Posters

Proper weight initialization significantly boosts Graph Neural Network (GNN) and Deep Neural Network (DNN) robustness against adversarial attacks, highlighting a critical, often-overlooked factor.

Idiographic Personality Gaussian Process (IPGP) offers a novel measurement framework that captures both shared and individual-specific psychological traits, improving prediction accuracy and revealing…

IDGen synthesizes LLM evaluation prompts using Item Discrimination theory, creating a more challenging and discriminative dataset than previous methods.

MuDI: a novel framework for multi-subject image personalization, effectively decoupling identities to prevent mixing using segmented subjects and a new evaluation metric.

AI pinpoints climate change impacts by identifying spatio-temporal extreme event drivers!

This paper introduces seq-NMF, a novel method for unsupervised subtask discovery in reinforcement learning that leverages selection variables to enhance generalization and data efficiency.

This study introduces a novel framework for individualized reinforcement learning, guaranteeing the identifiability of latent factors influencing state transitions and providing a practical method for…

Researchers can now pinpoint the sources of data shifts in complex linear causal systems using a new algorithm, even with limited perfect interventions, opening exciting possibilities for causal disco…

End-to-end sparse autoencoders revolutionize neural network interpretability by learning functionally important features, outperforming traditional methods in efficiency and accuracy.

Researchers solve the conditional image leakage problem in image-to-video diffusion models by proposing a new inference strategy and a time-dependent noise distribution for training. This yields video…

Unsupervised group recommendation model, ITR, achieves superior user and group recommendation accuracy by dynamically identifying user groups and employing self-supervised learning, eliminating the ne…

This paper proves that non-active exploration suffices for identifying linearly parameterized nonlinear systems with real-analytic features, providing non-asymptotic guarantees for least-squares and s…

Unaligned multimodal mixtures’ shared components are identifiable under mild conditions using a distribution-matching approach, relaxing assumptions of existing methods.

Probabilistic Slot Attention achieves identifiable object-centric representations without supervision, advancing systematic generalization in machine learning.

This paper provides identifiability guarantees for causal disentanglement from purely observational data using nonlinear additive Gaussian noise models, addressing a major challenge in causal represen…

ID-to-3D: Generate expressive, identity-consistent 3D human heads from just a few in-the-wild images using score distillation sampling and 2D diffusion models.

I2EBench: a new benchmark for Instruction-based Image Editing provides a comprehensive evaluation framework using 16 dimensions, aligned with human perception, to evaluate IIE models objectively.

Boosting LLM accuracy, a new calibration method using a special [IDK] token explicitly models uncertainty, mitigating hallucinations, and improving factual precision while maintaining knowledge retent…

HYSYNTH: A hybrid approach uses LLMs to create context-free surrogate models that guide efficient program synthesis, outperforming LLMs alone and existing synthesizers across multiple domains.

HyperPrism, a novel framework, tackles challenges in distributed machine learning by using adaptive non-linear aggregation to handle non-IID data and dynamic communication links, significantly improvi…