Posters

This paper introduces a novel information-theoretic framework, showing how to convert offline into online estimation algorithms efficiently, impacting interactive decision-making.

This paper presents a novel two-level online control algorithm that learns to control continuous-time linear systems under adversarial disturbances, achieving sublinear regret.

This paper introduces a novel, robust online control framework for managing evolving populations, achieving near-optimal control even in complex, noisy systems.

The 1-nearest neighbor rule achieves online consistency under surprisingly broad conditions: measurable label functions and mild assumptions on instance generation in doubling metric spaces.

Researchers achieve optimal regret bounds in online composite optimization under stochastic and adversarial settings using a novel optimistic composite mirror descent algorithm and a universal strateg…

Online learning algorithms can now leverage predictions about future data to achieve significantly lower regret, smoothly transitioning between worst-case and best-case performance based on prediction…

MetaAd, a novel meta-algorithm, achieves provable competitive ratios for online budgeted matching with general bids, removing prior restrictive assumptions.

MAC: Efficiently updates large language models (LLMs) using a memory of compressed contexts for improved real-time knowledge retention and adaptation.

OneRef: Unified one-tower model surpasses existing methods in visual grounding and segmentation by leveraging a novel Mask Referring Modeling paradigm.

OneBit achieves surprisingly good performance in 1-bit quantized LLMs by using a novel 1-bit parameter representation method and an effective parameter initialization method.

OneActor: One-shot tuning for consistent subject image generation, bypassing laborious backbone tuning via semantic guidance, achieving 4x faster speed.

One-to-Normal: Anomaly personalization boosts few-shot anomaly detection accuracy by transforming query images to match normal data, enabling precise, robust comparisons and flexible integration with …

PSTUDA, a novel progressive style transfer framework, efficiently segments kidney tumors across multiple MRI sequences using unsupervised domain adaptation, achieving higher accuracy and efficiency th…

OSEDiff: One-step diffusion network for real-world image super-resolution, achieving comparable or better results than multi-step methods with significantly reduced computational cost and improved ima…

Score Implicit Matching (SIM) revolutionizes diffusion model distillation by creating high-quality, single-step generators from complex, multi-step models, achieving comparable performance and enablin…

FedSD2C, a novel one-shot federated learning framework, tackles data heterogeneity and information loss by sharing synthetic distillates directly from local data, outperforming existing methods on com…

One-layer transformers provably learn the one-nearest neighbor prediction rule, offering theoretical insights into their in-context learning capabilities.

VideoLISA: A video-based multimodal large language model enabling precise, language-instructed video object segmentation with superior performance.

One-Sample-Fits-All (OFA) framework efficiently approximates all probabilistic values simultaneously, achieving faster convergence rates than existing methods.

OneDet3D: A universal 3D object detector trained jointly on diverse indoor/outdoor datasets, achieving one-for-all performance across domains and categories.