Posters

This paper introduces AdaptCDRP, a novel algorithm that uses conditional distributionally robust optimization to build robust classifiers from noisy labels, achieving superior accuracy.

OPCR, a novel one-step spatio-temporal imputation method, surpasses existing iterative approaches by directly propagating limited observations to the global context, achieving superior accuracy and ef…

Neural networks learn efficiently from higher-order correlations, exceeding the capabilities of random features, as demonstrated through hypothesis tests and novel theoretical analysis in high-dimensi…

FADA: a novel frequency-adapted learning scheme boosts domain-generalized semantic segmentation by decoupling style and content using Haar wavelets, achieving state-of-the-art results.

AI agents efficiently learn Nash equilibria in adversarial team Markov games using a novel learning algorithm with polynomial complexity, resolving prior limitations.

Learn optimal transport maps with structured displacements using elastic costs and a novel bilevel loss function!

AI learns emergent system features from time-series data using a novel differentiable architecture maximizing causal emergence, outperforming pure mutual information maximization.

Manifold Free-Form Flows (M-FFF) achieves fast and accurate generative modeling on Riemannian manifolds using a single function evaluation, outperforming prior methods.

Contrastive Trajectory Representation (CTR) boosts multi-agent reinforcement learning by learning distinguishable agent trajectories using contrastive loss, thus improving performance significantly.

DisPA: a novel disentangled representation learning framework for perceptual point cloud quality assessment achieves superior performance by minimizing mutual information between content and distortio…

This paper introduces a novel tensor rank condition for identifying causal structures among discrete latent variables, advancing causal discovery in complex scenarios.

This paper introduces a novel framework for learning discrete concepts from high-dimensional data, establishing theoretical conditions for identifying underlying hierarchical causal structures and pro…

This research introduces an Expectation-Maximization algorithm to train diffusion models from incomplete and noisy data, enabling their use in data-scarce scientific applications.

DebLoRA: A novel unsupervised learning approach debiases LoRA for remote sensing imagery, boosting minor class performance without sacrificing major class accuracy.

This research develops data-driven methods for selecting optimal cut generating functions in integer programming, providing theoretical guarantees and empirical improvements over existing techniques.

Unveiling cortico-muscular dependence using orthonormal decomposition of density ratios, FMCA-T, enhances movement classification and reveals channel-temporal dependencies.

V2X-Graph: a novel cooperative motion forecasting framework achieving interpretable trajectory feature fusion for enhanced accuracy.

CoupleNet dynamically links protein sequences and structures for improved representations, surpassing state-of-the-art methods in function prediction, particularly for uncommon proteins.

Progressive Contrastive Learning with Hard & Dynamic Prototypes (PCLHD) revolutionizes unsupervised visible-infrared person re-identification by effectively capturing data commonality, divergence, and…

Learned Bregman divergences significantly improve image corruption robustness in adversarial training.