GenAI4ELab/papercli-papers huggingface dataset:
Generalizing Weather Forecast to Fine-grained Temporal Scales via Physics-AI Hybrid Modeling
Authors: Wanghan Xu; Fenghua Ling; Wenlong Zhang; Tao Han; Hao Chen; Wanli Ouyang; LEI BAI
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Data-driven artificial intelligence (AI) models have made significant advancements in weather forecasting, particularly in medium-range and nowcasting. However, most data-driven weather forecasting models are black-box systems that focus on learning data mapping rather than fine-grained physical evolution in the time dimension. Consequently, the limitations in the temporal scale of datasets prevent these models from forecasting at finer time scales. This paper proposes a physics-AI hybrid model (i.e., WeatherGFT) which generalizes weather forecasts to finer-grained temporal scales beyond training dataset. Specifically, we employ a carefully designed PDE kernel to simulate physical evolution on a small time scale (e.g., 300 seconds) and use a parallel neural networks with a learnable router for bias correction. Furthermore, we introduce a lead time-aware training framework to promote the generalization of the model at different lead times. The weight analysis of physics-AI modules indicates that physics conducts major evolution while AI performs corrections adaptively. Extensive experiments show that WeatherGFT trained on an hourly dataset, effectively generalizes forecasts across multiple time scales, including 30-minute, which is even smaller than the dataset's temporal resolution.
Stochastic Zeroth-Order Optimization under Strongly Convexity and Lipschitz Hessian: Minimax Sample Complexity
Authors: Qian Yu; Yining Wang; Baihe Huang; Qi Lei; Jason D. Lee
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Optimization of convex functions under stochastic zeroth-order feedback has been a major and challenging question in online learning. In this work, we consider the problem of optimizing second-order smooth and strongly convex functions where the algorithm is only accessible to noisy evaluations of the objective function it queries. 
We provide the first tight characterization for the rate of the minimax simple regret by developing matching upper and lower bounds. 
We propose an algorithm that features a combination of a bootstrapping stage and a mirror-descent stage. 
Our main technical innovation consists of a sharp characterization for the spherical-sampling gradient estimator under higher-order smoothness conditions, which allows the algorithm to optimally balance the bias-variance tradeoff, 
and a new iterative method for the bootstrapping stage, which maintains the performance for unbounded Hessian.
Improved Analysis for Bandit Learning in Matching Markets
Authors: Fang Kong; Zilong Wang; Shuai Li
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: A rich line of works study the bandit learning problem in two-sided matching markets, where one side of market participants (players) are uncertain about their preferences and hope to find a stable matching during iterative matchings with the other side (arms). The state-of-the-art analysis shows that the player-optimal stable regret is of order $O(K\log T/\Delta^2)$ where $K$ is the number of arms, $T$ is the horizon and $\Delta$ is the players' minimum preference gap. However, this result may be far from the lower bound $\Omega(\max\{N\log T/\Delta^2, K\log T/\Delta\})$ since the number $K$ of arms (workers, publisher slots) may be much larger than that $N$ of players (employers in labor markets, advertisers in online advertising, respectively). In this paper, we propose a new algorithm and show that the regret can be upper bounded by $O(N^2\log T/\Delta^2 + K \log T/\Delta)$. This result removes the dependence on $K$ in the main order term and improves the state-of-the-art guarantee in common cases where $N$ is much smaller than $K$. Such an advantage is also verified in experiments. In addition, we provide a refined analysis for the existing centralized UCB algorithm and show that, under $\alpha$-condition, it achieves an improved $O(N \log T/\Delta^2 + K \log T / \Delta)$ regret.
MambaTree: Tree Topology is All You Need in State Space Model
Authors: Yicheng Xiao; Lin Song; Shaoli Huang; Jiangshan Wang; Siyu Song; Yixiao Ge; Xiu Li; Ying Shan
Venue: NeurIPS 2024 (NeurIPS 2024 spotlight)

Abstract: The state space models, employing recursively propagated features, demonstrate strong representation capabilities comparable to Transformer models and superior efficiency.
However, constrained by the inherent geometric constraints of sequences, it still falls short in modeling long-range dependencies.
To address this issue, we propose the MambaTree network, which first dynamically generates a tree topology based on spatial relationships and input features.
Then, feature propagation is performed based on this graph, thereby breaking the original sequence constraints to achieve stronger representation capabilities.
Additionally, we introduce a linear complexity dynamic programming algorithm to enhance long-range interactions without increasing computational cost.
MambaTree is a versatile multimodal framework that can be applied to both visual and textual tasks.
Extensive experiments demonstrate that our method significantly outperforms existing structured state space models on image classification, object detection and segmentation.
Besides, by fine-tuning large language models, our approach achieves consistent improvements in multiple textual tasks at minor training cost.
Spectral Graph Pruning Against Over-Squashing and Over-Smoothing
Authors: Adarsh Jamadandi; Celia Rubio-Madrigal; Rebekka Burkholz
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Message Passing Graph Neural Networks are known to suffer from two problems that are sometimes believed to be diametrically opposed: over-squashing and over-smoothing. The former results from topological bottlenecks that hamper the information flow from distant nodes and are mitigated by spectral gap maximization, primarily, by means of edge additions. However, such additions often promote over-smoothing that renders nodes of different classes less distinguishable. Inspired by the Braess phenomenon, we argue that deleting edges can address over-squashing and over-smoothing simultaneously. This insight explains how edge deletions can improve generalization, thus connecting spectral gap optimization to a seemingly disconnected objective of reducing computational resources by pruning graphs for lottery tickets. To this end, we propose a computationally effective spectral gap optimization framework to add or delete edges and demonstrate its effectiveness on the long range graph benchmark and on larger heterophilous datasets.
Revive Re-weighting in Imbalanced Learning by Density Ratio Estimation
Authors: Jiaan Luo; Feng Hong; Jiangchao Yao; Bo Han; Ya Zhang; Yanfeng Wang
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: In deep learning, model performance often deteriorates when trained on highly imbalanced datasets, especially when evaluation metrics require robust generalization across underrepresented classes. To address the challenges posed by imbalanced data distributions, this study introduces a novel method utilizing density ratio estimation for dynamic class weight adjustment, termed as Re-weighting with Density Ratio (RDR). Our method adaptively adjusts the importance of each class during training, mitigates overfitting on dominant classes and enhances model adaptability across diverse datasets. Extensive experiments conducted on various large scale benchmark datasets validate the effectiveness of our method. Results demonstrate substantial improvements in generalization capabilities, particularly under severely imbalanced conditions.
LSH-MoE: Communication-efficient MoE Training via Locality-Sensitive Hashing
Authors: Xiaonan Nie; Qibin Liu; Fangcheng Fu; Shenhan Zhu; Xupeng Miao; Xiaoyang Li; Yang Zhang; Shouda Liu; Bin CUI
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Larger transformer models perform better on various downstream tasks but require more cost to scale up the model size. To efficiently enlarge models, the Mixture-of-Expert (MoE) architecture is widely adopted, which consists of a gate network and a series of experts and keep the training cost constant by routing the input data to a fixed number of experts instead of all.
In existing large-scale MoE training systems, experts would be distributed among different GPUs for parallelization, and thus input data requires additional all-to-all communication to access the target expert and conduct corresponding computation. 
However, upon evaluating the training process of three mainstream MoE models on commonly used GPU clusters, we found that the all-to-all communication ratio averaged around 45\%, which significantly hinders the training efficiency and scalability of MoE models.

In this paper, we propose LSH-MoE, a communication-efficient MoE training framework using locality-sensitive hashing (LSH). 
We first present the problems of scaling MoE training in existing systems and highlight the potential of exploiting token similarity to facilitate data compression.
Then, we introduce an efficient LSH-based compression technique, which utilizes the cross-polytope hashing for rapid clustering and implements a residual-based error compensation scheme to alleviate the adverse impact of compression. 
To verify the effectiveness of our methods, we conduct experiments on both language models (e.g., RoBERTa, GPT, and T5) and vision models (e.g., Swin) for both pre-training and fine-tuning tasks. The results demonstrate that our method substantially outperforms its counterparts across different tasks by 1.28-2.2$\times$ of speedup.
Harmonizing Stochasticity and Determinism: Scene-responsive Diverse Human Motion Prediction
Authors: Zhenyu Lou; Qiongjie Cui; Tuo Wang; Zhenbo Song; Luoming Zhang; Cheng Cheng; Haofan Wang; Xu Tang; Huaxia Li; Hong Zhou
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Diverse human motion prediction (HMP) is a fundamental application in computer vision that has recently attracted considerable interest. Prior methods primarily focus on the stochastic nature of human motion, while neglecting the specific impact of external environment, leading to the pronounced artifacts in prediction when applied to real-world scenarios. To fill this gap, this work introduces a novel task: predicting diverse human motion within real-world 3D scenes. In contrast to prior works, it requires harmonizing the deterministic constraints imposed by the surrounding 3D scenes with the stochastic aspect of human motion. For this purpose, we propose DiMoP3D, a diverse motion prediction framework with 3D scene awareness, which leverages the 3D point cloud and observed sequence to generate diverse and high-fidelity predictions. DiMoP3D is able to comprehend the 3D scene, and determines the probable target objects and their desired interactive pose based on the historical motion. Then, it plans the obstacle-free trajectory towards these interested objects, and generates diverse and physically-consistent future motions. On top of that, DiMoP3D identifies deterministic factors in the scene and integrates them into the stochastic modeling, making the diverse HMP in realistic scenes become a controllable stochastic generation process. On two real-captured benchmarks, DiMoP3D has demonstrated significant improvements over state-of-the-art methods, showcasing its effectiveness in generating diverse and physically-consistent motion predictions within real-world 3D environments.
Long-range Meta-path Search on Large-scale Heterogeneous Graphs
Authors: Chao Li; Zijie Guo; Qiuting He; Kun He
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Utilizing long-range dependency, a concept extensively studied in homogeneous graphs, remains underexplored in heterogeneous graphs, especially on large ones, posing two significant challenges: Reducing computational costs while maximizing effective information utilization in the presence of heterogeneity, and overcoming the over-smoothing issue in graph neural networks. To address this gap, we investigate the importance of different meta-paths and introduce 
an automatic framework for utilizing long-range dependency on heterogeneous graphs, denoted as Long-range Meta-path Search through Progressive Sampling (LMSPS). Specifically, we develop a search space with all meta-paths related to the target node type. By employing a progressive sampling algorithm, LMSPS dynamically shrinks the search space with hop-independent time complexity. Through a sampling evaluation strategy, LMSPS conducts a specialized and effective meta-path selection, leading to retraining with only effective meta-paths, thus mitigating costs and over-smoothing. Extensive experiments across diverse heterogeneous datasets validate LMSPS's capability in discovering effective long-range meta-paths, surpassing state-of-the-art methods. Our code is available at https://github.com/JHL-HUST/LMSPS.
Towards Accurate and Fair Cognitive Diagnosis via Monotonic Data Augmentation
Authors: Zheng Zhang; Wei Song; Qi Liu; Qingyang Mao; Yiyan Wang; Weibo Gao; Zhenya Huang; Shijin Wang; Enhong Chen
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Intelligent education stands as a prominent application of machine learning. Within this domain, cognitive diagnosis (CD) is a key research focus that aims to diagnose students' proficiency levels in specific knowledge concepts. As a crucial task within the field of education, cognitive diagnosis encompasses two fundamental requirements: accuracy and fairness. Existing studies have achieved significant success by primarily utilizing observed historical logs of student-exercise interactions. However, real-world scenarios often present a challenge, where a substantial number of students engage with a limited number of exercises. This data sparsity issue can lead to both inaccurate and unfair diagnoses. To this end, we introduce a monotonic data augmentation framework, CMCD, to tackle the data sparsity issue and thereby achieve accurate and fair CD results. Specifically, CMCD integrates the monotonicity assumption, a fundamental educational principle in CD, to establish two constraints for data augmentation. These constraints are general and can be applied to the majority of CD backbones. Furthermore, we provide theoretical analysis to guarantee the accuracy and convergence speed of CMCD. Finally, extensive experiments on real-world datasets showcase the efficacy of our framework in addressing the data sparsity issue with accurate and fair CD results.
From Instance Training to Instruction Learning: Task Adapters Generation from Instructions
Authors: Huanxuan Liao; Shizhu He; Yao Xu; Yuanzhe Zhang; Yanchao Hao; Shengping Liu; Kang Liu; Jun Zhao
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Large language models (LLMs) have acquired the ability to solve general tasks by utilizing instruction finetuning (IFT). However, IFT still relies heavily on instance training of extensive task data, which greatly limits the adaptability of LLMs to real-world scenarios where labeled task instances are scarce and broader task generalization becomes paramount. Contrary to LLMs, humans acquire skills and complete tasks not merely through repeated practice but also by understanding and following instructional guidelines. This paper is dedicated to simulating human learning to address the shortcomings of instance training, focusing on instruction learning to enhance cross-task generalization. Within this context, we introduce Task Adapters Generation from Instructions (TAGI), which automatically constructs the task-specific model in a parameter generation manner based on the given task instructions without retraining for unseen tasks.  Specifically, we utilize knowledge distillation to enhance the consistency between TAGI developed through Learning with Instruction and task-specific models developed through Training with Instance, by aligning the labels, output logits, and adapter parameters between them. TAGI is endowed with cross-task generalization capabilities through a two-stage training process that includes hypernetwork pretraining and finetuning. We evaluate TAGI on the Super-Natural Instructions and P3 datasets. The experimental results demonstrate that TAGI can match or even outperform traditional meta-trained models and other hypernetwork models, while significantly reducing computational requirements. Our code will be available at https://github.com/Xnhyacinth/TAGI.
Semi-supervised Knowledge Transfer Across Multi-omic Single-cell Data
Authors: Fan Zhang; Tianyu Liu; Zihao Chen; Xiaojiang Peng; Chong Chen; Xian-Sheng Hua; Xiao Luo; Hongyu Zhao
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Knowledge transfer between multi-omic single-cell data aims to effectively transfer cell types from scRNA-seq data to unannotated scATAC-seq data. Several approaches aim to reduce the heterogeneity of multi-omic data while maintaining the discriminability of cell types with extensive annotated data. However, in reality, the cost of collecting both a large amount of labeled scRNA-seq data and scATAC-seq data is expensive. Therefore, this paper explores a practical yet underexplored problem of knowledge transfer across multi-omic single-cell data under cell type scarcity. To address this problem, we propose a semi-supervised knowledge transfer framework named Dual label scArcity elimiNation with Cross-omic multi-samplE Mixup (DANCE). To overcome the label scarcity in scRNA-seq data, we generate pseudo-labels based on optimal transport and merge them into the labeled scRNA-seq data. Moreover, we adopt a divide-and-conquer strategy which divides the scATAC-seq data into source-like and target-specific data. For source-like samples, we employ consistency regularization with random perturbations while for target-specific samples, we select a few candidate labels and progressively eliminate incorrect cell types from the label set for additional supervision. Next, we generate virtual scRNA-seq samples with multi-sample Mixup based on the class-wise similarity to reduce cell heterogeneity. Extensive experiments on many benchmark datasets suggest the superiority of our DANCE over a series of state-of-the-art methods.
Genetic-guided GFlowNets for Sample Efficient Molecular Optimization
Authors: Hyeonah Kim; Minsu Kim; Sanghyeok Choi; Jinkyoo Park
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: The challenge of discovering new molecules with desired properties is crucial in domains like drug discovery and material design. Recent advances in deep learning-based generative methods have shown promise but face the issue of sample efficiency due to the computational expense of evaluating the reward function. This paper proposes a novel algorithm for sample-efficient molecular optimization by distilling a powerful genetic algorithm into deep generative policy using GFlowNets training, the off-policy method for amortized inference. This approach enables the deep generative policy to learn from domain knowledge, which has been explicitly integrated into the genetic algorithm. Our method achieves state-of-the-art performance in the official molecular optimization benchmark, significantly outperforming previous methods. It also demonstrates effectiveness in designing inhibitors against SARS-CoV-2 with substantially fewer reward calls.
Implicit Curriculum in Procgen Made Explicit
Authors: Zhenxiong Tan; Kaixin Wang; Xinchao Wang
Venue: NeurIPS 2024 (NeurIPS 2024 spotlight)

Abstract: Procedurally generated environments such as Procgen Benchmark provide a testbed for evaluating the agent's ability to robustly learn a relevant skill, by situating the agent in ever-changing levels. The diverse levels associated with varying contexts are naturally connected to curriculum learning. Existing works mainly focus on arranging the levels to explicitly form a curriculum. In this work, we take a close look at the learning process itself under the multi-level training in Procgen. Interestingly, the learning process exhibits a gradual shift from easy contexts to hard contexts, suggesting an implicit curriculum in multi-level training. Our analysis is made possible through C-Procgen, a benchmark we build upon Procgen that enables explicit control of the contexts. We believe our findings will foster a deeper understanding of learning in diverse contexts, and our benchmark will benefit future research in curriculum reinforcement learning.
Octopus: A Multi-modal LLM with Parallel Recognition and Sequential Understanding
Authors: Chuyang Zhao; YuXin Song; Junru Chen; KANG RONG; Haocheng Feng; Gang Zhang; Shufan Ji; Jingdong Wang; Errui Ding; Yifan Sun
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: A mainstream of Multi-modal Large Language Models (MLLMs) have two essential functions, i.e., visual recognition (e.g., grounding) and understanding (e.g., visual question answering). Presently, all these MLLMs integrate visual recognition and understanding in a same sequential manner in the LLM head, i.e., generating the response token-by-token for both recognition and understanding. We think unifying them in the same sequential manner is not optimal for two reasons: 1) parallel recognition is more efficient than sequential recognition and is actually prevailing in deep visual recognition, and 2) the recognition results can be integrated to help high-level cognition (while the current manner does not). Such motivated, this paper proposes a novel “parallel recognition → sequential understanding” framework for MLLMs. The bottom LLM layers are utilized for parallel recognition and the recognition results are relayed into the top LLM layers for sequential understanding. Specifically, parallel recognition in the bottom LLM layers is implemented via object queries, a popular mechanism in DEtection TRansformer, which we find to harmonize well with the LLM layers. Empirical studies show our MLLM named Octopus improves accuracy on popular MLLM tasks and is up to 5× faster on visual grounding tasks.
FedSSP: Federated Graph Learning with Spectral Knowledge and Personalized Preference
Authors: Zihan Tan; Guancheng Wan; Wenke Huang; Mang Ye
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Personalized Federated Graph Learning (pFGL) facilitates the decentralized training of Graph Neural Networks (GNNs) without compromising privacy while accommodating personalized requirements for non-IID participants. In cross-domain scenarios, structural heterogeneity poses significant challenges for pFGL. Nevertheless, previous pFGL methods incorrectly share non-generic knowledge globally and fail to tailor personalized solutions locally under domain structural shift. We innovatively reveal that the spectral nature of graphs can well reflect inherent domain structural shifts. Correspondingly, our method overcomes it by sharing generic spectral knowledge. Moreover, we indicate the biased message-passing schemes for graph structures and propose the personalized preference module. Combining both strategies, we propose our pFGL framework $\textbf{FedSSP}$ which $\textbf{S}$hares generic $\textbf{S}$pectral knowledge while satisfying graph $\textbf{P}$references. Furthermore, We perform extensive experiments on cross-dataset and cross-domain settings to demonstrate the superiority of our framework. The code is available at https://github.com/OakleyTan/FedSSP.