Posters

TREAT: a novel framework boosting dynamical systems modeling accuracy by enforcing Time-Reversal Symmetry (TRS) via a regularization term. High-precision modeling is achieved across diverse systems, …

Physics-constrained learning significantly boosts optical neural network accuracy by addressing systematic physical errors, achieving state-of-the-art results on image classification tasks.

Physically consistent multi-task learning bridges heterogeneous molecular data by directly leveraging physical laws to improve predictions, enhancing accuracy beyond the limitations of individual data…

PHYRECON: A novel neural scene reconstruction method uses differentiable rendering and physics simulation for physically plausible 3D models.

PhyloGen uses a genomic language model to generate and optimize phylogenetic trees, offering faster and more accurate evolutionary analysis than traditional methods.

Phased Consistency Models (PCMs) revolutionize diffusion model generation by overcoming LCM limitations, achieving superior speed and quality in image and video generation.

TPGN, a novel framework for long-range time series forecasting, uses Parallel Gated Networks (PGN) to efficiently capture long-term dependencies, achieving state-of-the-art results on multiple dataset…

pFedClub: Controllable heterogeneous model aggregation boosts personalized federated learning by generating reasonable-sized, personalized models, significantly cutting computational costs.

Pessimistic Backward Policy for GFlowNets (PBP-GFN) tackles GFlowNets’ tendency to under-exploit high-reward objects by maximizing observed backward flow, enhancing high-reward object discovery and ov…

Bi-directional Preference Optimization (BiPO) generates superior steering vectors for personalized LLM control, improving upon existing methods by directly influencing the generation probability of hu…

Fed-POE: A personalized federated learning algorithm for superior real-time predictions!

Personalized federated learning (PFL) often struggles with data scarcity and distribution shifts. pFedFDA, a novel algorithm, tackles this by framing representation learning as a generative modeling …

LM-WEATHER uses pre-trained language models to create highly accurate, personalized weather models directly on resource-constrained devices, achieving state-of-the-art results with significantly reduc…

Persistent Test-Time Adaptation (PeTTA) prevents AI model collapse in recurring scenarios by dynamically adjusting the adaptation strategy based on divergence from the initial model, ensuring long-ter…

Spectral distances on k-nearest neighbor graphs enable robust topological analysis of high-dimensional noisy data using persistent homology, overcoming limitations of Euclidean distance.

Persistence Homology Distillation (PsHD) leverages topological data analysis to robustly preserve structural information in semi-supervised continual learning, significantly outperforming existing met…

PerpCorrect: Robust LLM alignment despite noisy human preferences, achieved via perplexity-based noisy preference detection and correction.

PASQL, a novel periodic agent-state Q-learning algorithm, significantly improves reinforcement learning in partially observable environments by leveraging non-stationary periodic policies to overcome …

PERIA: Holistic language & vision planning for complex robotic manipulation!

Performative control, where control policies change system dynamics, is analyzed; offering sufficient conditions for unique solutions, and proposing a convergent algorithm for achieving them.