorporates an auxiliary network. CAE+ increases the parameter count by less than 1% while preserving implementation simplicity, adding roughly 10 additional lines of code. Extensive experiments on MuJoCo, MiniHack, and Habitat validate the effectiveness of CAE and CAE+, highlighting their ability to unify theoretical rigor with practical efficiency.
URL: https://openreview.net/forum?id=54MOD02xC2
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Title: FedLog: Personalized Federated Classification with Less Communication and More Flexibility
Abstract: Federated representation learning (FRL) aims to learn personalized federated models with effective feature extraction from local data. FRL algorithms that share the majority of the model parameters face significant challenges with huge communication overhead. This overhead stems from the millions of neural network parameters and slow aggregation progress of the averaging heuristic. To reduce the overhead, we propose FedLog, which shares sufficient data summaries instead of raw model parameters. The data summaries encode minimal sufficient statistics of an exponential family, and Bayesian inference is utilized for global aggregation. FedLog helps reduce message sizes and communication frequency. We prove that the shared message is minimal and theoretically analyze the convergence rate of FedLog. To further ensure formal privacy guarantees, we extend FedLog with the differential privacy framework. Empirical results demonstrate high learning accuracy with low communication overhead of our method.
URL: https://openreview.net/forum?id=7Hwk0bvvKn
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