论文检索

General Machine Learning · Clustering

Yiming Wang, Qun Li, Dongxia Chang, Jie Wen, Hua Dai, Fu Xiao

Deep Multi-View Clustering (MVC) aims to extract a unified semantic consensus from diverse data sources without supervision. However, current approaches relying on flat Euclidean embeddings often fail to model data uncertainty, resulting in rigid alignment where high-quality views are forced to drift toward corrupted ones. To address these challenges, we propose the Hyperbolic Asymmetric Multi-view Clustering (HAMC) framework. By embedding features into the Poincaré ball model, HAMC leverages the exponential volume growth of hyperbolic geometry to optimize cluster separability. It pushes high-confidence representations toward the boundary while retaining noisy ones near the origin. To mitigate noise, we introduce an asymmetric view alignment mechanism, enabling reliable views to unidirectionally guide unreliable ones. Furthermore, a consensus-aware cluster learning strategy is designed to construct robust global pseudo-labels via a confidence-based screening scheme, refining the cluster structure. Extensive experiments against 13 baselines demonstrate that HAMC achieves state-of-the-art performance.

Theory · Reinforcement Learning and Planning

Abdullah Akgül, Gulcin Baykal, Manuel Haussmann, Mustafa Mert Çelikok, Melih Kandemir

Optimal control of complex environments with robotic systems faces two complementary and intertwined challenges: efficient organization of sensory state information and far-sighted action planning. Because the reinforcement learning framework addresses only the latter, it tends to deliver sample-inefficient solutions. Active inference is the state-of-the-art process theory that explains how biological brains handle this dual problem. However, its applications to artificial intelligence have thus far been limited to extensions of existing model-based approaches. We present a formal abstraction of reinforcement learning algorithms that spans model-based, distributional, and model-free approaches. This abstraction seamlessly integrates active inference into the distributional reinforcement learning framework, making its performance advantages accessible without transition dynamics modeling.

General Machine Learning · Causality

Yiwen (Evie) Qiu, Filip Kovačević, Shimeng Huang, Peter Spirtes, Francesco Locatello

Selection bias is pervasive in observational studies. For example, large scale biobanks data can exhibit ``healthy volunteer bias'' when respondents are healthier and of higher socio-economic status than the population they are meant to represent. Recovering causal effects from such sub-population is an important problem in causal inference, as estimating average treatment effects (ATE) from selected populations can result in a severely biased estimate of the ATE from the whole population. In this paper, we investigate the identifiability of the ATE under selection bias. We provide *necessary and sufficient conditions* for ATE identifiability, leveraging weak assumptions on probability classes to characterize propensity score and selection probability. Compared to previous works, our results extend existing graphical identifiability criteria and offer a more comprehensive understanding of causal effect identification *with strictly weaker conditions* in the presence of selection bias.

Deep Learning · Theory

Yuri Kinoshita, Naoki Nishikawa, Taro Toyoizumi

Dataset distillation, a training-aware data compression technique, has recently attracted increasing attention as an effective tool for mitigating costs of optimization and data storage. However, progress remains largely empirical. Mechanisms underlying the extraction of task-relevant information from the training process and the efficient encoding of such information into synthetic data points remain elusive. In this paper, we theoretically analyze practical algorithms of dataset distillation applied to the gradient-based training of two-layer neural networks with width $L$. By focusing on a non-linear task structure called multi-index model, we prove that the low-dimensional structure of the problem is efficiently encoded into the resulting distilled data. This dataset reproduces a model with high generalization ability for a required memory complexity of $\Theta(r^2d+L)$, where $d$ and $r$ are the input and intrinsic dimensions of the task. To the best of our knowledge, this is one of the first theoretical works that include a specific task structure, leverage its intrinsic dimensionality to quantify the compression rate and study dataset distillation implemented solely via gradient-based algorithms.

Optimization · Large Scale, Parallel and Distributed

zhixin wang, Jiaming Xu, Tianyi Zhou, Mingjun Zhang, Liming Liu, JiaruiHu, Dian Yang, TongYu Wang, Ping Zhang, Jinlong Hou 等

Effectively scaling Reinforcement Learning (RL) is crucial for enhancing the reasoning and alignment of Large Language Models. The massive data and complex execution flows inherent in these tasks require a distributed architecture capable of efficient scaling. However, to simplify programming and dependency management, mainstream frameworks often rely on a centralized architecture where a single node dispatches both control and data. This inherent coupling creates significant communication bottlenecks, severely limiting system scalability and efficiency. We present DistFlow, a novel, fully distributed RL framework that adopts a multi-controller paradigm. By decoupling data transmission from control dispatch, DistFlow establishes a parallelism-aware, decentralized Data Coordinator that leverages local caching, load balancing, and asynchronous double buffer to minimize communication overhead and mitigate straggler effects. For control logic, it introduces a task scheduler built upon Directed Acyclic Graph (DAG) that facilitates fine-grained, independent execution. Experimental results demonstrate that DistFlow achieves near-linear scalability up to 512 GPUs and delivers up to a 2.63x throughput improvement over state-of-the-art (SOTA) frameworks.

Theory · Reinforcement Learning and Planning

Yifan Jiang, Jiasheng Pan, Mengtian Li, Li Jin

We study decentralized multi-agent reinforcement learning (MARL) for networked service systems with affinity in the presence of Byzantine nodes. The way that a server processes a job depends on an affinity state that captures the correlation between the job and the server. Each node learns a local control policy via an actor-critic algorithm with linear function approximation over inherently unbounded space of traffic states, while exchanging parameter information with neighbors through a communication graph. A set of Byzantine agents can exploit the unbounded state space and the resulting stochastic variance to compromise the consensus mechanism, destabilizing both learning and queuing processes. To address this vulnerability, we propose a resilient consensus-based MARL algorithm with momentum-based smoothing, which mitigates adversarial parameter manipulation and guarantees traffic stability under mild assumptions. We prove that the cooperative agents’ policies converge almost surely to a bounded neighborhood of a stationary solution of the global objective. We demonstrate the effectiveness and generality of the proposed framework in several representative service systems, including semantic routing for large language model serving, distributed polling in cloud computing, and smart manufacturing logistics.

Applications · Chemistry, Physics, and Earth Sciences

Minghan Li, fengji Li, Yilin Tao, Yue Deng

Computational protein design typically employs a sequential workflow of structure generation followed by sequence (re)design. While structure generators can be explicitly conditioned on functional objectives, inverse folding models are constrained by their function-agnostic nature and sequence-structure degeneracy. More critically, the associated training objectives do not account for the *Best-of-N* (BoN) inference protocol, resulting in a fundamental training-inference misalignment. Here, we propose FIDIA, a reinforcement learning framework that enables **F**unction-**I**nformed sequence **D**esign via **I**nference-**A**ligned policy optimization. Specifically, FIDIA integrates functional constraints into composite rewards and explicitly optimize the induced policy under BoN toward high-fitness sequence regions. We achieve this via a grounded gradient estimator that directly maximizes the expected maximum reward. FIDIA consistently outperforms both standard and RL-optimized baselines in success rate and precision on a general motif scaffolding benchmark. Further experiments on realworld cases including vaccine and affinity-enhancing enzyme design validate FIDIA’s efficacy in complex therapeutic and biocatalytic contexts.

Applications · Robotics

Zhiyu Huang, Yun Zhang, Johnson Liu, Rui Song, Chen Tang, Jiaqi Ma

Robots in dynamic, human-centric environments must follow language instructions while maintaining real-time reactive control. Vision-language-action (VLA) models offer a promising framework, but they assume temporally aligned reasoning and control, despite semantic inference being inherently delayed relative to real-time action. We introduce Think-in-Control (TIC)-VLA, a latency-aware framework that explicitly models delayed semantic reasoning during action generation. TIC-VLA defines a delayed semantic-control interface that conditions action generation on delayed vision-language semantic states and explicit latency metadata, in addition to current observations. We further propose a latency-consistent training pipeline that injects reasoning inference delays during imitation learning and online reinforcement learning, aligning training with asynchronous deployment. To support realistic evaluation, we present DynaNav, a physics-accurate, photo-realistic simulation suite for language-guided navigation in dynamic environments. Extensive experiments in simulation and on a real robot show that TIC-VLA consistently outperforms prior VLA models while maintaining robust real-time control under multi-second reasoning latency. Code, data, and benchmarks will be released for reproducibility.

Deep Learning · Generative Models and Autoencoders

Yu-Yang Qian, Junda Su, Lanxiang Hu, Peiyuan Zhang, Zhijie Deng, Peng Zhao, Hao Zhang

Diffusion large language models (dLLMs) offer capabilities beyond those of autoregressive (AR) LLMs, such as parallel decoding and random-order generation. However, realizing these benefits in practice is non-trivial, as dLLMs inherently face an *accuracy-parallelism trade-off*. Despite increasing interest, existing methods typically focus on only one-side of the coin, targeting either efficiency or performance. To address this limitation, we propose d3LLM (*Pseudo-Distilled Diffusion Large Language Model*), striking a balance between accuracy and parallelism: (i) during training, we introduce *pseudo-trajectory distillation* to teach the model which tokens can be decoded confidently at early steps, thereby improving parallelism; (ii) during inference, we employ *entropy-based multi-block decoding* with a KV-cache refresh mechanism to achieve high parallelism while maintaining accuracy. To better evaluate dLLMs, we also introduce AUP (*Accuracy Under Parallelism*), a new metric that jointly measures accuracy and parallelism. Experiments demonstrate that our d3LLM achieves up to $10\times$ speedup over vanilla LLaDA/Dream, and up to $5\times$ speedup over the AR models (Qwen-2.5-7B) without much accuracy degradation.

Probabilistic Methods · Spectral Methods

Genki Osada

While diffusion models enable new approaches for estimating Local Intrinsic Dimension (LID), existing methods fail in high-dimensional spaces where noise from vast normal directions overwhelms the tangent signal. We propose Local Hessian Spectral Dimension (LHSD), which resolves this by applying spectral filtering to the log-density Hessian, explicitly cutting off large eigenvalues associated with normal directions to count zero-curvature tangent directions. Implemented using Stochastic Lanczos Quadrature (SLQ), LHSD avoids full Hessian construction, achieving linear scalability with dimension $D$. Experiments on synthetic and real data confirm LHSD’s superior robustness and its utility in detecting memorization in large-scale diffusion models.

Theory · Reinforcement Learning and Planning

Vagul Mahadevan, Claire Chen, Shuze Liu, Shangtong Zhang

Stochastic approximations (SA)--algorithms which derive their power through the use of random, incremental updates--are at the heart of reinforcement learning (RL). Expanding the theory of SA has established rigorous results concerning the most important algorithms in RL, including stochastic gradient descent and temporal difference learning. In this work, we focus on two-timescale stochastic approximations, a class which notably includes temporal difference learning with gradient correction (TDC) and actor-critic methods. Prior work has developed stability (boundedness) and convergence criteria for two-timescale SA under i.i.d. noise, but analogous results for Markovian noise have remained elusive--a critical issue since RL data are generated by a Markov chain, making i.i.d. assumptions unrealistic. To address this gap, we present the first stability result and the first asymptotic convergence result for two-timescale schemes with Markovian noise under general, verifiable conditions--notably, without resorting to projected variants of the schemes or requiring the noise to be in a compact space. As a key application, we contribute the first asymptotic convergence proof of TDC, an off-policy prediction algorithm with linear approximation and eligibility traces. Together, our results extend SA theory, establishing the first theoretical foundation for analysis of two-timescale algorithms with the realistic noise models inherent to RL.

General Machine Learning · Causality

Shimeng Huang, Matthew Robinson, Francesco Locatello

Mendelian Randomization (MR) is a prominent observational epidemiological research method, designed to address unobserved confounding when estimating causal effects. It is closely related to instrumental variable (IV) methods, where genetic variants serve as instruments to infer causal relationships from observational data. However, the core assumptions required for valid IV analysis---particularly the independence between instruments and unobserved confounders---are untestable and often violated in practice. In MR, such violations commonly arise when genetic variants are correlated with environmental factors (e.g., population stratification and assortive mating), leading to confounding between instruments and outcomes. At the same time, MR studies increasingly include data collected across multiple environments or populations, providing an opportunity to address these violations. Leveraging this setting, we propose a representation learning framework that exploits multi-environment data to recover latent exogenous components of genetic instruments suitable for causal inference. We provide theoretical insights into when and how the learned components can act as valid instruments, and we demonstrate the effectiveness of our approach through simulations and semi-synthetic experiments using genetic data from the All of Us Biobank.

Deep Learning · Foundation Models

Jiaxing Qiu, Kaihua Hou, Roxana Daneshjou, Ahmed Alaa, Thomas Hartvigsen

Model editing aims to correct errors in large, pretrained models without altering unrelated behaviors. While some recent works have edited vision–language models (VLMs), no existing editors tackle reasoning-heavy tasks, which typically require humans and models to reason about images. We therefore propose ReasonEdit, the first VLM editor to let users explain their reasoning during editing, introducing a new, practical model editing setup. ReasonEdit continuously stores human reasoning in a codebook, and retrieves only relevant facts during inference using a novel topology-balanced multimodal embedding method inspired by network science. Across four VLMs on multiple rationale-based visual question answering datasets, ReasonEdit achieves state-of-the-art editing performance, ultimately showing that using human reasoning during editing greatly improves edit generalization.

Deep Learning · Robustness

Ruize Zhang, Yu Li, Zhang Wan, Juan Cao, Jie Zhang, Sheng Tang

Recent test-time defenses for CLIP claim to preserve zero-shot clean accuracy while improving adversarial robustness. However, we find the reported robustness of six recent proposed state-of-the-art methods substantially overestimated: they fail under basic adaptive attacks. We further observe that these defenses share a common reliance on an indicative measurement that is assumed to capture the distributional difference between clean and adversarial samples and to determine whether the defense should preserve or alter the static model’s prediction. We argue that this assumption is the fundamental weakness, and we propose CLIP-MAD (Manipulating Assumed Difference), an adaptive attack strategy designed to break it. CLIP-MAD efficiently expands the adversarial distribution without costly full gradient calculations and can be flexibly combined with existing attack baselines to further boost attack strength. Experiments across 13 datasets demonstrate that CLIP-MAD produces strong adversarial samples that markedly reduce the robustness of diverse test-time defenses, revealing a false sense of security in CLIP’s zero-shot robustness.

Deep Learning · Generative Models and Autoencoders

Nicolas Dufour, Lucas Degeorge, Arijit Ghosh, Vicky Kalogeiton, David Picard

The default paradigm of post-training text-to-image generators includes post-hoc selection of generated images, and subsequent training with one reward model to align the generator to the reward, typically user preference. This discards informative data as well as optimizes only for a single reward, hence harming diversity, semantic fidelity and efficiency. Instead, we propose MIRO, a method that conditions the model on multiple rewards during training, thus letting the model learn user preferences directly. MIRO pre-training both improves the visual quality of the generated images and speeds up the training, achieving state of the art on the GenEval compositional benchmark and user-preference scores (PickAScore, ImageReward, HPSv2).

Applications · Robotics

Rufeng Chen, Yue Chang, Xiaqiang Tang, Hechang Chen, Sihong Xie

Open-vocabulary navigation requires embodied agents to manage significant perception uncertainty stemming from semantic ambiguity and model errors. However, most existing works settle for local optimal deterministic approaches, depriving complex navigation decision-making over multiple composite possibilities that are critical for globally better solutions. In this paper, we propose Probabilistic Scene Graph Navigation (PSG-Nav), which constructs a 3D Probabilistic Scene Graph that uses full semantic categorical distributions to account for perception uncertainty. To efficiently use the local distributions to compose and reason about the optimal navigation landmarks, we propose Multiverse Decision to sample multiple most likely world settings from the joint distribution, and evaluate navigation landmarks based on the compatibility between landmarks and multiverses. To mitigate false positives due to epistemic uncertainty in open-vocabulary navigation, we introduce the Evidential Experience Calibrator, which enables online lifelong adaptation by cross-validating detections against memories of past successes and failures. Extensive experiments on widely-used benchmarks MP3D, HD3D, and HSSD demonstrate that PSG-Nav establishes new state-of-the-art results, achieving a Success Rate of 66.1%, 44.8%, and 67.9%, respectively.

Theory · Reinforcement Learning and Planning

Muhammed Emrullah Ildiz, Halil Alperen Gozeten, Ege Onur Taga, Samet Oymak

State-of-the-art reasoning models can utilize long chain-of-thought to solve sophisticated coding and math problems. During this process, the model often attemps at a solution multiple times by utilizing verification and self-reflection capabilities. In this work, we view a long CoT as a process where the model makes K attempts at solving a problem in which each attempt is allowed to build on earlier solutions. This way, we formalize long CoT as a pass@K problem with dependent samples. Under this formalism, we derive the policy gradient and RL algorithms for optimizing long CoT reward and derive how each attempt should be weighed for unbiased gradient computation while maintaining small variance. Our theory reveals how the self-correction capability and dense feedback influence the training and eventual performance of long CoT-based reasoning. We provide both synthetic and real experiments corroborating our theory and the benefits of the associated algorithms. As a by product, our research also reveals when verification and long chain-of-thought is beneficial over parallel sampling strategies and the role of the model capability.

General Machine Learning · Kernel methods

Johannes Teutsch, Oleksii Molodchyk, Marion Leibold, Timm Faulwasser, Armin Lederer

Providing non-conservative uncertainty quantification for function estimates derived from noisy observations remains a fundamental challenge in statistical machine learning, particularly for applications in safety-critical domains. In this work, we propose novel non-asymptotic probabilistic uniform error bounds for kernel-based regression. Compared to related bounds in the literature that are restricted to (conditionally) independent sub-Gaussian noise, our bounds allow to consider a broad class of non-Gaussian distributions, such as sub-Gaussian, bounded, sub-exponential, and variance-bounded noise. Moreover, our results apply to correlated and uncorrelated noise. We compare our proposed error bounds with existing results in terms of the induced uncertainty region and their performance in safe control, demonstrating the tightness of the proposed bounds.

General Machine Learning · Evaluation

Drew Keller, Kweku Kwegyir-Aggrey, Ryan Steed, Anita Rao, Julia Sharp, A. Bergman

Benchmarks are widely used to evaluate and compare the performance of artificial intelligence systems. However, some approaches to computing benchmark metrics produce invalid uncertainty estimates or make unrecognized assumptions about the evaluation setting. We leverage statistical modeling to make two contributions to the practice of AI benchmarking. First, we formally distinguish measurements of benchmark accuracy from generalized accuracy (performance on all potential test items similar to those included in the benchmark). Then, in a simulated setting and with large-scale evaluation of 22 API-access frontier large language models on 3 popular benchmarks, we show how analysis via generalized linear mixed model can estimate generalized accuracy while more efficiently quantifying uncertainty compared to existing regression-free approaches. We also show how this approach can equip evaluators with important context on evaluation results, including variance decomposition and item difficulty estimates that illuminate important aspects of LLM performance and benchmark construction.

Deep Learning · Other Representation Learning

Yuwei Wang, Guikun Chen, Xiruo Jiang, Yazhou Yao, Di Liu, Xiangbo Shu, Fumin Shen, Wenguan Wang

Recent advances in visual representation learning have seen the rise of clustering-based vision backbones, which adopt clustering as a core paradigm for feature extraction. However, existing clustering-based backbones typically rely on a single clustering algorithm, whose inherent inductive bias limits their representational capacity. To address this, we propose EnFormer, which embeds ensemble clustering as a core component of feature extraction. EnFormer structures feature extraction around two steps: (i) Ensemble Generation, where several differentiable base clustering methods are introduced to capture diverse semantic structures; and (ii) Consensus Aggregation, which employs a differentiable mechanism to fuse the results of all base clusterings to reconstruct refined visual features. Extensive experiments show that EnFormer consistently outperforms existing clustering-based backbones across core vision tasks, with higher performance and significantly improved throughput.