12 accepted papers
In recent years, bilevel optimization (BLO) has attracted significant attention for its broad applications in machine learning. However, most existing works on BLO remain confined to the single-task setting and rely on the lower-level strong convexity assumption, which significantly restricts their …
As machine learning (ML) applications grow increasingly complex in recent years, modern ML frameworks often need to address multiple potentially conflicting objectives with coupled decision variables across different layers. This creates a compelling need for multi-objective bilevel learning (MOBL).
Actor-critic methods for decentralized multi-agent reinforcement learning (MARL) facilitate collaborative optimal decision making without centralized coordination, thus enabling a wide range of applications in practice. To date, however, most theoretical convergence studies for existing actor-critic…
Modeling complicated interactions among the ego-vehicle, road agents, and map elements has been a crucial part for safety-critical autonomous driving. Previous work on end-to-end autonomous driving relies on the attention mechanism to handle heterogeneous interactions, which fails to capture geometr
3D scene understanding is vital for applications in autonomous driving, robotics, and augmented reality. However, scene understanding based on 3D Gaussian Splatting faces three key challenges: (i) an imbalance between appearance and semantics, (ii) inconsistencies in object boundaries, and (iii) dif…
Recent advancements in Visual Language Models (VLMs) have significantly driven research in open-vocabulary 3D scene reconstruction, showcasing strong potential in open-set retrieval and semantic understanding. However, existing approaches face challenges in open-world environments: they either suffe
Multi-player multi-armed bandit (MP-MAB) has been widely studied owing to its diverse applications across numerous domains. We consider an MP-MAB problem where $N$ players compete for $K$ arms in $T$ rounds. The reward distributions are heterogeneous where each player has a different expected reward…
[1] Visual Simultaneous Localization and Mapping (SLAM) helps robots estimate their poses and perceive the environment in unknown settings. Recent work has demonstrated that implicit neural radiance fields and 3D Gaussian Splatting (3DGS) offer higher fidelity scene representation than traditional m
Recent advancements in neural implicit fields for Simultaneous Localization and Mapping (SLAM) have provided breakthroughs. However, the benefits of reconstruction results to the perception ability of robot are minimal. Therefore, we propose FI-SLAM, a dense semantic instance SLAM system based on ne…
The advancement of object detection algorithms has catalyzed the development of object-level semantic SLAM. However, due to missed and false detections, object-level semantic SLAM fails to represent the objects within the scene adequately. Therefore, this paper proposes a novel object-level semantic…
NeRF can reconstruct incredibly realistic environmental maps in dense simultaneous localization and mapping, providing robots with more comprehensive scene map information. However, NeRF often struggles with geometric distortions in indoor reconstructions. To correct geometric distortions, we develo…
Online learning to rank (OLTR) is a sequential decision-making problem where a learning agent selects an ordered list of items and receives feedback through user clicks. Although potential attacks against OLTR algorithms may cause serious losses in real-world applications, there is limited knowledge…