Author name cluster

Tao Yao

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9 papers

2 author rows

AAAI Conference 2026 Conference Paper

SimDiff: Simpler Yet Better Diffusion Model for Time Series Point Forecasting

Hang Ding
Xue Wang
Tian Zhou
Tao Yao

Diffusion models have recently shown promise in time series forecasting, particularly for probabilistic predictions. However, they often fail to achieve state-of-the-art point estimation performance compared to regression-based methods. This limitation stems from difficulties in providing sufficient contextual bias to track distribution shifts and in balancing output diversity with the stability and precision required for point forecasts. Existing diffusion-based approaches mainly focus on full-distribution modeling under probabilistic frameworks, often with likelihood maximization objectives, while paying little attention to dedicated strategies for high-accuracy point estimation. Moreover, other existing point prediction diffusion methods frequently rely on pre-trained or jointly trained mature models for contextual bias, sacrificing the generative flexibility of diffusion models. To address these challenges, we propose SimDiff, a single-stage, end-to-end framework. SimDiff employs a single unified Transformer network carefully tailored to serve as both denoiser and predictor, eliminating the need for external pre-trained or jointly trained regressors. It achieves state-of-the-art point estimation performance by leveraging intrinsic output diversity and improving mean squared error accuracy through multiple inference ensembling. Key innovations, including normalization independence and the median-of-means estimator, further enhance adaptability and stability. Extensive experiments demonstrate that SimDiff significantly outperforms existing methods in time series point forecasting.

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TIST Journal 2025 Journal Article

An Underwater Imaging Generative Adversarial Network by Simulating the Mechanism of Light Propagation in Water

Yujuan Sun
Xing Huang
Yanfang Cui
Junyu Dong
Xiaofeng Zhang
Tao Yao

Since capturing underwater images without degradation is challenging, there are few real image datasets with paired ground truth for underwater image enhancement. In this article, we propose a generative adversarial network (UIGAN) for underwater imaging; the network can convert images and their corresponding depth maps captured in air into images in water. The underwater imaging mechanism relies on many intrinsic parameters in water, which are difficult to estimate without field calibration. As the strong modeling capability of deep neural networks, this article uses the deep learning model to extract parameters from the real underwater environment. Then the proposed UIGAN simulates the light propagation process (direct attenuation, backscattering, and forward scattering) in water by using three modules with different constraints. We can generate a large training dataset with paired images in air and real water environment. The generated UIGAN dataset serves as input to a forward-attention transfer underwater enhancement model (FATUECNN), and it can output the restored images with appearance like those captured in air. The proposed pipeline is verified both qualitatively and quantitatively by extensive experiments and comparison evaluation with the existing state-of-the-art methods. The source code and the pre-trained model are made publicly available.

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NeurIPS Conference 2025 Conference Paper

MISA: Memory-Efficient LLMs Optimization with Module-wise Importance Sampling

Yuxi Liu
Renjia Deng
Yutong He
Xue Wang
Tao Yao
Kun Yuan

The substantial memory demands of pre-training and fine-tuning large language models (LLMs) require memory-efficient optimization algorithms. One promising approach is layer-wise optimization, which treats each transformer block as a single layer and optimizes it sequentially, while freezing the other layers to save optimizer states and activations. Although effective, these methods ignore the varying importance of the modules within each layer, leading to suboptimal performance. Moreover, layer-wise sampling provides only limited memory savings, as at least one full layer must remain active during optimization. To overcome these limitations, we propose **M**odule-wise **I**mportance **SA**mpling (**MISA**), a novel method that divides each layer into smaller modules and assigns importance scores to each module. MISA uses a weighted random sampling mechanism to activate modules, provably reducing gradient variance compared to layer-wise sampling. Additionally, we establish an $\mathcal{O}(1/\sqrt{K})$ convergence rate under non-convex and stochastic conditions, where $K$ is the total number of training steps, and provide a detailed memory analysis showcasing MISA's superiority over existing baseline methods. Experiments on diverse learning tasks validate the effectiveness of MISA.

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EAAI Journal 2024 Journal Article

Cross-modal Semantic Interference Suppression for image-text matching

Tao Yao
Shouyong Peng
Yujuan Sun
Guorui Sheng
Haiyan Fu
Xiangwei Kong

Image-text matching, which aims at precisely measuring the visual-semantic similarities between images and texts, is a fundamental research topic in multimedia analysis domain. Current methods have obtained an impressive performance by taking advantage of Transformer architecture. However, most of them only consider inter-modal relationships to mine the image-text semantic correspondences, which makes them hard to accurately measure the similarity when facing similar images and text due to the cross-modal semantic interferences. In this work, to tackle the issue mentioned above, we propose a Cross-Modal Semantic Interference Suppression (CMSIS) method, which incorporates intra-modal fine-grained semantics and unmatched segments to suppress the semantic influences caused by similar heterogeneous data points. The intra-modal fine-grained semantics are utilized to push similar images or text away in the learned latent embedding space for better matching results. To further suppress the cross-modal semantic interferences among similar data points, the unmatched segments that can provide explicit clues to distinguish similar images or text, is also adopted. Experimental results on two popular multimodal datasets have demonstrated that the proposed CMSIS outperforms a range of baselines.

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AAMAS Conference 2023 Conference Paper

Online Influence Maximization under Decreasing Cascade Model

Fang Kong
Jize Xie
Baoxiang Wang
Tao Yao
Shuai Li

We study online influence maximization (OIM) under a new model of decreasing cascade (DC). This model is a generalization of the independent cascade (IC) model by considering the common phenomenon of market saturation. In DC, the chance of an influence attempt being successful reduces with previous failures. The effect is neglected by previous OIM works under IC and linear threshold models. We propose the DC-UCB algorithm to solve this problem, which achieves a regret bound of the same order as the state-of-theart works on the IC model. Extensive experiments on both synthetic and real datasets show the effectiveness of our algorithm.

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NeurIPS Conference 2022 Conference Paper

FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting

Tian Zhou
Ziqing Ma
Xue Wang
Qingsong Wen
Liang Sun
Tao Yao
Wotao Yin
Rong Jin

Recent studies have shown that deep learning models such as RNNs and Transformers have brought significant performance gains for long-term forecasting of time series because they effectively utilize historical information. We found, however, that there is still great room for improvement in how to preserve historical information in neural networks while avoiding overfitting to noise present in the history. Addressing this allows better utilization of the capabilities of deep learning models. To this end, we design a Frequency improved Legendre Memory model, or FiLM: it applies Legendre polynomial projections to approximate historical information, uses Fourier projection to remove noise, and adds a low-rank approximation to speed up computation. Our empirical studies show that the proposed FiLM significantly improves the accuracy of state-of-the-art models in multivariate and univariate long-term forecasting by (19. 2%, 22. 6%), respectively. We also demonstrate that the representation module developed in this work can be used as a general plugin to improve the long-term prediction performance of other deep learning modules. Code is available at https: //github. com/tianzhou2011/FiLM/.

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ICRA Conference 2022 Conference Paper

Tracking Fast Trajectories with a Deformable Object using a Learned Model

James A. Preiss
David Millard 0001
Tao Yao
Gaurav S. Sukhatme

We propose a method for robotic control of deformable objects using a learned nonlinear dynamics model. After collecting a dataset of trajectories from the real system, we train a recurrent neural network (RNN) to approximate its input-output behavior with a latent state-space model. The RNN internal state is low-dimensional enough to enable realtime nonlinear control methods. We demonstrate a closed-loop control scheme with the RNN model using a standard nonlinear state observer and model-predictive controller. We apply our method to track a highly dynamic trajectory with a point on the deformable object, in real time and on real hardware. Our experiments show that the RNN model captures the true system's frequency response and can be used to track trajectories outside the training distribution. In an ablation study, we find that the full method improves tracking accuracy compared to an open-loop version without the state observer.

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IJCAI Conference 2019 Conference Paper

A Practical Semi-Parametric Contextual Bandit

Yi Peng
Miao Xie
Jiahao Liu
Xuying Meng
Nan Li
Cheng Yang
Tao Yao
Rong Jin

Classic multi-armed bandit algorithms are inefficient for a large number of arms. On the other hand, contextual bandit algorithms are more efficient, but they suffer from a large regret due to the bias of reward estimation with finite dimensional features. Although recent studies proposed semi-parametric bandits to overcome these defects, they assume arms' features are constant over time. However, this assumption rarely holds in practice, since real-world problems often involve underlying processes that are dynamically evolving over time especially for the special promotions like Singles' Day sales. In this paper, we formulate a novel Semi-Parametric Contextual Bandit Problem to relax this assumption. For this problem, a novel Two-Steps Upper-Confidence Bound framework, called Semi-Parametric UCB (SPUCB), is presented. It can be flexibly applied to linear parametric function problem with a satisfied gap-free bound on the n-step regret. Moreover, to make our method more practical in online system, an optimization is proposed for dealing with high dimensional features of a linear function. Extensive experiments on synthetic data as well as a real dataset from one of the largest e-commercial platforms demonstrate the superior performance of our algorithm.

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ICML Conference 2018 Conference Paper

Minimax Concave Penalized Multi-Armed Bandit Model with High-Dimensional Convariates

Xue Wang
Mike Mingcheng Wei
Tao Yao

In this paper, we propose a Minimax Concave Penalized Multi-Armed Bandit (MCP-Bandit) algorithm for a decision-maker facing high-dimensional data with latent sparse structure in an online learning and decision-making process. We demonstrate that the MCP-Bandit algorithm asymptotically achieves the optimal cumulative regret in sample size T, O(log T), and further attains a tighter bound in both covariates dimension d and the number of significant covariates s, O(s^2 (s + log d). In addition, we develop a linear approximation method, the 2-step Weighted Lasso procedure, to identify the MCP estimator for the MCP-Bandit algorithm under non-i. i. d. samples. Using this procedure, the MCP estimator matches the oracle estimator with high probability. Finally, we present two experiments to benchmark our proposed the MCP-Bandit algorithm to other bandit algorithms. Both experiments demonstrate that the MCP-Bandit algorithm performs favorably over other benchmark algorithms, especially when there is a high level of data sparsity or when the sample size is not too small.

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