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Hao Deng

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5 papers

1 author row

AAAI Conference 2025 Conference Paper

Knockoffs Inference for Partially Linear Models with Automatic Structure Discovery

Hao Wang
Biqin Song
Hao Deng
Hong Chen

Partially linear models (PLM) have attracted much attention in the field of statistical machine learning. Specially, the ability of variable selection of PLM has been studied extensively due to the high requirement of model interpretability. However, few of the existing works concerns the false discovery rate (FDR) controllability of variable selection associated with PLM. To address this issue, we formulate a new Knockoffs Inference scheme for Linear And Nonlinear Discoverer (called KI-LAND), where FDR is controlled with respect to both linear and nonlinear variables for automatic structure discovery. For the proposed KI-LAND, theoretical guarantees are established for both FDR controllability and power, and experimental evaluations are provided to validate its effectiveness.

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IJCAI Conference 2025 Conference Paper

Trajectory-Dependent Generalization Bounds for Pairwise Learning with φ-mixing Samples

Liyuan Liu
Hong Chen
Weifu Li
Tieliang Gong
Hao Deng
Yulong Wang

Recently, the mathematical tool from fractal geometry (i. e. , fractal dimension) has been employed to investigate optimization trajectory-dependent generalization ability for some pointwise learning models with independent and identically distributed (i. i. d. ) observations. This paper goes beyond the limitations of pointwise learning and i. i. d. samples, and establishes generalization bounds for pairwise learning with uniformly strong mixing samples. The derived theoretical results fill the gap of trajectory-dependent generalization analysis for pairwise learning, and can be applied to wide learning paradigms, e. g. , metric learning, ranking and gradient learning. Technically, our framework brings concentration estimation with Rademacher complexity and trajectory-dependent fractal dimension together in a coherent way for felicitous learning theory analysis. In addition, the efficient computation of fractal dimension can be guaranteed for random algorithms (e. g. , stochastic gradient descent algorithm for deep neural networks) by bridging topological data analysis tools and the trajectory-dependent fractal dimension.

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NeurIPS Conference 2024 Conference Paper

LinNet: Linear Network for Efficient Point Cloud Representation Learning

Hao Deng
Kunlei Jing
Shengmei Cheng
Cheng Liu
Jiawei Ru
Jiang Bo
Lin Wang

Point-based methods have made significant progress, but improving their scalability in large-scale 3D scenes is still a challenging problem. In this paper, we delve into the point-based method and develop a simpler, faster, stronger variant model, dubbed as LinNet. In particular, we first propose the disassembled set abstraction (DSA) module, which is more effective than the previous version of set abstraction. It achieves more efficient local aggregation by leveraging spatial anisotropy and channel anisotropy separately. Additionally, by mapping 3D point clouds onto 1D space-filling curves, we enable parallelization of downsampling and neighborhood queries on GPUs with linear complexity. LinNet, as a purely point-based method, outperforms most previous methods in both indoor and outdoor scenes without any extra attention, and sparse convolution but merely relying on a simple MLP. It achieves the mIoU of 73. 7\%, 81. 4\%, and 69. 1\% on the S3DIS Area5, NuScenes, and SemanticKITTI validation benchmarks, respectively, while speeding up almost 10x times over PointNeXt. Our work further reveals both the efficacy and efficiency potential of the vanilla point-based models in large-scale representation learning. Our code will be available upon publication.

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NeurIPS Conference 2023 Conference Paper

Fine-Grained Theoretical Analysis of Federated Zeroth-Order Optimization

Jun Chen
Hong Chen
Bin Gu
Hao Deng

Federated zeroth-order optimization (FedZO) algorithm enjoys the advantages of both zeroth-order optimization and federated learning, and has shown exceptional performance on black-box attack and softmax regression tasks. However, there is no generalization analysis for FedZO, and its analysis on computing convergence rate is slower than the corresponding first-order optimization setting. This paper aims to establish systematic theoretical assessments of FedZO by developing the analysis technique of on-average model stability. We establish the first generalization error bound of FedZO under the Lipschitz continuity and smoothness conditions. Then, refined generalization and optimization bounds are provided by replacing bounded gradient with heavy-tailed gradient noise and utilizing the second-order Taylor expansion for gradient approximation. With the help of a new error decomposition strategy, our theoretical analysis is also extended to the asynchronous case. For FedZO, our fine-grained analysis fills the theoretical gap on the generalization guarantees and polishes the convergence characterization of the computing algorithm.

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AAAI Conference 2022 System Paper

EasySM: A Data-Driven Intelligent Decision Support System for Server Merge

Manhu Qu
Jie Huang
Hao Deng
Runze Wu
Xudong Shen
Jianrong Tao
Tangjie Lv

As independent social economic entities, game servers play a dominant role in building a living and attractive virtual world in massive multi-player online role-playing games (MMORPGs). We propose and implement a novel intelligent decision support system for server merge (SM) which could benefit the maintaining of game ecology at the macro level. The services provided by the system consist of server health diagnosis, server merge assessment, and combination strategy recommendation. In particular, we design an effective time series prediction algorithm to diagnose the health status of one server (e. g. , player activity) based on real game scenarios, and then select the servers with poor status from all servers. Moreover, to dig out the inherent development laws of servers from the historical merge records, we leverage a correlation measurement algorithm to find the historical merged servers that are similar to the servers to be merged and then evaluate the potential trend after merging, which can assist experts to make reasonable decisions. We deploy our system online for multiple MMORPGs and achieve sound online performance endorsed by the game operation team.

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