Posters

realSEUDO: Real-time calcium imaging analysis now possible at speeds exceeding 30 Hz, enabling sophisticated closed-loop neuroscience experiments.

New surrogate loss functions for learning-to-defer achieve Bayes-consistency, realizable H-consistency, and H-consistency bounds simultaneously, resolving open questions and improving L2D performance.

RealCompo: A novel training-free framework dynamically balances realism and compositionality in text-to-image generation, achieving state-of-the-art results.

StreamDSGN: a real-time stereo 3D object detection framework significantly boosts streaming perception accuracy by leveraging historical information, a feature-flow fusion method, and a motion consist…

II-COS: a novel online sample selection method effectively controls individual and interactive constraints in real-time via predictive inference, improving efficiency and addressing various practical …

Recurrent Trace Units (RTUs) significantly enhance real-time recurrent learning in reinforcement learning, outperforming other methods with less computation.

ECP-ViT: Real-time Vision Transformer on Mobile Devices via Core-Periphery Attention and Smart Data Layout.

RCDN: Robust, camera-insensitive collaborative perception via dynamic 3D neural modeling, overcoming camera failures for high-performance autonomous systems.

RAW: A novel watermark framework ensures the authenticity of AI-generated images by embedding learnable watermarks directly into the image data, providing provable guarantees even under adversarial at…

RAVL: a novel approach that accurately discovers and effectively mitigates spurious correlations in fine-tuned vision-language models, improving zero-shot classification accuracy.

RashomonGB tackles predictive multiplicity in gradient boosting by introducing a novel inference technique to efficiently identify and mitigate conflicting model predictions, improving model selection…

RankUp: Revolutionizing semi-supervised regression by cleverly adapting classification techniques for superior performance!

RankRAG: One LLM, dual-purpose instruction-tuning for superior RAG!

Randomized truthful auctions outperform deterministic ones when bidders employ learning algorithms, maximizing revenue in repeated interactions.

Randomized strategies improve truthful learning-augmented mechanisms for strategic facility location, achieving better approximations than deterministic methods.

Randomized sparse matrix compression boosts large-scale cancer radiotherapy optimization, improving treatment quality without sacrificing speed.

Provably efficient randomized exploration in cooperative MARL is achieved via a novel unified algorithm framework, CoopTS, using Thompson Sampling with PHE and LMC exploration strategies.

First provably efficient randomized RL algorithms using multinomial logistic function approximation are introduced, achieving superior performance and constant-time computational cost.

This paper presents randomized algorithms with PAC bounds for solving inverse reinforcement learning problems in continuous state and action spaces, offering robust theoretical guarantees and practica…

Random pixel projections outperform complex online continual learning methods for image classification, challenging assumptions about representation learning.