SCAN: Self-Denoising Monte Carlo Annotation reduces noise in synthetic data, boosting PRM F1 from 19.9 to 59.1 with only 6% inference cost.

Yuyang Ding, Xinyu Shi, Juntao Li et al.

DiffusionNFT employs flow matching on the forward process, using contrastive positive-negative samples, achieving 25× efficiency without likelihood estimation.

Kaiwen Zheng, Huayu Chen, Haotian Ye et al.

Proposes a Graph-Latent Neural ODE framework combining GNNs, NODEs, and neural processes for cardiac trajectory reconstruction and classification.

Jaume Banus, Augustin C. Ogier, Roger Hullin et al.

Proposes dual spectral encoding and geometric functional maps for unsupervised graph alignment, improving robustness and discriminability.

Maysam Behmanesh, Erkan Turan, Maks Ovsjanikov

Proposes Wasserstein-based performance prediction and data selection framework for federated models, ensuring privacy and efficiency.

Wenqian Li, Youjia Yang, Ruoxi Jia et al.

Derived fundamental bounds linking confidence and efficiency in transductive conformal prediction, showing exponential growth of prediction sets governed by conditional entropy.

Arash Behboodi, Alvaro H. C. Correia, Fabio Valerio Massoli et al.

This study reveals data uncertainty as a key factor in tabular deep learning, proposing uncertainty-aware feature embeddings for robustness.

Nikolay Kartashev, Ivan Rubachev, Artem Babenko

AEnt method improves LLM-RL performance via adaptive entropy control, excelling in math reasoning tasks.

Han Shen

SimpleTIR algorithm boosts AIME24 score from 22.1 to 50.5 by filtering void turns.

Zhenghai Xue, Longtao Zheng, Qian Liu et al.

RDIT combines point estimation and residual diffusion, optimizing CRPS, outperforming baselines on 8 datasets with faster inference.

Chih-Yu Lai, Yu-Chien Ning, Duane S. Boning

Proposed SoTPDEG framework for multi-graph data, preserving high-frequency info with spectral decomposition and stability guarantees.

Aref Einizade, Fragkiskos D. Malliaros, Jhony H. Giraldo

Attention-based encoder-decoder deep RL (AEDM) optimizes drone routes, achieving 20-71% better solutions in seconds for post-disaster road assessment.

Huatian Gong, Jiuh-Biing Sheu, Zheng Wang et al.

Unified geometric framework for nonlinear RL, integrating reward, safety, and diversity via occupancy measure optimization.

Nikola Milosevic, Nico Scherf

Proposed BiLSTM-AM-VMD framework integrates multimodal data for early liver cancer diagnosis, achieving AUC of 0.963, outperforming baseline models.

Cheng Cheng, Zeping Chen, Xavier Wang

Multi-modal ML framework combining MRI and biomarkers achieves 78.2% C-index for early brain tumor recurrence prediction.

Cheng Cheng, Zeping Chen, Rui Xie et al.

SVS-SNG analyzes single-mini-batch natural gradient through sketch-and-project geometry, yielding convergence rates governed by α/γ.

Gil Goldshlager, Jiang Hu, Lin Lin

Proposes a novel framework combining probabilistic crowd modeling and active learning to enhance preference-based reinforcement learning efficiency.

Jonathan Erskine, Taku Yamagata, Raúl Santos-Rodríguez

Proposes FRISBI, an inductive SBI framework combining mini-batch OT and conditional normalizing flows, addressing model misspecification.

Ortal Senouf, Antoine Wehenkel, Cédric Vincent-Cuaz et al.

MAVIS achieves multi-objective alignment via inference-time value-guided selection, enhancing Pareto front performance.

Jeremy Carleton, Debajoy Mukherjee, Srinivas Shakkottai et al.

Trust-region constrained measure transport improves high-dimensional stochastic control, reducing control error to 0.002 and transfer path RMSD to 1.2 Å, outperforming baselines.

Denis Blessing, Julius Berner, Lorenz Richter et al.