Posters

VCR-GauS: Novel view-consistent depth-normal regularizer for superior, real-time 3D surface reconstruction using Gaussian splatting.

VB-LoRA achieves extreme parameter efficiency in fine-tuning LLMs by sharing parameters globally via a vector bank, outperforming state-of-the-art PEFT methods while maintaining comparable or better p…

New uncertainty estimation method for 3D Gaussian Splatting improves scene reconstruction quality by leveraging variational multi-scale representation and efficiently removing noisy data.

CatFlow: a novel flow matching method for graph generation, offering superior computational efficiency and performance.

VDD distills complex diffusion policies into efficient Mixture of Experts (MoE) models via variational inference, enabling faster inference and improved performance in behavior learning.

Unlocking the optimal variance estimation rate in compound decision theory under bounded means, this paper reveals a surprising (log log n/log n)² rate and introduces a rate-optimal cumulant-based est…

MAST: Train ultra-sparse deep MARL agents with minimal performance loss!

Vaccine: a novel technique safeguards LLMs against harmful fine-tuning attacks by creating invariant hidden embeddings.

UV3-TeD generates high-quality 3D textures directly on object surfaces using a novel diffusion probabilistic model, eliminating UV-mapping limitations.

ImagenTime transforms time series into images, leveraging advanced diffusion models for superior generative modeling of both short and long sequences.

AI explanations can be subtly manipulated to influence human decisions, highlighting the urgent need for more robust and ethical AI explanation design.

Boosting clinical trial efficiency, this research introduces a covariate-adjusted response-adaptive randomization (CARA) design that effectively leverages surrogate outcomes to handle delayed primary …

Noise in data surprisingly simplifies machine learning models, improving their interpretability without sacrificing accuracy; this paper quantifies this effect across various hypothesis spaces.

Recommendation systems must balance user satisfaction with fair item exposure. This research provides a theoretical model and empirical validation showing that user preference diversity can significan…

Recommender systems, when influenced by both users and creators, inevitably polarize; however, prioritizing efficiency through methods like top-k truncation can surprisingly enhance diversity.

UrbanKGent: A unified LLM agent framework revolutionizes urban knowledge graph construction, achieving significantly improved accuracy and efficiency.

UQE: A novel query engine uses LLMs for efficient and accurate unstructured data analytics, surpassing existing methods.

Uncertainty-aware HPO boosts iterative learner performance by over 50%, reducing regret and exploration time via a novel UQ-guided scheme.

UPS: A novel algorithm for lightweight single-image super-resolution, decoupling feature extraction and similarity modeling for enhanced efficiency and robustness.

Boosting 3D medical image segmentation, a novel U-shaped Connection (uC) integrates 2D U-Net skip connections into 3D CNNs, improving axial-slice plane feature extraction, surpassing state-of-the-art …