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ORCIDTransactions on Machine Learning Research, Volume 2026
Volume 2026, 2026
- Hongseok Oh, Wonseok Hwang:
Do Vision Encoders Truly Explain Object Hallucination?: Mitigating Object Hallucination via Simple Fine-Grained CLIPScore. - Sonia Cromp, Satya Sai Srinath Namburi GNVV, Mohammed Alkhudhayri, Catherine Cao, Samuel Guo, Nicholas Carl Roberts, Frederic Sala:
Tabby: A Language Model Architecture for Tabular and Structured Data Synthesis. - Abhishek A, Manohar Kaul, Mohit Meena, Mahesh Chandran:
Proper Orthogonal Decomposition for Scalable Training of Graph Neural Networks. - Ziting Wen, Wenle Dong, Zili Zhang, Yiheng Qiang, Kemi Ding, Xiaoqiang Ren:
Noise-Aware Adaptation of Pre-trained Foundation Models for Single-photon Image Classification. - Sisuo Lyu, Hong Liu, Jie Li, Yan Teng, Yingchun Wang:
Improving Foundation Model Group Robustness with Auxiliary Sentence Embeddings. - Neeraj Anand, Samyak Jha, Udbhav Bamba, Rahul Rahaman:
CRoPS: A Training-Free Hallucination Mitigation Framework for Vision-Language Models. - Feng Zhu, Aritra Mitra, Robert W. Heath Jr.:
Achieving Tighter Finite-Time Rates for Heterogeneous Federated Stochastic Approximation under Markovian Sampling. - Aditi Palit, Kalidas Yeturu:
Federated Multimodal Fusion for Action Recognition Leveraging Vision-Language Embeddings and Spatio- Temporal CNNs. - Yahan Yang, Soham Dan, Dan Roth, Insup Lee:
On Calibration of Multilingual Question Answering LLMs. - Umesh-Vangapally, Wenhan Wu, Chen Chen, Zhishuai Guo:
Communication-Efficient Federated AUC Maximization with Cyclic Client Participation. - Edgar W. Jatho III, Armon Barton, Matthew Wright, Patrick McClure:
Overcoming Open-Set Approaches to Adversarial Defense. - Hesam Hosseini, Ghazal Hosseini Mighan, Amirabbas Afzali, Sajjad Amini, Amir Houmansadr:
ULTra: Unveiling Latent Token Interpretability in Transformer-Based Understanding and Segmentation. - Yunusa Haruna, Adamu Lawan, Abdulganiyu Abdu Yusuf:
iiANET: Inception Inspired Attention Hybrid Network for efficient Long-Range Dependency. - Yechao Xu, Zhengxing Sun, Qian Li, Jiao Qu:
PredLDM: Spatiotemporal Sequence Prediction with Latent Diffusion Models. - Tengyuan Liang, Kulunu Dharmakeerthi, Takuya Koriyama:
Denoising Diffusions with Optimal Transport: Localization, Curvature, and Multi-Scale Complexity. - Adam Izdebski, Jan Olszewski, Pankhil Gawade, Krzysztof Koras, Serra Korkmaz, Valentin Rauscher, Jakub M. Tomczak, Ewa Szczurek:
Synergistic Benefits of Joint Molecule Generation and Property Prediction. - Hai-Vy Nguyen, Fabrice Gamboa, Sixin Zhang, Reda Chhaibi, Serge Gratton, Thierry Giaccone:
Training More Robust Classification Model via Discriminative Loss and Gaussian Noise Injection. - Yongtao Wu, Luca Viano, Kimon Antonakopoulos, Yihang Chen, Zhenyu Zhu, Quanquan Gu, Volkan Cevher:
Multi-Step Alignment as Markov Games: An Optimistic Online Mirror Descent Approach with Convergence Guarantees. - Patrik Joslin Kenfack, Samira Ebrahimi Kahou, Ulrich Aïvodji:
Towards Fair In-Context Learning with Tabular Foundation Models. - Minsu Kim, Jiayao Gu, Ye Yuan, Taeyoung Yun, Zixuan Liu, Yoshua Bengio, Can Chen:
Offline Model-Based Optimization: Comprehensive Review. - Sakshi Choudhary, Sai Aparna Aketi, Kaushik Roy:
Achieving Global Flatness in Decentralized Learning with Heterogeneous Data. - Madhavi Kondapally, K. Naveen Kumar, C. Krishna Mohan:
Eyes on the Road, Words in the Changing Skies: Vision-Language Assistance for Autonomous Driving in Transitional Weather. - Cuong Tran Van, Trong-Thang Pham, Ngoc Son Nguyen, Duy Minh Ho Nguyen, Ngan Le:
DuFal: Dual-Frequency-Aware Learning for High-Fidelity Extremely Sparse-view CBCT Reconstruction. - Jialin Yang, Dongfu Jiang, Tony He, Sherman Siu, Yuxuan Zhang, Disen Liao, Zhuofeng Li, Huaye Zeng, Yiming Jia, Haozhe Wang, Benjamin Schneider, Chi Ruan, Wentao Ma, Zhiheng Lyu, Yifei Wang, Yi Lu, Quy Duc Do, Ziyan Jiang, Ping Nie, Wenhu Chen:
StructEval: Benchmarking LLMs' Capabilities to Generate Structural Outputs. - Shangqian Gao, Ting Hua, Reza Shirkavand, Chi-Heng Lin, Zheng Tang, Zhengao Li, Longge Yuan, Fangyi Li, Zeyu Zhang, Alireza Ganjdanesh, Qian Lou, Jie Xu, Yen-Chang Hsu:
ToMoE: Converting Dense Large Language Models to Mixture-of-Experts through Dynamic Structural Pruning. - Rajdeep Haldar, Yue Xing, Qifan Song, Guang Lin:
Adversarial Vulnerability from On-Manifold Inseparability and Poor Off-Manifold Convergence. - Jakob Nyberg, Pontus Johnson:
Vejde: A Framework for Inductive Deep Reinforcement Learning Based on Factor Graph Color Refinement. - Karen Sargsyan:
The Geometry of Algorithmic Stability: A Hodge Theoretic View on Structural vs. Statistical Instability. - Jacob Si, Zijing Ou, Mike Qu, Zhengrui Xiang, Yingzhen Li:
TabRep: Training Tabular Diffusion Models with a Simple and Effective Continuous Representation. - Kazuki Irie, Samuel J. Gershman:
Fast weight programming and linear transformers: from machine learning to neurobiology. - Shicong Cen, Jincheng Mei, Hanjun Dai, Dale Schuurmans, Yuejie Chi, Bo Dai:
Beyond Expectations: Learning with Stochastic Dominance Made Practical. - Prashant Govindarajan, Davide Baldelli, Jay Pathak, Quentin Fournier, Sarath Chandar:
CADmium: Fine-Tuning Code Language Models for Text- Driven Sequential CAD Design. - Mathis Antonetti, Henrique Donâncio, Florence Forbes:
An analysis of distributional reinforcement learning with Gaussian mixtures. - Ramón Calvo González, Daniele Paliotta, Matteo Pagliardini, Martin Jaggi, François Fleuret:
Leveraging the True Depth of LLMs. - Tao Yu, Zhengbo Zhang, Zhiheng Lyu, Junhao Gong, Hongzhu Yi, Xinming Wang, Yuxuan Zhou, Jiabing Yang, Ping Nie, Yan Huang, Wenhu Chen:
BrowserAgent: Building Web Agents with Human-Inspired Web Browsing Actions. - Junho Lee, Kwanseok Kim, Joonseok Lee:
Is There a Better Source Distribution than Gaussian? Exploring Source Distributions for Image Flow Matching. - Xiaoyang Hou, Tian Zhu, Milong Ren, Dongbo Bu, Xin Gao, Chunming Zhang, Shiwei Sun:
GGFlow: A Graph Flow Matching Method with Efficient Optimal Transport. - Jichan Chung, Irene Y. Chen:
Enhancing Semi-supervised Learning with Zero-shot Pseudolabels. - Alireza Pourali, Arian Boukani, Hamzeh Khazaei:
CAPE: Generalized Convergence Prediction Across Architectures Without Full Training. - Joanna Hong, Sanjeel Parekh, Honglie Chen, Jacob Donley, Ke Tan, Buye Xu, Anurag Kumar:
Efficient Audiovisual Speech Processing via MUTUD: Multimodal Training and Unimodal Deployment. - Anastasios Manganaris, Vittorio Giammarino, Ahmed H. Qureshi, Suresh Jagannathan:
Formal Methods in Robot Policy Learning and Verification: A Survey on Current Techniques and Future Directions. - Philipp Dahlinger, Niklas Freymuth, Tai Hoang, Tobias Würth, Michael Volpp, Luise Kärger, Gerhard Neumann:
Context-aware Learned Mesh-based Simulation via Trajectory-Level Meta-Learning. - Manuel Brenner, Georgia Koppe:
Uncovering the Computational Roles of Nonlinearity in Sequence Modeling Using Almost-Linear RNNs. - Guanghao Li, Li Shen, Yan Sun, Yue Hu, Han Hu, Dacheng Tao:
Subspace based Federated Unlearning. - Viorica Patraucean, Xu Owen He, Joseph Heyward, Chuhan Zhang, Mehdi S. M. Sajjadi, George-Cristian Muraru, Artem Zholus, Mahdi Karami, Ross Goroshin, Yutian Chen, Simon Osindero, João Carreira, Razvan Pascanu:
TRecViT: A Recurrent Video Transformer. - Amir Eskandari, Aman Anand, Elyas Rashno, Farhana Zulkernine:
InfGraND: An Influence-Guided GNN-to-MLP Knowledge Distillation. - Abhilash Neog, Arka Daw, Sepideh Fatemi, Medha Sawhney, Aanish Pradhan, Mary E. Lofton, Bennett J. McAfee, Adrienne Breef-Pilz, Heather L. Wander, Dexter W. Howard, Cayelan C. Carey, Paul C. Hanson, Anuj Karpatne:
Investigating a Model-Agnostic and Imputation-Free Approach for Irregularly-Sampled Multivariate Time-Series Modeling. - Riyasat Ohib, Bishal Thapaliya, Gintare Karolina Dziugaite, Jingyu Liu, Vince D. Calhoun, Sergey M. Plis:
SSFL: Discovering Sparse Unified Subnetworks at Initialization for Efficient Federated Learning. - Ge Zhang, Mohammad Ali Alomrani, Hongjian Gu, Jiaming Zhou, Yaochen Hu, Bin Wang, Qun Liu, Mark Coates, Yingxue Zhang, Jianye Hao:
Extracting and Following Paths for Robust Relational Reasoning with Large Language Models. - Lorenzo Brigato, Rafael Morand, Knut J. Strømmen, Maria Panagiotou, Markus Schmidt, Stavroula G. Mougiakakou:
There are no Champions in Supervised Long-Term Time Series Forecasting. - Mehrdad Pournaderi, Yu Xiang:
Training-Conditional Coverage Bounds under Covariate Shift. - Sara Vera Marjanovic, Arkil Patel, Vaibhav Adlakha, Milad Aghajohari, Parishad BehnamGhader, Mehar Bhatia, Aditi Khandelwal, Austin Kraft, Benno Krojer, Xing Han Lù, Nicholas Meade, Dongchan Shin, Amirhossein Kazemnejad, Gaurav Kamath, Marius Mosbach, Karolina Stanczak, Siva Reddy:
DeepSeek-R1 Thoughtology: Let's think about LLM reasoning. - Ryuichi Kiryo, Futoshi Futami, Masashi Sugiyama:
Estimating Expected Calibration Error for Positive-Unlabeled Learning. - Guanquan Wang, Takuya Hiraoka, Yoshimasa Tsuruoka:
Consistency Trajectory Planning: High-Quality and Efficient Trajectory Optimization for Offline Model-Based Reinforcement Learning. - Bo Zhao, Robin Walters, Rose Yu:
Symmetry in Neural Network Parameter Spaces. - Haoyuan Sun, Bo Xia, Yifu Luo, Tiantian Zhang, Xueqian Wang:
Calibration Enhanced Decision Maker: Towards Trustworthy Sequential Decision-Making with Large Sequence Models. - Yuyang Liu, Meng Cao, Xinyuan Shi, Xiaodan Liang:
COLT: Enhancing Video Large Language Models with Continual Tool Usage. - Hangwei Zhang, Zhimu Huang, Yan Wang:
AC-PKAN: Attention-Enhanced and Chebyshev Polynomial-Based Physics-Informed Kolmogorov-Arnold Networks. - Hongyang R. Zhang, Zhenshuo Zhang, Huy Nguyen, Guanghui Lan:
One-Sided Matrix Completion from Ultra-Sparse Samples. - Kangyu Zheng, Kai Zhang, Jiale Tan, Xuehan Chen, Yingzhou Lu, Zaixi Zhang, Lichao Sun, Marinka Zitnik, Tianfan Fu, Zhiding Liang:
Beyond Affinity: A Benchmark of 1D, 2D, and 3D Methods Reveals Critical Trade-offs in Structure-Based Drug Design. - Lorenzo Nespoli, Anubhab Biswas, Roberto Rocchetta, Vasco Medici:
Nonlinear reconciliation: Error reduction theorems. - Ce Zhang, Yan-Bo Lin, Ziyang Wang, Mohit Bansal, Gedas Bertasius:
SiLVR: A Simple Language-based Video Reasoning Framework. - Abhinav Raghuvanshi, Mayank Baranwal, Debasish Chatterjee:
On a Gradient Approach to Chebyshev Center Problems with Applications to Function Learning. - Brian Bernhard Moser, Shalini Sarode, Federico Raue, Stanislav Frolov, Krzysztof Adamkiewicz, Arundhati S. Shanbhag, Joachim Folz, Tobias Christian Nauen, Andreas Dengel:
PRISM: Diversifying Dataset Distillation by Decoupling Architectural Priors. - Erin Feiglin, Nir Hutnik, Raz Lapid:
BenchOverflow: Measuring Overflow in Large Language Models via Plain-Text Prompts. - Aman Anand, Amir Eskandari, Elyas Rashno, Farhana Zulkernine:
ASMa: Asymmetric Spatio-temporal Masking for Skeleton Action Representation Learning. - Kevin Bleakley, Martin Royer, Benjamin Auder:
Supervised score aggregation for active anomaly detection. - Robin Hesse, Dogukan Bagci, Bernt Schiele, Simone Schaub-Meyer, Stefan Roth:
Beyond Accuracy: What Matters in Designing Well-Behaved Image Classification Models? - Peiyang Song, Pengrui Han, Noah Goodman:
Large Language Model Reasoning Failures. - Athanasios Glentis, Ioannis C. Tsaknakis, Jiangweizhi Peng, Xun Xian, Yihua Zhang, Gaowen Liu, Charles Fleming, Mingyi Hong:
RT2I-Bench: Evaluating Robustness of Text-to-Image Systems Against Adversarial Attacks. - Guibin Zhang, Hejia Geng, Xiaohang Yu, Zhenfei Yin, Zaibin Zhang, Zelin Tan, Heng Zhou, Zhong-Zhi Li, Xiangyuan Xue, Yijiang Li, Yifan Zhou, Yang Chen, Chen Zhang, Yutao Fan, Zihu Wang, Songtao Huang, Francisco Piedrahita Velez, Yue Liao, Hongru Wang, Mengyue Yang, Heng Ji, Jun Wang, Shuicheng Yan, Philip Torr, Lei Bai:
The Landscape of Agentic Reinforcement Learning for LLMs: A Survey. - Olympio Hacquard:
Hypergraph clustering using Ricci curvature: an edge transport perspective. - Pravin Nair:
Softmax is $1/2$-Lipschitz: A tight bound across all $\ell_p$ norms. - Zhankun Luo, Abolfazl Hashemi:
Characterizing Evolution in Expectation-Maximization Estimates for Overspecified Mixed Linear Regression. - Qihao Liu, Ju He, Qihang Yu, Liang-Chieh Chen, Alan L. Yuille:
ReVision: Refining Video Diffusion with Explicit 3D Motion Modeling. - Yuhao Du, Zhuo Li, Pengyu Cheng, Zhihong Chen, Yuejiao Xie, Xiang Wan, Anningzhe Gao:
RLHF in an SFT Way: From Optimal Solution to Reward-Weighted Alignment. - Yasir Ghunaim, Hasan Abed Al Kader Hammoud, Bernard Ghanem:
On the Importance of Pretraining Data Alignment for Atomic Property Prediction. - Francisco Silva, Hélder P. Oliveira, Tânia Pereira:
Learning object representations through amortized inference over probabilistic programs. - Jierun Chen, Tiezheng YU, Haoli Bai, Lewei Yao, Jiannan Wu, Kaican Li, Fei Mi, Chaofan Tao, Lei Zhu, Manyi Zhang, Xiao-Hui Li, Lu Hou, Lifeng Shang, Qun Liu:
The Synergy Dilemma of Long-CoT SFT and RL: Investigating Post-Training Techniques for Reasoning VLMs. - Yadi Cao, Yuxuan Liu, Liu Yang, Rose Yu, Hayden Schaeffer, Stanley J. Osher:
VICON: Vision In-Context Operator Networks for Multi-Physics Fluid Dynamics Prediction. - Daniel D. Lee, Arie Matsliah, Lawrence K. Saul:
AC$\oplus$DC search: behind the winning solution to the FlyWire graph-matching challenge. - Sally Turutov, Kira Radinsky:
Mechanism-Aware Prediction of Tissue-Specific Drug Activity via Multi-Modal Biological Graphs. - Brian Mwigo, Anirban Dasgupta:
Generalization Bound for a Shallow Transformer Trained Using Gradient Descent. - Stefania Scheurer, Philipp Reiser, Tim Brünnette, Wolfgang Nowak, Anneli Guthke, Paul-Christian Bürkner:
Uncertainty-Aware Surrogate-based Amortized Bayesian Inference for Computationally Expensive Models. - Brown Ebouky, Ajad Chhatkuli, A. Cristiano I. Malossi, Christoph Studer, Roy Assaf, Andrea Bartezzaghi:
Enhancing Semantic Segmentation with Continual Self-Supervised Pre-training. - Bianca Lamm, Janis Keuper:
mSOP-765k: A Benchmark For Multi-Modal Structured Output Predictions. - Youssef Tawfilis, Hossam Amer, Minar El-Aasser, Tallal Elshabrawy:
A Distributed Generative AI Approach for Heterogeneous Multi-Domain Environments under Data Sharing constraints. - Emani Naga Sai Venkata Sowmya, Amit Kesari, Ajin George Joseph:
Mitigating Steady-State Bias in Off-Policy TD Learning via Distributional Correction. - Nikita Dhawan, Daniel Shen, Leonardo Cotta, Chris J. Maddison:
Bayesian Sensitivity of Causal Inference Estimators under Evidence-Based Priors. - Benjamin Weinstein-Raun, Jeremy Schlatter, Jeffrey Ladish:
Incomplete Tasks Induce Shutdown Resistance in Some Frontier LLMs. - Yohann Perron, Vladyslav Sydorov, Christophe Pottier, Loïc Landrieu:
Adapting Vision Transformers to Ultra-High Resolution Semantic Segmentation with Relay Tokens. - Bharati K, Vikesh Siddhu, Krishna P. Jagannathan:
Batch Entanglement Detection in Parameterized Qubit States using Classical Bandit Algorithms. - Nour Jamoussi, Giuseppe Serra, Photios A. Stavrou, Marios Kountouris:
Cost-Free Personalization via Information-Geometric Projection in Bayesian Federated Learning. - Manit Baser, Dinil Mon Divakaran, Mohan Gurusamy:
ThinkEval: Practical Evaluation of Knowledge Leakage in LLM Editing using Thought-based Knowledge Graphs. - Noël Kury, Dmitry Kobak, Sebastian Damrich:
DREAMS: Preserving both Local and Global Structure in Dimensionality Reduction. - Anuj Kumar Sirohi, Anjali Gupta, Sandeep Kumar, Amitabha Bagchi, Sayan Ranu:
GraphGini: Fostering Individual and Group Fairness in Graph Neural Networks. - Sha Lai, Prakash Ishwar, Margrit Betke:
Budget-Optimized Crowdworker Allocation. - Minh H. Vu, Daniel Edler, Carl Wibom, Tommy Löfstedt, Beatrice Melin, Martin Rosvall:
A Unified Framework for Tabular Generative Modeling: Loss Functions, Benchmarks, and Improved Multi-objective Bayesian Optimization Approaches. - Yulong Huang, Jianxiong Tang, Chao Wang, Ziyi Wang, Jianguo Zhang, Zhichao Lu, Bojun Cheng, Luziwei Leng:
SpikingMamba: Towards Energy-Efficient Large Language Models via Knowledge Distillation from Mamba. - Pushkar Mishra, Charvi Rastogi, Stephen R. Pfohl, Alicia Parrish, Tian Huey Teh, Roma Patel, Mark Diaz, Ding Wang, Michela Paganini, Vinodkumar Prabhakaran, Lora Aroyo, Verena Rieser:
Decoding Safety Feedback from Diverse Raters: A Data-driven Lens on Responsiveness to Severity. - Khurram Yamin, Vibhhu Sharma, Edward Kennedy, Bryan Wilder:
Accounting for Missing Covariates in Heterogeneous Treatment Estimation. - Nang Hung Nguyen, Phi Le Nguyen, Truong Thao Nguyen, Trong Nghia Hoang, Masashi Sugiyama:
Causal Graph Learning via Distributional Invariance of Cause-Effect Relationship. - Yunzhe Qi, Yao Zhou, Yikun Ban, Allan Stewart, Chuanwei Ruan, Jiachuan He, Shishir Kumar Prasad, Haixun Wang, Jingrui He:
Bi-level Hierarchical Neural Contextual Bandits for Online Recommendation. - Rui Luo, Jie Bao, Suqun Cao, Chuangyin Dang, Zhixin Zhou:
Game-Theoretic Defenses for Adversarially Robust Conformal Prediction. - Keru Chen, Honghao Wei, Zhigang Deng, Sen Lin:
Towards Fast Safe Online Reinforcement Learning via Policy Finetuning. - Ce Zhang, Kaixin Ma, Tianqing Fang, Wenhao Yu, Hongming Zhang, Zhisong Zhang, Haitao Mi, Dong Yu:
VScan: Rethinking Visual Token Reduction for Efficient Large Vision-Language Models.
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