Posters

Differentially private ReLU regression algorithms, DP-GLMtron and DP-TAGLMtron, achieve comparable performance with only an additional factor of O(log N) in the utility upper bound compared to the con…

Researchers developed Camera-Agnostic Patch (CAP) attacks, improving adversarial patch reliability by simulating camera image processing in attacks against person detectors.

ReVideo enables precise local video editing by independently controlling content and motion, overcoming limitations of existing methods and paving the way for advanced video manipulation.

Reverse the forget-retain objectives for efficient LLM unlearning!

I-Div accurately quantifies distribution discrepancy between training and test datasets without test labels, enabling reliable hypothesis applicability evaluation in complex scenarios.

Boosting time series forecasting accuracy, Retrieval-Augmented Diffusion Models (RATD) leverage relevant historical data to guide the diffusion process, overcoming limitations of existing models and d…

LoCalPFN: boosting in-context tabular learning via retrieval & fine-tuning!

RETR: Multi-view radar detection transformer significantly improves indoor object detection and segmentation.

Selective Projection Decay (SPD) enhances robust fine-tuning of foundation models by selectively applying weight decay, improving generalization and out-of-distribution robustness.

This paper presents a novel regional cross-attention module for rich-context layout-to-image generation, significantly improving image accuracy while addressing limitations of existing methods. Two n…

GLAFF: A novel framework that significantly improves time series forecasting robustness by fusing global timestamp information with local observations, achieving 12.5% average performance enhancement.

Boosting spiking neural network accuracy by 4.05% on ImageNet and achieving state-of-the-art results on CIFAR10-DVS and N-Caltech101 through learnable initial membrane potential and refined training s…

NewImp boosts diffusion models’ missing data imputation by curbing sample diversity and eliminating data masking, achieving superior accuracy.

This paper proves that higher-order GNNs can universally approximate strong branching in MILP solvers, whereas simpler GNNs can only accurately approximate for a specific class of problems.

Researchers enhanced image generation by improving score distillation sampling via a novel Schrödinger Bridge framework, improving realism without computational overhead.

MUSE, a novel graph anomaly detection method, leverages multifaceted summaries of reconstruction errors, achieving state-of-the-art performance by addressing limitations of existing Graph-AE-based met…

Enhanced VQAs via Hamming Weight Preserving ansatz and parity checks achieve superior performance on quantum chemistry and combinatorial problems, showcasing quantum advantage potential in NISQ era.

ImOOD tackles the challenge of imbalanced data distribution in OOD detection by introducing a generalized statistical framework and a unified regularization technique, leading to significant performan…

Offline RL enhanced via Optimal Transport: A new algorithm stitches best expert behaviors for efficient policy extraction.

Revolutionizing image exposure assessment, Pixel-level IEA Network (P-IEANet) achieves state-of-the-art performance with a novel pixel-level approach, a new dataset (IEA40K), and a benchmark of 19 met…