Posters

PromptFix: a novel framework enables diffusion models to precisely follow instructions for diverse image processing tasks, using a new high-frequency guidance sampling method and an auxiliary prompt a…

Prompt-Agnostic Adversarial Perturbation (PAP) defends customized diffusion models against image tampering, achieving superior generalization over prompt-specific methods.

LoPA: a novel parameter-efficient fine-tuning method matches state-of-the-art performance while requiring no server-side adapters, improving upon traditional prompt tuning.

This paper presents novel algorithms for online matching markets that prioritize fairness among dynamic agents, achieving asymptotic optimality in various scenarios and offering extensions to group-le…

Progressive Exploration-Conformal Learning (PECL) revolutionizes sparsely annotated object detection in aerial images by adaptively selecting high-quality pseudo-labels, overcoming limitations of exis…

Progressive Entropic Optimal Transport (PROGOT) solvers efficiently and robustly compute optimal transport plans and maps, even at large scales, by progressively scheduling parameters.

ProEdit: High-quality 3D scene editing via progressive subtask decomposition.

Prod-family algorithms achieve optimal regret in adversarial multi-armed bandits, disproving prior suboptimality conjectures.

LLMs’ in-context learning, though effective, exhibits surprisingly irregular decision boundaries, hindering generalization; this paper reveals this issue and proposes methods to improve smoothness via…

ChatGPT amplifies gender bias in job applications, revealing AI’s potential to worsen labor market inequality.

This study introduces a novel Bayesian functional mixed model that reliably recovers the size and shape of fixed effects from noisy functional data with phase variability, outperforming current state-…

IPR-MPNNs revolutionize graph neural networks by implicitly rewiring graphs using virtual nodes, achieving state-of-the-art performance with significantly faster computation.

Probabilistic Federated Prompt Tuning (PFPT) significantly improves federated learning accuracy on heterogeneous and imbalanced data by using a probabilistic model for prompt aggregation, outperformin…

Probabilistic Decomposed Linear Dynamical Systems (p-dLDS) improve latent variable inference in nonlinear neural systems by using a probabilistic approach that’s robust to noise and includes a time-va…

Enhance multimodal model robustness against missing data with Probabilistic Conformal Distillation (PCD)! PCD models missing modalities probabilistically, achieving superior performance on multiple be…

PrivCirNet accelerates private deep learning inference by cleverly transforming DNN weights into circulant matrices, converting matrix-vector multiplications into efficient 1D convolutions suitable fo…

Achieving near-optimal rates for differentially private stochastic convex optimization with heavy-tailed gradients is possible using simple reduction-based techniques.

New transformation boosts privacy in online learning!

This paper introduces new differentially private algorithms to compute the geometric median, achieving improved accuracy by scaling with the effective data diameter instead of a known radius.

This paper introduces a novel privacy-preserving algorithm for personalized frequency estimation in federated settings, significantly improving accuracy and efficiency over existing methods.