UCF Celebrates 8 Papers Accepted to Top AI Conference, ICML

@conference{Cui2025b,

title = {Improving Soft Unification with Knowledge Graph Embedding Methods},

author = {Xuanming Cui and Wei Peng Chionh and Adriel Kuek and Ser-Nam Lim},

url = {https://openreview.net/forum?id=OOqvY9yvVG



},

year  = {2025},

date = {2025-07-13},

urldate = {2025-07-13},

publisher = {Forty-Second International Conference on Machine Learning},

abstract = {Neural Theorem Provers (NTPs) present a promising framework for neuro-symbolic reasoning, combining end-to-end differentiability with the interpretability of symbolic logic programming. However, optimizing NTPs remains a significant challenge due to their complex objective landscape and gradient sparcity. On the other hand, Knowledge Graph Embedding (KGE) methods offer smooth optimization with well-defined learning objectives but often lack interpretability. In this work, we propose several strategies to integrate the strengths of NTPs and KGEs. By incorporating KGE objectives into the NTP framework, we demonstrate substantial improvements in both accuracy and computational efficiency.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Li2025,

title = {LARM: Large Auto-Regressive Model for Long-Horizon Embodied Intelligence},

author = {Zhuoling Li and Xiaogang Xu and Zhenhua Xu and Ser-Nam Lim and Hengshuang Zhao},

url = {https://lizhuoling.github.io/LARM_webpage/},

year  = {2025},

date = {2025-07-13},

publisher = {Forty-Second International Conference on Machine Learning},

abstract = {Recent embodied agents are primarily built based on reinforcement learning (RL) or large language models (LLMs). Among them, RL agents are efficient for deployment but only perform very few tasks. By contrast, giant LLM agents (often more than 1000B parameters) present strong generalization while demanding enormous computing resources. In this work, we combine their advantages while avoiding the drawbacks by conducting the proposed referee RL on our developed large auto-regressive model (LARM). Specifically, LARM is built upon a lightweight LLM (fewer than 5B parameters) and directly outputs the next action to execute rather than text. We mathematically reveal that classic RL feedbacks vanish in long-horizon embodied exploration and introduce a giant LLM based referee to handle this reward vanishment during training LARM. In this way, LARM learns to complete diverse open-world tasks without human intervention. Especially, LARM successfully harvests enchanted diamond equipment in Minecraft, which demands significantly longer decision-making chains than the highest achievements of prior best methods.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{nokey,

title = {Guarantees of a Preconditioned Subgradient Algorithm for Overparameterized Asymmetric Low-rank Matrix Recovery},

author = {Paris Giampouras and HanQin Cai and René Vidal},

url = {https://arxiv.org/abs/2410.16826},

year  = {2025},

date = {2025-07-13},

urldate = {2025-07-13},

publisher = {Forty-Second International Conference on Machine Learning},

abstract = {In this paper, we focus on a matrix factorization-based approach for robust low-rank and asymmetric matrix recovery from corrupted measurements. We address the challenging scenario where the rank of the sought matrix is unknown and employ an overparameterized approach using the variational form of the nuclear norm as a regularizer. We propose a subgradient algorithm that inherits the merits of preconditioned algorithms, whose rate of convergence does not depend on the condition number of the sought matrix, and addresses their current limitation, i.e., the lack of convergence guarantees in the case of asymmetric matrices with unknown rank. In this setting, we provide, for the first time in the literature, linear convergence guarantees for the derived overparameterized preconditioned subgradient algorithm in the presence of gross corruptions. Additionally, by applying our approach to matrix sensing, we highlight its merits when the measurement operator satisfies the mixed-norm restricted isometry properties. Lastly, we present numerical experiments that validate our theoretical results and demonstrate the effectiveness of our approach.

},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{nokey,

title = {Inference-Time Alignment of LLMs via User-Specified Multi-Criteria Transfer Decoding},

author = {M. F. El Hajj Chehade and S. S. Ghosal and S. Chakraborty and A. Reddy and Dinesh Manocha and H. Zhu, H. and Amrit Singh Bedi },

url = {https://arxiv.org/html/2410.09300v3},

year  = {2025},

date = {2025-07-13},

publisher = {Forty-Second International Conference on Machine Learning},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Chen2025c,

title = {Expressive Power of Graph Neural Networks for (Mixed-Integer) Quadratic Programs},

author = {Ziang Chen and Xiaohan Chen and Jialin Liu and Xinshang Wang and Wotao Yin},

url = {https://openreview.net/forum?id=iqd8aHKwGA},

year  = {2025},

date = {2025-07-13},

abstract = {Quadratic programming (QP) is the most widely applied category of problems in nonlinear programming. Many applications require real-time/fast solutions, though not necessarily with high precision. Existing methods either involve matrix decomposition or use the preconditioned conjugate gradient method. For relatively large instances, these methods cannot achieve the real-time requirement unless there is an effective preconditioner. Recently, graph neural networks (GNNs) opened new possibilities for QP. Some promising empirical studies of applying GNNs for QP tasks show that GNNs can capture key characteristics of an optimization instance and provide adaptive guidance accordingly to crucial configurations during the solving process, or directly provide an approximate solution. Despite notable empirical observations, theoretical foundations are still lacking.



In this work, we investigate the expressive or representative power of GNNs, a crucial aspect of neural network theory, specifically in the context of QP tasks, with both continuous and mixed-integer settings. We prove the existence of message-passing GNNs that can reliably represent key properties of quadratic programs, including feasibility, optimal objective value, and optimal solution. Our theory is validated by numerical results.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Roch2025,

title = {Efficient and Scalable Reinforcement Learning for Average Reward under Model Uncertainty},

author = {Zachary Andrew Roch and George Atia and Yue Wang},

year  = {2025},

date = {2025-07-13},

publisher = {Forty-Second International Conference on Machine Learning},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Zhang2025c,

title = {Pessimism Principle Can Be Effective: Towards a Framework for Zero-Shot Transfer Reinforcement Learning},

author = {Chi Zhang and Ziying Jia and George Atia and Sihong He and Yue Wang},

year  = {2025},

date = {2025-07-13},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Chan-Santiago2025,

title = {MGD^3: Mode-Guided Dataset Distillation using Diffusion Models},

author = {Jeffrey A. Chan-Santiago and Praveen Tirupattur and Gaurav Kumar Nayak and Gaowen Liu and Mubarak Shah},

url = {https://icml.cc/virtual/2025/poster/45507},

year  = {2025},

date = {2025-07-13},

publisher = {Forty-Second International Conference on Machine Learning},

abstract = {Dataset distillation has emerged as an effective strategy, significantly reducing training costs and facilitating more efficient model deployment.Recent advances have leveraged generative models to distill datasets by capturing the underlying data distribution. Unfortunately, existing methods require model fine-tuning with distillation losses to encourage diversity and representativeness. However, these methods do not guarantee sample diversity, limiting their performance.We propose a mode-guided diffusion model leveraging a pre-trained diffusion model without the need to fine-tune with distillation losses. Our approach addresses dataset diversity in three stages: Mode Discovery to identify distinct data modes, Mode Guidance to enhance intra-class diversity, and Stop Guidance to mitigate artifacts in synthetic samples that affect performance.We evaluate our approach on ImageNette, ImageIDC, ImageNet-100, and ImageNet-1K, achieving accuracy improvements of 4.4%, 2.9%, 1.6%, and 1.6%, respectively, over state-of-the-art methods. Our method eliminates the need for fine-tuning diffusion models with distillation losses, significantly reducing computational costs.



},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}