Posters

Projected Diffusion Models (PDM) revolutionizes generative modeling by directly incorporating constraints into the sampling process, ensuring high-fidelity outputs that strictly adhere to predefined c…

Constrained sampling made easy! Primal-Dual Langevin Monte Carlo efficiently samples from complex probability distributions while satisfying statistical constraints.

Constrained Latent Action Policies (C-LAP) revolutionizes offline reinforcement learning by jointly modeling state-action distributions, implicitly constraining policies to improve efficiency and redu…

Trust Sampling enhances guided diffusion by iteratively optimizing constrained generation at each step, improving efficiency and accuracy in image and 3D motion generation.

Constrained diffusion models, trained via a novel dual approach, achieve optimal trade-offs between data fidelity and user-defined distribution constraints, enabling fairer and more controlled data ge…

This paper presents a unified framework for solving binary statistical decision-making problems, enabling efficient derivation of optimal strategies for diverse applications like OOD detection and sel…

ConStat: Exposing hidden LLM contamination!

Constant Acceleration Flow (CAF) drastically accelerates image generation in diffusion models by leveraging a constant acceleration equation, outperforming state-of-the-art methods in both speed and q…

Consistency Purification boosts certified robustness by efficiently purifying noisy images using a one-step generative model, achieving state-of-the-art results while maintaining semantic alignment.

Neural causal models consistently estimate partial causal effects, even with continuous/categorical variables, thanks to Lipschitz regularization.

CMPE: a new conditional sampler for SBI, achieves fast few-shot inference with an unconstrained architecture, outperforming current state-of-the-art algorithms on various benchmarks.

Consistency Diffusion Bridge Models (CDBMs) dramatically speed up diffusion bridge model sampling by learning a consistency function, achieving up to a 50x speedup with improved sample quality.

SeCo: Semantic Connectivity-driven Pseudo-Labeling enhances cross-domain semantic segmentation by correcting noisy pseudo-labels at the connectivity level, improving model accuracy and robustness.

Data connectivity profoundly shapes implicit regularization in matrix factorization for matrix completion, transitioning from low nuclear norm to low rank solutions as data shifts from disconnected to…

LLMs surprisingly infer censored knowledge from implicit training data hints, posing safety challenges.

Boosting zero-shot OOD detection accuracy, this paper introduces a conjugated semantic pool (CSP) improving FPR95 by 7.89%. CSP leverages modified superclass names for superior OOD label identificatio…

A novel conjugate Bayesian two-step change point detection method for Hawkes processes, CoBay-CPD, achieves higher accuracy and efficiency by employing data augmentation for improved dynamic event mod…

CRFed, a novel federated learning framework, uses diffusion-based data harmonization and confusion-resistant strategies to significantly boost accuracy and robustness in non-IID data scenarios.

Conformalized Time Series with Semantic Features (CT-SSF) significantly improves time-series forecasting by dynamically weighting latent semantic features, achieving greater prediction efficiency whil…

This paper introduces Selective Conformal P-Value (SCPV), a novel method for controlling FDR in conformalized multiple testing after data-dependent selection, offering a unified theoretical framework …