Posters

ROPINN: Revolutionizing Physics-Informed Neural Networks with Region Optimization

RoME, a robust contextual bandit algorithm, leverages mixed-effects modeling and debiased machine learning to optimize personalized mobile health interventions, achieving superior performance in simul…

ROIDICE: A novel offline reinforcement learning algorithm maximizes Return on Investment (ROI) by formulating the problem as linear fractional programming, yielding superior return-cost trade-offs.

SGA+ significantly boosts neural image compression by refining latents, offering a flexible, hyperparameter-insensitive approach with improved rate-distortion trade-off.

New algorithms achieve near-optimal error rates for sparse linear regression, even under adversarial data corruption and heavy-tailed noise distributions.

CIMSleepNet: a novel framework for robust sleep staging from incomplete multimodal physiological signals using contrastive imagination.

This paper introduces robust reinforcement learning with general utility, providing novel algorithms with convergence guarantees for training robust policies under environmental uncertainty, significa…

R³M enhances reinforcement learning from human feedback by robustly handling corrupted preference labels, consistently learning the underlying reward and identifying outliers with minimal computationa…

This paper introduces a novel offline active learning method for node-level tasks on graphs, incorporating network structure and node covariates to improve efficiency and robustness, especially in noi…

R-NeuralUCB: A robust neural contextual bandit algorithm uses a context-aware gradient descent training to defend against adversarial reward corruptions, achieving better performance with theoretical …

Outlier-robust mixture learning gets order-optimal error guarantees, even when outliers massively outnumber small groups, via a novel meta-algorithm leveraging mixture structure.

This paper introduces robust group inference procedures for high-dimensional single index models, offering substantial efficiency gains for heavy-tailed errors and handling group testing effectively w…

RUNG: a novel GNN architecture boasting superior robustness against adaptive attacks by employing an unbiased aggregation technique.

Robust Gaussian Processes via Relevance Pursuit tackles noisy data by cleverly inferring data-point specific noise levels, leading to more accurate predictions.

Variance Reduction Fine-tuning (VRF) simultaneously boosts in-distribution and out-of-distribution accuracy in fine-tuned zero-shot models, overcoming the ID-OOD trade-off.

CANDY refines contrastive multi-view clustering by cleverly using inter-view similarities to identify and correct false negatives and a spectral method to remove false positives, resulting in signific…

ZO-GDEGA: A unified algorithm achieves faster, more robust zeroth-order minimax optimization with lower complexity and weaker conditions, solving stochastic nonconvex-concave problems.

Temporal Optimal Transport (TemporalOT) reward enhances robot policy learning by incorporating temporal order information into Optimal Transport (OT)-based proxy rewards, leading to improved accuracy …

RoboMamba: a novel robotic VLA model efficiently combines reasoning and action, achieving high speeds and accuracy while requiring minimal fine-tuning.

RobIR: Robust inverse rendering in high-illumination scenes using ACES tone mapping and regularized visibility estimation for accurate BRDF reconstruction.