AI Applications

LLM-ESR enhances sequential recommendation by integrating semantic information from LLMs, significantly improving performance on long-tail users and items.

LLM-based Skill Diffusion (LDuS) enables zero-shot robotic policy adaptation to various contexts specified in natural language by generating controllable skill trajectories via loss-guided diffusion a…

LiDAR Translator (LiT) unifies diverse LiDAR data through a novel data-driven translation framework, enabling zero-shot and multi-domain joint learning, thus improving autonomous driving systems.

ProtoSurv uses heterogeneous graph representation learning to predict cancer survival more accurately by incorporating tumor heterogeneity and tissue spatial relationships from WSIs.

BeatDiff, a lightweight diffusion model, reconstructs ECG morphology from indirect signals, enabling noise removal, artifact reduction, missing-lead recovery, and anomaly detection.

Conformal Prediction with Length Optimization (CPL) achieves shorter, conditionally valid prediction sets by optimizing length while ensuring coverage across various covariate shifts.

This paper develops efficient algorithms for pricing homogeneous data in online settings, achieving low regret using novel discretization schemes that scale well with data size and number of buyer typ…

Generative Agent Modeling for Multi-agent Adaptation (GAMMA) improves human-AI cooperation by training AI agents against diverse partners generated from a latent model, enhancing zero-shot coordinatio…

AI agents trained with Empowerment via Successor Representations (ESR) empower humans by maximizing their control over environmental outcomes, eliminating the need for human intention inference, unlik…

SDF-Sim: A novel learned rigid-body simulator that leverages SDFs to achieve unprecedented scalability, enabling simulations with hundreds of objects and millions of nodes.

V2X-Graph: a novel cooperative motion forecasting framework achieving interpretable trajectory feature fusion for enhanced accuracy.

Actionable AI agents are trained efficiently via a novel framework, VPDD, which uses discrete diffusion to pre-train on massive human videos, and fine-tunes on limited robot data for superior multi-ta…

LLMs conquer StarCraft II: A new benchmark and Chain of Summarization method enable real-time strategic gameplay evaluation, showcasing impressive LLM strategic abilities.

LLM agents effectively generate realistic personal mobility patterns using semantically rich data.

InteractTraj: Generating realistic, interactive traffic trajectories from natural language!

LaKD: a novel length-agnostic knowledge distillation framework enables accurate trajectory prediction regardless of observation length, overcoming limitations of existing methods.

KEDGN, a novel graph neural network, leverages medical knowledge to model variable-specific temporal dependencies and dynamic inter-variable correlations in irregularly sampled medical time series, si…

KFNN dynamically determines optimal neighborhood sizes for label integration in crowdsourcing, significantly boosting accuracy and robustness, especially with limited noisy labels.

IWBVT: A novel instance weighting approach significantly improves model quality in crowdsourcing by mitigating the impact of intractable instances and achieving a bias-variance trade-off.

iVideoGPT: A scalable, interactive world model trained on millions of human & robot manipulation videos, enabling efficient video prediction and model-based reinforcement learning.