Posters

Learn on massive graphs efficiently using Intersecting Community Graphs (ICGs)! This method approximates large graphs with ICGs, enabling linear time/memory complexity for node classification.

ELCD: The first neural network guaranteeing globally contracting dynamics!

DDiffPG: A novel actor-critic algorithm learns multimodal policies from scratch using diffusion models, enabling agents to master versatile behaviors in complex tasks.

Researchers developed an efficient algorithm to uniquely identify causal relationships from mixed interventional and observational data with noisy interventions.

Learn macroscopic dynamics efficiently using only partial microscopic force computations! This novel method leverages sparsity assumptions and stochastic estimation for accurate, cost-effective modeli…

Low-Rank Feature Learning (LRFL) significantly boosts thorax disease classification accuracy by reducing noise and background interference in medical images.

Create animatable interacting hand avatars from a single image using a novel two-stage interaction-aware 3D Gaussian splatting framework!

Learn continuous symmetries from data without pre-defined groups using Neural ODEs and a novel validity score to improve model generalization and efficiency.

Learning against adaptive adversaries in Markov games is hard, but this paper shows how to achieve low policy regret with efficient algorithms by introducing a new notion of consistent adaptive advers…

PaDIS: Patch-based diffusion inverse solver learns efficient image priors from image patches, enabling high-resolution inverse problem solutions with reduced computational costs and data needs.

FCR, a novel method, reveals causal structure in single-cell perturbation data by learning disentangled cellular representations specific to covariates, treatments, and their interactions, outperformi…

Aligning AI’s world representation with humans enables faster, safer learning of human values, improving both exploration and generalization.

This paper proposes a novel method to model group actions within autoencoders by learning these actions in the latent space, enhancing model versatility and improving performance in various real-world…

Goal-conditioned contrastive learning boosts language reward model performance and enables better control of language model generation.

First-ever sublinear regret & constraint violation bounds achieved for infinite horizon average reward CMDPs with general policy parametrization using a novel primal-dual policy gradient algorithm.

ZORRO: A new method for learning linear models from uncertain data, providing sound over-approximations of all possible models and prediction ranges.

Boost deep learning generalization with Learning from Teaching (LOT)! LOT trains auxiliary ‘student’ models to imitate a primary ’teacher’ model, improving the teacher’s ability to capture generalizab…

This paper presents novel theoretical frameworks and algorithms for learning from snapshots of discrete and continuous data streams, resolving key learnability challenges in online learning under cont…

Self-Supervised Controllable Generation (SCG) framework achieves brain-like associative generation by using a modular autoencoder with equivariance constraints and a self-supervised pattern completion…

LOFF-TA leverages offline foundation model features and tensor augmentations for efficient, resource-light training, achieving up to 37x faster training and 26x less GPU memory usage.