Posters

OptEx significantly speeds up first-order optimization by cleverly parallelizing iterations, enabling faster convergence for complex tasks.

Opponent Modeling with In-context Search (OMIS) leverages in-context learning and decision-time search for stable and effective opponent adaptation in multi-agent environments.

Opponent modeling based on subgoal inference (OMG) outperforms existing methods by inferring opponent subgoals, enabling better generalization to unseen opponents in multi-agent environments.

POWR: a novel RL algorithm using operator world models and policy mirror descent achieves global convergence with improved sample efficiency.

OPERA: A new algorithm intelligently blends multiple offline policy evaluation estimators for more accurate policy performance estimates.

OpenGaussian achieves 3D point-level open vocabulary understanding using 3D Gaussian Splatting by training 3D instance features with high 3D consistency, employing a two-level codebook for feature dis…

OpenDlign uses novel depth-aligned images from a diffusion model to boost open-world 3D understanding, achieving significant performance gains on diverse benchmarks.

Language Hierarchical Self-training (LHST) enhances weakly-supervised object detection by integrating language hierarchy, mitigating label mismatch, and improving generalization across diverse dataset…

This paper introduces open-book neural algorithmic reasoning, a novel framework that significantly enhances neural reasoning capabilities by allowing networks to access and utilize all training instan…

Open LLMs outperform closed alternatives for private data adaptation, offering superior privacy, performance, and lower costs.

OPEL: a novel optimal transport framework for procedure learning, significantly outperforms SOTA methods by aligning similar video frames and relaxing strict temporal assumptions.

Predictive coding networks learn faster than backpropagation by changing the loss landscape’s geometry, making saddles easier to escape and improving robustness to vanishing gradients.

OnlineTAS, a novel framework, achieves state-of-the-art performance in online temporal action segmentation by using an adaptive memory and a post-processing method to mitigate over-segmentation.

First algorithm for online weighted paging that learns page weights from samples, achieving optimal O(log k) competitiveness and sublinear regret.

ORI: a novel online relational inference framework efficiently identifies hidden interaction graphs in evolving multi-agent systems using streaming data and real-time adaptation.

This paper introduces efficient approximate posterior sampling for contextual bandits using diffusion model priors, improving Thompson sampling’s performance and expressiveness.

Boost online learning algorithms with sublinear best-action queries to achieve optimal regret!

New algorithms conquer delayed feedback in online choice modeling, achieving optimal decision-making even with unknown customer preferences and delayed responses.

This paper proposes a novel, reward-free RLHF framework using a general preference oracle, surpassing existing reward-based approaches in efficiency and generalizability.

Online Label Shift adaptation with Online Feature Updates (OLS-OFU) significantly boosts online label shift adaptation by dynamically refining feature extractors using self-supervised learning, achiev…