Posters

Random Function Descent (RFD) replaces the classical convex function framework with a random function approach, providing a scalable gradient descent method with inherent scale invariance and a theore…

Random Cycle Coding (RCC) optimally compresses cluster assignments in large datasets, saving up to 70% storage in vector databases by eliminating the need for integer IDs.

RandNet-Parareal: A novel time-parallel PDE solver using Random Neural Networks achieves speed gains up to x125, dramatically improving scalability for large-scale simulations.

RAMP: A novel training framework significantly boosts DNN robustness against diverse adversarial attacks by mitigating accuracy-robustness tradeoffs and improving generalization.

Rainbow Teaming: a novel black-box approach generates diverse adversarial prompts to enhance LLM robustness and safety, achieving over 90% attack success rate across various models.

RAGRAPH, a novel retrieval-augmented graph learning framework, boosts GNN generalization by integrating external graph data, significantly outperforming state-of-the-art methods.

RadarOcc: Revolutionizing autonomous driving with robust 3D occupancy prediction using 4D imaging radar, overcoming limitations of LiDAR and camera-based approaches.

RA-PbRL introduces a provably efficient algorithm for risk-aware preference-based reinforcement learning, addressing the limitations of existing risk-neutral methods in applications demanding heighten…

QWO: a novel method dramatically speeds up permutation-based causal discovery in linear Gaussian models, enabling the analysis of larger datasets and advancing causal inference.

Quantum VAE with spherical latent variable learning enables efficient, one-shot 3D molecule generation, outperforming classic and other quantum methods.

Novel algorithms stabilize multi-server queueing systems with unknown service rates, achieving sublinear regret by learning server preferences via preference feedback.

QueST: A novel self-supervised skill abstraction architecture for continuous robot control, achieves state-of-the-art performance on multitask and few-shot learning benchmarks by learning flexible, tr…

QUEST, a novel Metropolis-Hastings sampling method, generates high-quality & diverse machine translations by using quality metrics as energy functions, overcoming limitations of likelihood-based and r…

QUEST: Quadruple Multimodal Contrastive Learning tackles feature suppression by using quaternion embedding to extract unique information while penalizing excessive shared information influence, achiev…

Novel algorithms for query-efficient correlation clustering with noisy oracles achieve a balance between query complexity and solution quality, offering theoretical guarantees and outperforming baseli…

Researchers developed a query-based attack that generates adversarial prompts, fooling language models into producing harmful outputs with significantly higher success rates than previous methods, eff…

QUEEN: A novel framework for quantized and efficient streaming of free-viewpoint videos achieving high compression, quality, and speed.

Quasi-Bayesian Vine (QB-Vine) efficiently models high-dimensional densities by recursively updating 1D marginal predictives and a vine copula, significantly outperforming state-of-the-art methods.

QuaRot: Revolutionizing 4-bit LLM inference with lossless quantization via rotation!

Quantum Deep Equilibrium Models (QDEQs) achieve higher QML performance with shallower circuits by using a DEQ training paradigm, improving near-term quantum computation efficiency.