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Poster | Actial: Activate Spatial Reasoning Ability of Multimodal Large Language Models | https://neurips.cc//virtual/2025/poster/116431 | Xiaoyu Zhan, Wenxuan Huang, Hao Sun, Xinyu Fu, Changfeng Ma, Shaosheng Cao, Bohan Jia, Shaohui Lin, Zhenfei Yin, LEI BAI, Wanli Ouyang, Yuanqi Li, Jie Guo, Yanwen Guo | Recent advances in Multimodal Large Language Models (MLLMs) have significantly improved 2D visual understanding, prompting interest in their application to complex 3D reasoning tasks. However, it remains unclear whether these models can effectively capture the detailed spatial information required for robust real-world... |
Poster | Activated LoRA: Fine-tuned LLMs for Intrinsics | https://neurips.cc//virtual/2025/poster/117964 | Kristjan Greenewald, Luis Lastras, Thomas Parnell, Vraj Shah, Lucian Popa, Giulio Zizzo, Chulaka Gunasekara, Ambrish Rawat, David Cox | Low-Rank Adaptation (LoRA) has emerged as a highly efficient framework for finetuning the weights of large foundation models, and has become the go-to method for data-driven customization of LLMs. Despite the promise of highly customized behaviors and capabilities, switching between relevant LoRAs in a multiturn settin... |
Poster | Activation Control for Efficiently Eliciting Long Chain-of-thought Ability of Language Models | https://neurips.cc//virtual/2025/poster/117520 | Zekai Zhao, Qi Liu, Kun Zhou, Zihan Liu, Yifei Shao, Zhiting Hu, Biwei Huang | Despite the remarkable reasoning performance, eliciting the long chain-of-thought(CoT) ability in large language models(LLMs) typically requires costly reinforcement learning or supervised fine-tuning on high-quality distilled data. We investigate the internal mechanisms behind this capability and show that a small set... |
Poster | Activation-Guided Consensus Merging for Large Language Models | https://neurips.cc//virtual/2025/poster/117217 | Yuxuan Yao, Shuqi LIU, Zehua Liu, Qintong Li, Mingyang LIU, Xiongwei Han, Zhijiang Guo, Han Wu, Linqi Song | Recent research has increasingly focused on reconciling the reasoning capabilities of System 2 with the efficiency of System 1. While existing training-based and prompt-based approaches face significant challenges in terms of efficiency and stability, model merging emerges as a promising strategy to integrate the diver... |
Poster | Activation-Informed Merging of Large Language Models | https://neurips.cc//virtual/2025/poster/117880 | Amin Heyrani Nobari, Kaveh Alimohammadi, Ali ArjomandBigdeli, Akash Srivastava, Faez Ahmed, Navid Azizan | Model merging, a method that combines the parameters and embeddings of multiple fine-tuned large language models (LLMs), offers a promising approach to enhance model performance across various tasks while maintaining computational efficiency. This paper introduces Activation-Informed Merging (AIM), a technique that int... |
Poster | Active Measurement: Efficient Estimation at Scale | https://neurips.cc//virtual/2025/poster/116156 | Max Hamilton, Jinlin Lai, Wenlong Zhao, Subhransu Maji, Daniel Sheldon | AI has the potential to transform scientific discovery by analyzing vast datasets with little human effort. However, current workflows often do not provide the accuracy or statistical guarantees that are needed. We introduce \emph{active measurement}, a human-in-the-loop AI framework for scientific measurement. An AI m... |
Poster | Active Seriation | https://neurips.cc//virtual/2025/poster/115335 | James Cheshire, Yann Issartel | We consider the problem of active seriation, in which the learner must order $n$ items from $T$ sequentially observed pairwise similarities. Items are ordered according to their pairwise similarities: two items sharing a high similarity are expected to be close in the ordering, and vise-versa. At each round, the learne... |
Poster | Active Target Discovery under Uninformative Priors: The Power of Permanent and Transient Memory | https://neurips.cc//virtual/2025/poster/115332 | Anindya Sarkar, Binglin Ji, Yevgeniy Vorobeychik | In many scientific and engineering fields, where acquiring high-quality data is expensive—such as medical imaging, environmental monitoring, and remote sensing—strategic sampling of unobserved regions based on prior observations is crucial for maximizing discovery rates within a constrained budget. The rise of powerful... |
Poster | Active Test-time Vision-Language Navigation | https://neurips.cc//virtual/2025/poster/119128 | Heeju Ko, Sung June Kim, Gyeongrok Oh, Jeongyoon Yoon, Honglak Lee, Sujin Jang, Seungryong Kim, Sangpil Kim | Vision-Language Navigation (VLN) policies trained on offline datasets often exhibit degraded task performance when deployed in unfamiliar navigation environments at test time, where agents are typically evaluated without access to external interaction or feedback. Entropy minimization has emerged as a practical solutio... |
Poster | ActiveVOI: Value of Information Guided Active Knowledge Acquisition for Open-World Embodied Lifted Regression Planning | https://neurips.cc//virtual/2025/poster/117082 | Xiatoian Liu, Ali Pesaranghader, Jaehong Kim, Tanmana Sadhu, Hyejeong Jeon, Scott Sanner | The ability to actively acquire information is essential for open-world planning under partial observability and incomplete knowledge. Existing embodied AI systems typically rely on passive strategies that exhaustively collect object and relational information. However, such passive knowledge acquisition becomes imprac... |
Poster | Activity Pruning for Efficient Spiking Neural Networks | https://neurips.cc//virtual/2025/poster/115023 | Tong Bu, Xinyu Shi, Zhaofei Yu | While sparse coding plays an important role in promoting the efficiency of biological neural systems, it has not been fully utilized by artificial models as the activation sparsity is not well suited to the current structure of deep networks. Spiking Neural Networks (SNNs), with their event-driven characteristics, offe... |
Poster | Act Only When It Pays: Efficient Reinforcement Learning for LLM Reasoning via Selective Rollouts | https://neurips.cc//virtual/2025/poster/115272 | Haizhong Zheng, Yang Zhou, Brian Bartoldson, Bhavya Kailkhura, Fan Lai, Jiawei Zhao, Beidi Chen | Reinforcement learning, such as PPO and GRPO, has powered recent breakthroughs in LLM reasoning. Scaling rollout to sample more prompts enables models to selectively use higher-quality data for training, which can stabilize RL training and improve model performance, but at the cost of significant computational overhead... |
Poster | Actor-Free Continuous Control via Structurally Maximizable Q-Functions | https://neurips.cc//virtual/2025/poster/118279 | Yigit Korkmaz, Urvi Bhuwania, Ayush Jain, Erdem Bıyık | Value-based algorithms are a cornerstone of off-policy reinforcement learning due to their simplicity and training stability. However, their use has traditionally been restricted to discrete action spaces, as they rely on estimating Q-values for individual state-action pairs. In continuous action spaces, evaluating the... |
Poster | Act to See, See to Act: Diffusion-Driven Perception-Action Interplay for Adaptive Policies | https://neurips.cc//virtual/2025/poster/118213 | Jing Wang, Weiting Peng, Jing Tang, Zeyu Gong, Xihua Wang, Bo Tao, Li cheng | Existing imitation learning methods decouple perception and action, which overlooks the causal reciprocity between sensory representations and action execution that humans naturally leverage for adaptive behaviors. To bridge this gap, we introduce Action-Guided Diffusion Policy (DP-AG), a unified representation learnin... |
Poster | AcuRank: Uncertainty-Aware Adaptive Computation for Listwise Reranking | https://neurips.cc//virtual/2025/poster/118910 | Soyoung Yoon, Gyuwan Kim, Gyu-Hwung Cho, seung-won hwang | Listwise reranking with large language models (LLMs) enhances top-ranked results in retrieval-based applications.Due to the limit in context size and high inference cost of long context, reranking is typically performed over a fixed size of small subsets, with the final ranking aggregated from these partial results.Thi... |
Poster | AdaDetectGPT: Adaptive Detection of LLM-Generated Text with Statistical Guarantees | https://neurips.cc//virtual/2025/poster/120036 | Hongyi Zhou, Jin Zhu, Pingfan Su, Kai Ye, Ying Yang, Shakeel Gavioli-Akilagun, Chengchun Shi | We study the problem of determining whether a piece of text has been authored by a human or by a large language model (LLM). Existing state of the art logits-based detectors make use of statistics derived from the log-probability of the observed text evaluated using the distribution function of a given source LLM. Howe... |
Poster | Ada-KV: Optimizing KV Cache Eviction by Adaptive Budget Allocation for Efficient LLM Inference | https://neurips.cc//virtual/2025/poster/115578 | Yuan Feng, Junlin Lv, Yukun Cao, Xike Xie, S. Kevin Zhou | Large Language Models have excelled in various domains but face efficiency challenges due to the growing Key-Value (KV) cache required for long-sequence inference. Recent efforts aim to reduce KV cache size by evicting vast non-critical cache elements during runtime while preserving generation quality. However, these m... |
Poster | AdaLRS: Loss-Guided Adaptive Learning Rate Search for Efficient Foundation Model Pretraining | https://neurips.cc//virtual/2025/poster/118011 | Hongyuan Dong, Dingkang Yang, LiangXiao, ChaoFeng, Ran Jiao | Learning rate is widely regarded as crucial for effective foundation model pretraining.Recent research explores and demonstrates the transferability of learning rate configurations across varying model and dataset sizes, etc. Nevertheless, these approaches are constrained to specific training scenarios and typically ne... |
Poster | Adam Reduces a Unique Form of Sharpness: Theoretical Insights Near the Minimizer Manifold | https://neurips.cc//virtual/2025/poster/116405 | Xinghan Li, Haodong Wen, Kaifeng Lyu | Despite the popularity of Adam optimizer in practice, most theoretical analyses study SGD as a proxy and little is known about how the solutions found by Adam differ. In this paper, we show that Adam reduces a specific form of sharpness measure shaped by its adaptive updates, leading to qualitatively different solution... |
Poster | AdaMSS: Adaptive Multi-Subspace Approach for Parameter-Efficient Fine-Tuning | https://neurips.cc//virtual/2025/poster/119606 | Jingjing Zheng, Wanglong Lu, Yiming Dong, Chaojie Ji, Yankai Cao, Zhouchen Lin | In this paper, we propose AdaMSS, an adaptive multi-subspace approach for parameter-efficient fine-tuning of large models. Unlike traditional parameter-efficient fine-tuning methods that operate within a large single subspace of the network weights, AdaMSS leverages subspace segmentation to obtain multiple smaller sub... |
Poster | AdaPA-Agent: A Personalized Agent with Adaptive Preference Arithmetic for Dynamic Preference Modeling | https://neurips.cc//virtual/2025/poster/116708 | Hongyi Nie, Yaqing Wang, Mingyang Zhou, Feiyang Pan, Quanming Yao, Zhen Wang | As large language models (LLMs) are increasingly used as personalized user assistants, effectively adapting to users' evolving preferences is critical for delivering high-quality personalized responses. While user preferences are often stable in content, their relative strengths shift over time due to changing goals an... |
Poster | Adaptable Safe Policy Learning from Multi-task Data with Constraint Prioritized Decision Transformer | https://neurips.cc//virtual/2025/poster/118870 | Ruiqi Xue, Ziqian Zhang, Lihe Li, Cong Guan, Lei Yuan, Yang Yu | Learning safe reinforcement learning (RL) policies from offline multi-task datasets without direct environmental interaction is crucial for efficient and reliable deployment of RL agents. Benefiting from their scalability and strong in-context learning capabilities, recent approaches attempt to utilize Decision Transfo... |
Poster | AdaptDel: Adaptable Deletion Rate Randomized Smoothing for Certified Robustness | https://neurips.cc//virtual/2025/poster/115261 | Zhuoqun Huang, Neil Marchant, Olga Ohrimenko, Benjamin Rubinstein | We consider the problem of certified robustness for sequence classification against edit distance perturbations. Naturally occurring inputs of varying lengths (e.g., sentences in natural language processing tasks) present a challenge to current methods that employ fixed-rate deletion mechanisms and lead to suboptimal p... |
Poster | AdaptGrad: Adaptive Sampling to Reduce Noise | https://neurips.cc//virtual/2025/poster/116129 | Linjiang Zhou, Chao Ma, Zepeng Wang, Libing Wu, XIAOCHUAN SHI | Gradient smoothing is an efficient approach to reducing noise in gradient-based model explanation methods. SmoothGrad adds Gaussian noise to mitigate much of this noise. However, the crucial hyperparameter in this method, the variance $\sigma$ of the Gaussian noise, is often set manually or determined using a heuristic... |
Poster | Adapting Frame-Based Networks for Stable and Robust Video Inference | https://neurips.cc//virtual/2025/poster/118835 | Matthew Dutson, Nathan Labiosa, Yin Li, Mohit Gupta | Frame-based neural networks often exhibit temporal inconsistency, i.e., flickering or incoherent predictions, when applied to videos. This problem is amplified when the input frames contain time-varying corruptions such as noise. Many prior works propose stable video-optimized networks for individual tasks or groups of... |
Poster | Adapting to Stochastic and Adversarial Losses in Episodic MDPs with Aggregate Bandit Feedback | https://neurips.cc//virtual/2025/poster/119322 | Shinji Ito, Kevin Jamieson, Haipeng Luo, Arnab Maiti, Taira Tsuchiya | We study online learning in finite-horizon episodic Markov decision processes (MDPs) under the challenging \textit{aggregate bandit feedback} model,where the learner observes only the cumulative loss incurred in each episode,rather than individual losses at each state-action pair.While prior work in this setting has fo... |
Poster | Adaptive 3D Reconstruction via Diffusion Priors and \\Forward Curvature-Matching Likelihood Updates | https://neurips.cc//virtual/2025/poster/118820 | Seunghyeok Shin, Dabin Kim, Hongki Lim | Reconstructing high-quality point clouds from images remains challenging in computer vision. Existing generative model, particularly diffusion model, based approaches that directly learn the posterior may suffer from inflexibility—they require conditioning signals during training, support only fixed numbers of input vi... |
Poster | Adaptive Algorithms with Sharp Convergence Rates for Stochastic Hierarchical Optimization | https://neurips.cc//virtual/2025/poster/119024 | Xiaochuan Gong, Jie Hao, Mingrui Liu | Hierarchical optimization refers to problems with interdependent decision variables and objectives, such as minimax and bilevel formulations. While various algorithms have been proposed, existing methods and analyses lack adaptivity in stochastic optimization settings: they cannot achieve optimal convergence rates acro... |
Poster | Adaptive and Multi-scale Affinity Alignment for Hierarchical Contrastive Learning | https://neurips.cc//virtual/2025/poster/115680 | Jiawei Huang, Minming Li, Hu Ding | Contrastive self-supervised learning has emerged as a powerful paradigm for extracting meaningful representations without labels. While effective at capturing broad categorical distinctions, current methods often struggle to preserve the fine-grained and hierarchical relationships inherent in real-world data. From the ... |
Poster | Adaptive Classifier-Free Guidance via Dynamic Low-Confidence Masking | https://neurips.cc//virtual/2025/poster/120339 | Pengxiang Li, Shilin Yan, Jiayin Cai, Renrui Zhang, Ruichuan An, Ziyu Guo, Xiaowei Gao | Classifier-Free Guidance (CFG) significantly enhances controllability in generative models by interpolating conditional and unconditional predictions. However, standard CFG often employs a static unconditional input, which can be suboptimal for iterative generation processes where model uncertainty varies dynamically. ... |
Poster | Adaptive Context Length Optimization with Low-Frequency Truncation for Multi-Agent Reinforcement Learning | https://neurips.cc//virtual/2025/poster/119966 | Wenchang Duan, Yaoliang Yu, Jiwan He, Yi Shi | Recently, deep multi-agent reinforcement learning (MARL) has demonstrated promising performance for solving challenging tasks, such as long-term dependencies and non-Markovian environments. Its success is partly attributed to conditioning policies on large fixed context length. However, such large fixed context lengths... |
Poster | Adaptive Correction During the LLM Forward Pass | https://neurips.cc//virtual/2025/poster/119727 | Arjun Patrawala, Jiahai Feng, Erik Jones, Jacob Steinhardt | Recent methods in language model interpretability employ sparse autoencoders and other techniques to decompose residual stream contributions into linear, semantically-meaningful features. Our work demonstrates that the underlying assumption of these methods—that residual stream contributions build additively upon each ... |
Poster | Adaptive Cyclic Diffusion for Inference Scaling | https://neurips.cc//virtual/2025/poster/116439 | Gyubin Lee, Bao Truong, Jaesik Yoon, Dongwoo Lee, Minsu Kim, Yoshua Bengio, Sungjin Ahn | Diffusion models have demonstrated strong generative capabilities across domains ranging from image synthesis to complex reasoning tasks. However, most inference-time scaling methods rely on fixed denoising schedules, limiting their ability to allocate computation based on instance difficulty or task-specific demands a... |
Poster | Adaptive Data Analysis for Growing Data | https://neurips.cc//virtual/2025/poster/118935 | Neil Marchant, Benjamin Rubinstein | Reuse of data in adaptive workflows poses challenges regarding overfitting and the statistical validity of results. Previous work has demonstrated that interacting with data via differentially private algorithms can mitigate overfitting, achieving worst-case generalization guarantees with asymptotically optimal... |
Poster | Adaptive Data-Borrowing for Improving Treatment Effect Estimation using External Controls | https://neurips.cc//virtual/2025/poster/118271 | Qinwei Yang, Jingyi Li, Peng Wu | Randomized controlled trials (RCTs) often exhibit limited inferential efficiency in estimating treatment effects due to small sample sizes. In recent years, the combination of external controls has gained increasing attention as a means of improving the efficiency of RCTs. However, external controls are not always comp... |
Poster | Adaptive Defense against Harmful Fine-Tuning via Bayesian Data Scheduler | https://neurips.cc//virtual/2025/poster/115659 | Zixuan Hu, Li Shen, Zhenyi Wang, Yongxian Wei, Dacheng Tao | Harmful fine-tuning poses critical safety risks to fine-tuning-as-a-service for large language models. Existing defense strategies preemptively build robustness via attack simulation but suffer from fundamental limitations: (i) the infeasibility of performing attack simulation due to lacking prior knowledge about poten... |
Poster | Adaptive Discretization for Consistency Models | https://neurips.cc//virtual/2025/poster/119050 | Jiayu Bai, Zhanbo Feng, Zhijie Deng, TianQi Hou, Robert Qiu, Zenan Ling | Consistency Models (CMs) have shown promise for efficient one-step generation. However, most existing CMs rely on manually designed discretization schemes, which can cause repeated adjustments for different noise schedules and datasets. To address this, we propose a unified framework for the automatic and adaptive disc... |
Poster | Adaptive Distraction: Probing LLM Contextual Robustness with Automated Tree Search | https://neurips.cc//virtual/2025/poster/115688 | Yanbo Wang, Zixiang Xu, Yue Huang, Gao Chujie, Siyuan Wu, Jiayi Ye, Pin-Yu Chen, Xiuying Chen, Xiangliang Zhang | Large Language Models (LLMs) often struggle to maintain their original performance when faced with semantically coherent but task-irrelevant contextual information. Although prior studies have explored this issue using fixed-template or retrieval-based distractions, such static methods show limited effectiveness agains... |
Poster | Adaptive Divergence Regularized Policy Optimization for Fine-tuning Generative Models | https://neurips.cc//virtual/2025/poster/117259 | Jiajun Fan, Tong Wei, Chaoran Cheng, Yuxin Chen, Ge Liu | Balancing exploration and exploitation during reinforcement learning fine-tuning of generative models presents a critical challenge, as existing approaches rely on fixed divergence regularization that creates an inherent dilemma: strong regularization preserves model capabilities but limits reward optimization, while w... |
Poster | Adaptive Fission: Post-training Encoding for Low-latency Spike Neural Networks | https://neurips.cc//virtual/2025/poster/120078 | Yizhou Jiang, Tianren Zhang, Yihan Li, Yuqian Liu, Haichuan Gao, Ying Fang, Feng Chen | Spiking Neural Networks often rely on rate coding, where high-precision inference requires long time-steps, leading to significant latency and energy cost—especially for ANN-to-SNN conversions. To address this, we propose Adaptive Fission, a post-training encoding technique that selectively splits high-sensitivity neur... |
Poster | Adaptive Frontier Exploration on Graphs with Applications to Network-Based Disease Testing | https://neurips.cc//virtual/2025/poster/116207 | XianJun, Davin Choo, Yuqi Pan, Tonghan Wang, Milind Tambe, Alastair van Heerden, Cheryl Johnson | We study a sequential decision-making problem on a $n$-node graph $\mathcal{G}$ where each node has an unknown label from a finite set $\mathbf{\Sigma}$, drawn from a joint distribution that is Markov with respect to $\mathcal{G}$. At each step, selecting a node reveals its label and yields a label-dependent reward. Th... |
Poster | Adaptive Gradient Masking for Balancing ID and MLLM-based Representations in Recommendation | https://neurips.cc//virtual/2025/poster/117400 | Yidong Wu, Siyuan Chen, Binrui Wu, Fan Li, Jiechao Gao | In large-scale recommendation systems, multimodal content is increasingly introduced to enhance the generalization of ID features.The rise of Multimodal Large Language Models (MLLMs) enables the construction of unified user and item representations.However, the semantic distribution gap between MM and ID representation... |
Poster | Adaptive Kernel Design for Bayesian Optimization Is a Piece of CAKE with LLMs | https://neurips.cc//virtual/2025/poster/119378 | Richard Suwandi, Feng Yin, Juntao Wang, Renjie Li, Tsung-Hui Chang, Sergios Theodoridis | The efficiency of Bayesian optimization (BO) relies heavily on the choice of the Gaussian process (GP) kernel, which plays a central role in balancing exploration and exploitation under limited evaluation budgets. Traditional BO methods often rely on fixed or heuristic kernel selection strategies, which can result in s... |
Poster | Adaptive Latent-Space Constraints in Personalized FL | https://neurips.cc//virtual/2025/poster/116101 | Sana Ayromlou, David B. Emerson | Federated learning (FL) has become an effective and widely used approach to training deep learning models on decentralized datasets held by distinct clients. FL also strengthens both security and privacy protections for training data. Common challenges associated with statistical heterogeneity between distributed datas... |
Poster | Adaptive LoRA Experts Allocation and Selection for Federated Fine-Tuning | https://neurips.cc//virtual/2025/poster/116881 | Lei Wang, Jieming Bian, Letian Zhang, Jie Xu | Large Language Models (LLMs) have demonstrated impressive capabilities across various tasks, but fine-tuning them for domain-specific applications often requires substantial domain-specific data that may be distributed across multiple organizations. Federated Learning (FL) offers a privacy-preserving solution, but face... |
Poster | Adaptively Coordinating with Novel Partners via Learned Latent Strategies | https://neurips.cc//virtual/2025/poster/118507 | Benjamin J Li, Shuyang Shi, Lucia Romero, Huao Li, Yaqi Xie, Woojun Kim, Stefanos Nikolaidis, Charles Lewis, Katia Sycara, Simon Stepputtis | Adaptation is the cornerstone of effective collaboration among heterogeneous team members. In human-agent teams, artificial agents need to adapt to their human partners in real time, as individuals often have unique preferences and policies that may change dynamically throughout interactions. This becomes particularly ... |
Poster | Adaptive Neighborhood-Constrained Q Learning for Offline Reinforcement Learning | https://neurips.cc//virtual/2025/poster/115847 | Yixiu Mao, Yun Qu, Qi Wang, Xiangyang Ji | Offline reinforcement learning (RL) suffers from extrapolation errors induced by out-of-distribution (OOD) actions. To address this, offline RL algorithms typically impose constraints on action selection, which can be systematically categorized into density, support, and sample constraints. However, we show that each c... |
Poster | Adaptive network automata modelling of complex networks for link prediction | https://neurips.cc//virtual/2025/poster/116787 | Jialin Zhao, Alessandro Muscoloni, Umberto Michieli, Yingtao Zhang, Carlo Cannistraci | Many complex networks have a connectivity that might be only partially detected or that tends to grow over time, hence the prediction of non-observed links is a fundamental problem in network science. The aim of topological link prediction is to forecast these non-observed links by only exploiting features intrinsic to... |
Poster | Adaptive Prediction-Powered AutoEval with Reliability and Efficiency Guarantees | https://neurips.cc//virtual/2025/poster/116118 | Sangwoo Park, Matteo Zecchin, Osvaldo Simeone | Selecting artificial intelligence (AI) models, such as large language models (LLMs), from multiple candidates requires accurate performance estimation. This is ideally achieved through empirical evaluations involving abundant real-world data. However, such evaluations are costly and impractical at scale. To address th... |
Poster | Adaptive Quantization in Generative Flow Networks for Probabilistic Sequential Prediction | https://neurips.cc//virtual/2025/poster/120256 | Nadhir Hassen, Zhen Zhang, Johan Verjans | Probabilistic time series forecasting, essential in domains like healthcare and neuroscience, requires models capable of capturing uncertainty and intricate temporal dependencies. While deep learning has advanced forecasting, generating calibrated probability distributions over continuous future values remains challeng... |
Poster | Adaptive Re-calibration Learning for Balanced Multimodal Intention Recognition | https://neurips.cc//virtual/2025/poster/116409 | Qu Yang, Xiyang Li, Fu Lin, Mang Ye | Multimodal Intention Recognition (MIR) plays a critical role in applications such as intelligent assistants, service robots, and autonomous systems. However, in real-world settings, different modalities often vary significantly in informativeness, reliability, and noise levels. This leads to modality imbalance, where m... |
Poster | Adaptive Riemannian ADMM for Nonsmooth Optimization: Optimal Complexity without Smoothing | https://neurips.cc//virtual/2025/poster/116261 | Kangkang Deng, Jiachen Jin, Jiang Hu, Hongxia Wang | We study the problem of minimizing the sum of a smooth function and a nonsmooth convex regularizer over a compact Riemannian submanifold embedded in Euclidean space. By introducing an auxiliary splitting variable, we propose an adaptive Riemannian alternating direction method of multipliers (ARADMM), which, for the fir... |
Poster | Adaptive Sample Scheduling for Direct Preference Optimization | https://neurips.cc//virtual/2025/poster/119641 | Zixuan Huang, Yikun Ban, Lean Fu, Xiaojie Li, Zhongxiang Dai, Jianxin Li, deqing wang | Direct Preference Optimization (DPO) has emerged as an effective approach for aligning large language models (LLMs) with human preferences. However, its performance is highly dependent on the quality of the underlying human preference data. To address this bottleneck, prior work has explored various data selection stra... |
Poster | Adaptive Sigmoid Clipping for Balancing the Direction–Magnitude Mismatch Trade-off in Differentially Private Learning | https://neurips.cc//virtual/2025/poster/119881 | Faeze Moradi Kalarde, Ali Bereyhi, Ben Liang, Min Dong | Differential privacy (DP) limits the impact of individual training data samples by clipping the sample gradients. Conventional clipping methods often assign unequal weights to the gradients, which prevents excessive magnitude attenuation after clipping. This however leads to a notable direction mismatch between the tru... |
Poster | Adaptive Stochastic Coefficients for Accelerating Diffusion Sampling | https://neurips.cc//virtual/2025/poster/119065 | Ruoyu Wang, Beier Zhu, Junzhi Li, Liangyu Yuan, Chi Zhang | Diffusion-based generative processes, grounded in differential equation solving, frequently require striking a balance between computational speed and output quality. Our theoretical investigation of prevalent solving approaches - ordinary differential equations (ODE) and stochastic differential equations (SDE) solvers... |
Poster | Adaptive Time Encoding for Irregular Multivariate Time-Series Classification | https://neurips.cc//virtual/2025/poster/116324 | Sangho Lee, Kyeongseo Min, Youngdoo Son, Hyungrok Do | Time series are often irregularly sampled with uneven time intervals. In multivariate cases, such irregularities may lead to misaligned observations across variables and varying observation counts, making it difficult to extract intrinsic patterns and degrading the classification performance of deep learning models. In... |
Poster | Adaptive Variance Inflation in Thompson Sampling: Efficiency, Safety, Robustness, and Beyond | https://neurips.cc//virtual/2025/poster/118839 | Feng Zhu, David Simchi-Levi | Thompson Sampling (TS) has emerged as a powerful algorithm for sequential decision-making, with strong empirical success and theoretical guarantees. However, it has been shown that its behavior under stringent safety and robustness criteria --- such as safety of cumulative regret distribution and robustness to model mi... |
Poster | Ada-R1: From Long-CoT to Hybrid-CoT via Bi-Level Adaptive Reasoning Optimization | https://neurips.cc//virtual/2025/poster/117295 | Haotian Luo, Haiying He, Yibo Wang, Jinluan Yang, Rui Liu, Naiqiang Tan, Xiaochun Cao, Dacheng Tao, Li Shen | Recently, long-thought reasoning models achieve strong performance on complex reasoning tasks, but often incur substantial inference overhead, making efficiency a critical concern. Our empirical analysis reveals that the benefit of using Long-CoT varies across problems: while some problems require elaborate reasoning, ... |
Poster | AdaReasoner: Adaptive Reasoning Enables More Flexible Thinking | https://neurips.cc//virtual/2025/poster/117660 | Xiangqi Wang, Yue Huang, Yanbo Wang, Xiaonan Luo, Kehan Guo, Yujun Zhou, Xiangliang Zhang | LLMs often need effective configurations, like temperature and reasoning steps, to handle tasks requiring sophisticated reasoning and problem-solving, ranging from joke generation to mathematical reasoning. Existing prompting approaches usually adopt general-purpose, fixed configurations that work “well enough” across ... |
Poster | AdaSPEC: Selective Knowledge Distillation for Efficient Speculative Decoders | https://neurips.cc//virtual/2025/poster/115055 | Yuezhou Hu, Jiaxin Guo, Xinyu Feng, Tuo Zhao | Speculative decoding (SD) accelerates large language model inference by employing a small draft model to generate predictions, which are then verified by a larger target model. The effectiveness of SD hinges on the alignment between these models, typically enhanced through knowledge distillation (KD). However, draft mo... |
Poster | AdaSTaR: Adaptive Data Sampling for Training Self-Taught Reasoners | https://neurips.cc//virtual/2025/poster/119242 | Reiss Koh, Wonbeen Oh, Jaein Jang, MinHyung Lee, Hyeongjin Kim, Ah Kim, Joonkee Kim, Junghyun Lee, Taehyeon Kim, Se-Young Yun | Self-Taught Reasoners (STaR), synonymously known as Rejection sampling Fine-Tuning (RFT), is an integral part of the training pipeline of self-improving reasoning Language Models (LMs). The self-improving mechanism often employs random observation (data) sampling. However, this results in trained observation imbalance;... |
Poster | A data and task-constrained mechanistic model of the mouse outer retina shows robustness to contrast variations | https://neurips.cc//virtual/2025/poster/117219 | Kyra Kadhim, Jonas Beck, Ziwei Huang, Jakob H Macke, Fred Rieke, Thomas Euler, Michael Deistler, Philipp Berens | Visual processing starts in the outer retina where photoreceptors transform light into electrochemical signals. These signals are modulated by inhibition from horizontal cells and sent to the inner retina via excitatory bipolar cells. The outer retina is thought to play an important role in contrast invariant coding of... |
Poster | A Data-Driven Prism: Multi-View Source Separation with Diffusion Model Priors | https://neurips.cc//virtual/2025/poster/119352 | Sebastian Wagner-Carena, Aizhan Akhmetzhanova, Sydney Erickson | In the natural sciences, a common challenge is to disentangle distinct, unknown sources from observations. Examples of this source separation task include deblending galaxies in a crowded field, distinguishing the activity of individual neurons from overlapping signals, and separating seismic events from the ambient ba... |
Poster | A Dataset for Distilling Knowledge Priors from Literature for Scientific Discovery | https://neurips.cc//virtual/2025/poster/121470 | Haydn Jones, Natalie Maus, Josh magnus Ludan, Maggie Huan, Jiaming Liang, Marcelo Der Torossian Torres, Jiatao Liang, Zachary Ives, Yoseph Barash, Cesar de la Fuente-Nunez, Jacob Gardner, Mark Yatskar | AI-driven discovery can greatly reduce design time and enhance new therapeutics’ effectiveness. Models using simulators explore broad design spaces but risk violating implicit constraints due to a lack of experimental priors. For example, in a new analysis we performed on a diverse set of models on the GuacaMol benchma... |
Poster | AdaTS: Learning Adaptive Time Series Representations via Dynamic Soft Contrasts | https://neurips.cc//virtual/2025/poster/116535 | Denizhan Kara, Tomoyoshi Kimura, Jinyang Li, Bowen He, Yizhuo Chen, Yigong Hu, Hongjue Zhao, Shengzhong Liu, Tarek Abdelzaher | Learning robust representations from unlabeled time series is crucial, and contrastive learning offers a promising avenue. However, existing contrastive learning approaches for time series often struggle with defining meaningful similarities, tending to overlook inherent physical correlations and diverse, sequence-vary... |
Poster | AdaVideoRAG: Omni-Contextual Adaptive Retrieval-Augmented for Efficient Long Video Understanding | https://neurips.cc//virtual/2025/poster/119056 | Xue zhucun, Jiangning Zhang, Xie Xurong, Yuxuan Cai, Yong Liu, Xiangtai Li, Dacheng Tao | Multimodal Large Language Models (MLLMs) have demonstrated excellent performance in video understanding but suffer from degraded effectiveness when processing long videos due to fixed-length contexts and weaknesses in modeling long-term dependencies. Retrieval-Augmented Generation (RAG) technology can mitigate these li... |
Poster | Addressing Mark Imbalance using Integration-free Marked Temporal Point Processes | https://neurips.cc//virtual/2025/poster/116275 | Sishun Liu, KE DENG, Xiuzhen Zhang, Yongli Ren, Yan Wang | Marked Temporal Point Process (MTPP) has been well studied to model the event distribution in marked event streams, which can be used to predict the mark and arrival time of the next event. However, existing studies overlook that the distribution of event marks is highly imbalanced in many real-world applications, with... |
Poster | ADG: Ambient Diffusion-Guided Dataset Recovery for Corruption-Robust Offline Reinforcement Learning | https://neurips.cc//virtual/2025/poster/119600 | Zeyuan Liu, Zhihe Yang, Jiawei Xu, Rui Yang, Jiafei Lyu, Baoxiang Wang, Yunjian Xu, Xiu Li | Real-world datasets collected from sensors or human inputs are prone to noise and errors, posing significant challenges for applying offline reinforcement learning (RL). While existing methods have made progress in addressing corrupted actions and rewards, they remain insufficient for handling corruption in high-dimens... |
Poster | A Difference-of-Convex Functions Approach to Energy-Based Iterative Reasoning | https://neurips.cc//virtual/2025/poster/118080 | Daniel Tschernutter, David Castro, Maciej Kasiński | While energy-based models have recently proven to be a powerful framework for learning to reason with neural networks, their practical application is still limited by computational cost. That is, existing methods for energy-based iterative reasoning suffer from computational bottlenecks by relying on expensive optimiza... |
Poster | A Diffusion Model for Regular Time Series Generation from Irregular Data with Completion and Masking | https://neurips.cc//virtual/2025/poster/118491 | Gal Fadlon, Idan Arbiv, Nimrod Berman, Omri Azencot | Generating realistic time series data is critical for applications in healthcare, finance, and climate science. However, irregular sampling and missing values present significant challenges. While prior methods address these irregularities, they often yield suboptimal results and incur high computational costs. Recent ... |
Poster | Adjacent Words, Divergent Intents: Jailbreaking Large Language Models via Task Concurrency | https://neurips.cc//virtual/2025/poster/118973 | Yukun Jiang, Mingjie Li, Michael Backes, Yang Zhang | Despite their superior performance on a wide range of domains, large language models (LLMs) remain vulnerable to misuse for generating harmful content, a risk that has been further amplified by various jailbreak attacks.Existing jailbreak attacks mainly follow sequential logic, where LLMs understand and answer each giv... |
Poster | Adjoint Schrödinger Bridge Sampler | https://neurips.cc//virtual/2025/poster/115786 | Guan-Horng Liu, Jaemoo Choi, Yongxin Chen, Benjamin Miller, Ricky T. Q. Chen | Computational methods for learning to sample from the Boltzmann distribution—where the target distribution is known only up to an unnormalized energy function—have advanced significantly recently. Due to the lack of explicit target samples, however, prior diffusion-based methods, known as _diffusion samplers_, often re... |
Poster | Adjusted Count Quantification Learning on Graphs | https://neurips.cc//virtual/2025/poster/119333 | Clemens Damke, Eyke Hüllermeier | *Quantification learning* is the task of predicting the label distribution of a set of instances.We study this problem in the context of graph-structured data, where the instances are vertices.Previously, this problem has only been addressed via node clustering methods.In this paper, we extend the popular *Adjusted Cla... |
Poster | Adjusting Initial Noise to Mitigate Memorization in Text-to-Image Diffusion Models | https://neurips.cc//virtual/2025/poster/119951 | Hyeonggeun Han, Sehwan Kim, Hyungjun Joo, Sangwoo Hong, Jungwoo Lee | Despite their impressive generative capabilities, text-to-image diffusion models often memorize and replicate training data, prompting serious concerns over privacy and copyright. Recent work has attributed this memorization to an attraction basin—a region where applying classifier-free guidance (CFG) steers the denois... |
Poster | ADMN: A Layer-Wise Adaptive Multimodal Network for Dynamic Input Noise and Compute Resources | https://neurips.cc//virtual/2025/poster/115905 | Jason Wu, Yuyang Yuan, Kang Yang, Lance Kaplan, Mani Srivastava | Multimodal deep learning systems are deployed in dynamic scenarios due to the robustness afforded by multiple sensing modalities. Nevertheless, they struggle with varying compute resource availability (due to multi-tenancy, device heterogeneity, etc.) and fluctuating quality of inputs (from sensor feed corruption, envi... |
Poster | AdmTree: Compressing Lengthy Context with Adaptive Semantic Trees | https://neurips.cc//virtual/2025/poster/116317 | Yangning Li, Shaoshen Chen, Yinghui Li, Yankai Chen, Hai-Tao Zheng, Hui Wang, Wenhao Jiang, Philip S Yu | The quadratic complexity of self-attention limits Large Language Models (LLMs) in processing long contexts, a capability vital for many advanced applications. Context compression aims to mitigate this computational barrier while preserving essential semantic information. However, existing methods often falter: explicit... |
Poster | ADPretrain: Advancing Unsupervised Anomaly Detection via Anomaly Representation Pretraining | https://neurips.cc//virtual/2025/poster/116220 | Xincheng Yao, Yan Luo, Zefeng Qian, Chongyang Zhang | The current mainstream and state-of-the-art anomaly detection (AD) methods are substantially established on pretrained feature networks yielded by ImageNet pretraining. However, regardless of supervised or self-supervised pretraining, the pretraining process on ImageNet does not match the goal of anomaly detection (i.e... |
Poster | A Driving-Style-Adaptive Framework for Vehicle Trajectory Prediction | https://neurips.cc//virtual/2025/poster/120322 | Di Wen, Yu Wang, Zhigang Wu, Zhaocheng He, Zhe Wu, Zheng Qingfang | Vehicle trajectory prediction serves as a critical enabler for autonomous navigation and intelligent transportation systems. While existing approaches predominantly focus on temporal pattern extraction and vehicle-environment interaction modeling, they exhibit a fundamental limitation in addressing trajectory heterogen... |
Poster | Advanced Sign Language Video Generation with Compressed and Quantized Multi-Condition Tokenization | https://neurips.cc//virtual/2025/poster/119805 | Cong Wang, Zexuan Deng, Zhiwei Jiang, Fei Shen, Yafeng Yin, Shiwei Gan, Zifeng Cheng, Shiping Ge, Qing Gu | Sign Language Video Generation (SLVG) seeks to generate identity-preserving sign language videos from spoken language texts. Existing methods primarily rely on the single coarse condition (e.g., skeleton sequences) as the intermediary to bridge the translation model and the video generation model, which limits both the... |
Poster | Advancing Compositional Awareness in CLIP with Efficient Fine-Tuning | https://neurips.cc//virtual/2025/poster/118214 | Amit Peleg, Naman Deep Singh, Matthias Hein | Vision-language models like CLIP have demonstrated remarkable zero-shot capabilities in classification and retrieval.However, these models often struggle with compositional reasoning – the ability to understand the relationships between concepts. A recent benchmark, SugarCrepe++, reveals that previous works on improvi... |
Poster | Advancing Expert Specialization for Better MoE | https://neurips.cc//virtual/2025/poster/116506 | Hongcan Guo, Haolang Lu, Guoshun Nan, Bolun Chu, Jialin Zhuang, Yuan Yang, Wenhao Che, Xinye Cao, Sicong Leng, Qimei Cui, Xudong Jiang | Mixture-of-Experts (MoE) models enable efficient scaling of large language models (LLMs) by activating only a subset of experts per input. However, we observe that the commonly used auxiliary load balancing loss often leads to expert overlap and overly uniform routing, which hinders expert specialization and degrades o... |
Poster | Advancing Interpretability of CLIP Representations with Concept Surrogate Model | https://neurips.cc//virtual/2025/poster/118587 | Nhat Hoang-Xuan, Xiyuan Wei, Wanli Xing, Tianbao Yang, My T. Thai | Contrastive Language-Image Pre-training (CLIP) generates versatile multimodal embeddings for diverse applications, yet the specific information captured within these representations is not fully understood. Current explainability techniques often target specific tasks, overlooking the rich, general semantics inherent i... |
Poster | Advancing Machine-Generated Text Detection from an Easy to Hard Supervision Perspective | https://neurips.cc//virtual/2025/poster/119371 | Chenwang Wu, Yiu-ming Cheung, Bo Han, Defu Lian | Existing machine-generated text (MGT) detection methods implicitly assume labels as the "golden standard". However, we uncover boundary ambiguity in MGT detection, implying that traditional training paradigms are inexact. The limitations of human cognition and the super intelligence of detectors make inexact learning w... |
Poster | Advancing Wasserstein Convergence Analysis of Score-Based Models: Insights from Discretization and Second-Order Acceleration | https://neurips.cc//virtual/2025/poster/117151 | Yifeng Yu, Lu Yu | Score-based diffusion models have emerged as powerful tools in generative modeling, yet their theoretical foundations remain underexplored. In this work, we focus on the Wasserstein convergence analysis of score-based diffusion models. Specifically, we investigate the impact of various discretization schemes, including... |
Poster | AdvEDM: Fine-grained Adversarial Attack against VLM-based Embodied Decision-Making Systems | https://neurips.cc//virtual/2025/poster/116436 | Yichen Wang, Hangtao Zhang, Hewen Pan, Ziqi Zhou, Xianlong Wang, Peijin Guo, Lulu Xue, Shengshan Hu, Minghui Li, Leo Yu Zhang | Vision-Language Models (VLMs), with their strong reasoning and planning capabilities, are widely used in embodied decision-making (EDM) tasks such as autonomous driving and robotic manipulation. Recent research has increasingly explored adversarial attacks on VLMs to reveal their vulnerabilities. However, these attacks... |
Poster | Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment | https://neurips.cc//virtual/2025/poster/116337 | Xiaojun Jia, Sensen Gao, Simeng Qin, Tianyu Pang, Chao Du, Yihao Huang, Xinfeng Li, Yiming Li, Yang Liu, Bo Li | Multimodal large language models (MLLMs) remain vulnerable to transferable adversarial examples. While existing methods typically achieve targeted attacks by aligning global features—such as CLIP’s [CLS] token—between adversarial and target samples, they often overlook the rich local information encoded in patch tokens... |
Poster | Adversarial Diffusion for Robust Reinforcement Learning | https://neurips.cc//virtual/2025/poster/119337 | Daniele Foffano, Alessio Russo, Alexandre Proutiere | Robustness to modeling errors and uncertainties remains a central challenge in reinforcement learning (RL). In this work, we address this challenge by leveraging diffusion models to train robust RL policies. Diffusion models have recently gained popularity in model-based RL due to their ability to generate full traject... |
Poster | Adversarial generalization of unfolding (model-based) networks | https://neurips.cc//virtual/2025/poster/117610 | Vicky Kouni | Unfolding networks are interpretable networks emerging from iterative algorithms, incorporate prior knowledge of data structure, and are designed to solve inverse problems like compressed sensing, which deals with recovering data from noisy, missing observations. Compressed sensing finds applications in critical domain... |
Poster | Adversarial Graph Fusion for Incomplete Multi-view Semi-supervised Learning with Tensorial Imputation | https://neurips.cc//virtual/2025/poster/115365 | Zhangqi Jiang, Tingjin Luo, Xu Yang, Xinyan Liang | View missing remains a significant challenge in graph-based multi-view semi-supervised learning, hindering their real-world applications. To address this issue, traditional methods introduce a missing indicator matrix and focus on mining partial structure among existing samples in each view for label propagation (LP). ... |
Poster | Adversarial Locomotion and Motion Imitation for Humanoid Policy Learning | https://neurips.cc//virtual/2025/poster/116642 | Jiyuan Shi, Xinzhe Liu, Dewei Wang, ouyang lu, Sören Schwertfeger, Chi Zhang, Fuchun Sun, Chenjia Bai, Xuelong Li | Humans exhibit diverse and expressive whole-body movements. However, attaining human-like whole-body coordination in humanoid robots remains challenging, as conventional approaches that mimic whole-body motions often neglect the distinct roles of upper and lower body. This oversight leads to computationally intensive p... |
Poster | Adversarial Paraphrasing: A Universal Attack for Humanizing AI-Generated Text | https://neurips.cc//virtual/2025/poster/116833 | Yize Cheng, Vinu Sankar Sadasivan, Mehrdad Saberi, Shoumik Saha, Soheil Feizi | The increasing capabilities of Large Language Models (LLMs) have raised concerns about their misuse in AI-generated plagiarism and social engineering. While various AI-generated text detectors have been proposed to mitigate these risks, many remain vulnerable to simple evasion techniques such as paraphrasing. However, ... |
Poster | Adversarial Robustness of Nonparametric Regression | https://neurips.cc//virtual/2025/poster/120103 | Parsa Moradi, Hanzaleh Nodehi, Mohammad Maddah-Ali | In this paper, we investigate the adversarial robustness of regression, a fundamental problem in machine learning, under the setting where an adversary can arbitrarily corrupt a subset of the input data. While the robustness of parametric regression has been extensively studied, its nonparametric counterpart remains la... |
Poster | Adversarial Self-Supervised Representation Learning with Theoretical Guarantees | https://neurips.cc//virtual/2025/poster/117299 | Chenguang Duan, Yuling Jiao, Huazhen Lin, Wensen Ma, Jerry Yang | Learning transferable data representations from abundant unlabeled data remains a critical challenge in machine learning. While numerous self-supervised learning methods have emerged to address this challenge, a significant class of these approaches aligns the covariance or correlation matrix with the identity matrix. ... |
Poster | Adversarial Training for Generalized and Invariant Single-Neuron In-Vivo Activity Representation | https://neurips.cc//virtual/2025/poster/117047 | Wei Wu, Yuxing Lu, Zhengrui Guo, Chi Zhang, Can Liao, Yifan Bu, Fangxu Zhou, Jinzhuo Wang | In computational neuroscience, models representing single-neuron in-vivo activity have become essential for understanding the functional identities of individual neurons. These models, such as implicit representation methods based on Transformer architectures, contrastive learning frameworks, and variational autoencode... |
Poster | Adversary Aware Optimization for Robust Defense | https://neurips.cc//virtual/2025/poster/117691 | Daniel Wesego, Pedram Rooshenas | Deep neural networks remain highly susceptible to adversarial attacks, where small, subtle perturbations to input images may induce misclassification. We propose a novel optimization-based purification framework that directly removes these perturbations by maximizing a Bayesian-inspired objective combining a pretrained... |
Poster | AdvPrefix: An Objective for Nuanced LLM Jailbreaks | https://neurips.cc//virtual/2025/poster/118071 | Sicheng Zhu, Brandon Amos, Yuandong Tian, Chuan Guo, Ivan Evtimov | Many jailbreak attacks on large language models (LLMs) rely on a common objective: making the model respond with the prefix ``Sure, here is (harmful request)''. While straightforward, this objective has two limitations: limited control over model behaviors, yielding incomplete or unrealistic jailbroken responses, and a... |
Poster | A Dynamic Learning Strategy for Dempster-Shafer Theory with Applications in Classification and Enhancement | https://neurips.cc//virtual/2025/poster/119965 | Linlin Fan, Xingyu Liu, Mingliang Zhou, Xuekai Wei, Weizhi Xian, Jielu Yan, Weijia Jia | Effective modelling of uncertain information is crucial for quantifying uncertainty. Dempster–Shafer evidence (DSE) theory is a widely recognized approach for handling uncertain information. However, current methods often neglect the inherent a priori information within data during modelling, and imbalanced data lead t... |
Poster | AegisGuard: RL-Guided Adapter Tuning for TEE-Based Efficient & Secure On-Device Inference | https://neurips.cc//virtual/2025/poster/116589 | CHE WANG, Ziqi Zhang, Yinggui Wang, Tiantong Wang, Yurong Hao, Jianbo Gao, Tao Wei, Yang Cao, Zhong Chen, Wei Lim | On-device large models (LMs) reduce cloud dependency but expose proprietary model weights to the end-user, making them vulnerable to white-box model stealing (MS) attacks. A common defense is TEE-Shielded DNN Partition (TSDP), which places all trainable LoRA adapters (fine tuned on private data) inside a trusted execut... |
Poster | Aeolus: A Multi-structural Flight Delay Dataset | https://neurips.cc//virtual/2025/poster/121793 | Lin Xu, Xinyun Yuan, Yuxuan Liang, Suwan Yin, Yuankai Wu | We introduce Aeolus, a large-scale Multi-modal Flight Delay Dataset designed to advance research on flight delay prediction and support the development of foundation models for tabular data. Existing datasets in this domain are typically limited to flat tabular structures and fail to capture the spatiotemporal dynamics... |
Poster | A Fair Federated Learning Method for Handling Client Participation Probability Inconsistencies in Heterogeneous Environments | https://neurips.cc//virtual/2025/poster/115969 | Siyuan Wu, Yongzhe Jia, Haolong Xiang, Xiaolong Xu, Xuyun Zhang, Lianyong Qi, Wanchun Dou | Federated learning (FL) is a distributed machine learning paradigm that enables multiple clients to collaboratively train a shared model without exposing their raw data. However, existing FL research has primarily focused on optimizing learning performance based on the assumption of uniform client participation, with f... |
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