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Yi Tian

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7 papers

2 author rows

JBHI Journal 2026 Journal Article

DiffM 4 RI: A Latent Diffusion Model With Modality Inpainting for Synthesizing Missing Modalities in MRI Analysis

Wen Ye
Zhetao Guo
Yuxiang Ren
Yi Tian
Yushi Shen
Zan Chen
Junjun He
Jing Ke

Foundation Models (FMs) have shown great promise for multimodal medical image analysis such as Magnetic Resonance Imaging (MRI). However, certain MRI sequences may be unavailable due to various constraints, such as limited scanning time, patient discomfort, or scanner limitations. The absence of certain modalities can hinder the performance of FMs in clinical applications, making effective missing modality imputation crucial for ensuring their applicability. Previous approaches, including generative adversarial networks (GANs), have been employed to synthesize missing modalities in either a one-to-one or many-to-one manner. However, these methods have limitations, as they require training a new model for different missing scenarios and are prone to mode collapse, generating limited diversity in the synthesized images. To address these challenges, we propose DiffM 4 RI, a diffusion model for many-to-many missing modality imputation in MRI. DiffM 4 RI innovatively formulates the missing modality imputation as a modality-level inpainting task, enabling it to handle arbitrary missing modality situations without the need for training multiple networks. Experiments on the BraTs datasets demonstrate DiffM 4 RI can achieve an average SSIM improvement of 0. 15 over MustGAN, 0. 1 over SynDiff, and 0. 02 over VQ-VAE-2. These results highlight the potential of DiffM 4 RI in enhancing the reliability of FMs in clinical applications.

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EAAI Journal 2025 Journal Article

Dual-path information enhanced pyramid Unet for COVID-19 lung infection segmentation

Yan Zhang
Qi Mao
Yi Tian
Wenfeng Wang
Lijia Ren
Haibo Li

The coronavirus disease 2019 (COVID-19) pandemic has brought computer-aided diagnosis into the spotlight. COVID-19 computed tomography (CT) images often have redundant background, and the proportion of infected area and healthy areas is unbalanced, which may confuse the model and make it difficult to identify the infected areas, resulting in the inability of the model to make accurate determinations. The low contrast between infected and normal areas makes the boundaries difficult to distinguish. The infected areas are small, irregular and unevenly distributed. Thus, models often suffers from incomplete and inadequate segmentation. To solve these challenges, a dual-path information enhanced pyramid Unet network (DIEP-Unet) was proposed for COVID-19 infection segmentation. First, an approach of coarse multi-scale feature map (CMFM) was proposed, which included a Fourier image boundary enhancement (FIBE) module to enhance the boundary features of infected areas, and a multi-scale information extraction and fusion (MSIE) module to locate the position of the infected areas. Second, the hybrid attention global context awareness (HAGCA) module collected hybrid attention information and multi-scale features from different branches, which can better aggregate information between the encoder and the decoder. Experimental results revealed that the proposed method achieves an accuracy of 0. 9982 and a specificity of 0. 9989. Numerical examples and ablation studies demonstrate that the proposed DIEP-Unet achieves high accuracy and outperforms existing segmentation methods. Therefore, the proposed method has the potential applications in the detection, localization, and labeling of other diseased areas.

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NeurIPS Conference 2023 Conference Paper

Convex and Non-convex Optimization Under Generalized Smoothness

Haochuan Li
Jian Qian
Yi Tian
Alexander Rakhlin
Ali Jadbabaie

Classical analysis of convex and non-convex optimization methods often requires the Lipschitz continuity of the gradient, which limits the analysis to functions bounded by quadratics. Recent work relaxed this requirement to a non-uniform smoothness condition with the Hessian norm bounded by an affine function of the gradient norm, and proved convergence in the non-convex setting via gradient clipping, assuming bounded noise. In this paper, we further generalize this non-uniform smoothness condition and develop a simple, yet powerful analysis technique that bounds the gradients along the trajectory, thereby leading to stronger results for both convex and non-convex optimization problems. In particular, we obtain the classical convergence rates for (stochastic) gradient descent and Nesterov's accelerated gradient method in the convex and/or non-convex setting under this general smoothness condition. The new analysis approach does not require gradient clipping and allows heavy-tailed noise with bounded variance in the stochastic setting.

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NeurIPS Conference 2021 Conference Paper

Complexity Lower Bounds for Nonconvex-Strongly-Concave Min-Max Optimization

Haochuan Li
Yi Tian
Jingzhao Zhang
Ali Jadbabaie

We provide a first-order oracle complexity lower bound for finding stationary points of min-max optimization problems where the objective function is smooth, nonconvex in the minimization variable, and strongly concave in the maximization variable. We establish a lower bound of $\Omega\left(\sqrt{\kappa}\epsilon^{-2}\right)$ for deterministic oracles, where $\epsilon$ defines the level of approximate stationarity and $\kappa$ is the condition number. Our lower bound matches the best existing upper bound in the $\epsilon$ and $\kappa$ dependence up to logarithmic factors. For stochastic oracles, we provide a lower bound of $\Omega\left(\sqrt{\kappa}\epsilon^{-2} + \kappa^{1/3}\epsilon^{-4}\right)$. It suggests that there is a gap between the best existing upper bound $\mathcal{O}(\kappa^3 \epsilon^{-4})$ and our lower bound in the condition number dependence.

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ICML Conference 2021 Conference Paper

Online Learning in Unknown Markov Games

Yi Tian
Yuanhao Wang 0001
Tiancheng Yu
Suvrit Sra

We study online learning in unknown Markov games, a problem that arises in episodic multi-agent reinforcement learning where the actions of the opponents are unobservable. We show that in this challenging setting, achieving sublinear regret against the best response in hindsight is statistically hard. We then consider a weaker notion of regret by competing with the \emph{minimax value} of the game, and present an algorithm that achieves a sublinear $\tilde{\mathcal{O}}(K^{2/3})$ regret after $K$ episodes. This is the first sublinear regret bound (to our knowledge) for online learning in unknown Markov games. Importantly, our regret bound is independent of the size of the opponents’ action spaces. As a result, even when the opponents’ actions are fully observable, our regret bound improves upon existing analysis (e. g. , (Xie et al. , 2020)) by an exponential factor in the number of opponents.

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ICML Conference 2021 Conference Paper

Provably Efficient Algorithms for Multi-Objective Competitive RL

Tiancheng Yu
Yi Tian
Jingzhao Zhang
Suvrit Sra

We study multi-objective reinforcement learning (RL) where an agent’s reward is represented as a vector. In settings where an agent competes against opponents, its performance is measured by the distance of its average return vector to a target set. We develop statistically and computationally efficient algorithms to approach the associated target set. Our results extend Blackwell’s approachability theorem \citep{blackwell1956analog} to tabular RL, where strategic exploration becomes essential. The algorithms presented are adaptive; their guarantees hold even without Blackwell’s approachability condition. If the opponents use fixed policies, we give an improved rate of approaching the target set while also tackling the more ambitious goal of simultaneously minimizing a scalar cost function. We discuss our analysis for this special case by relating our results to previous works on constrained RL. To our knowledge, this work provides the first provably efficient algorithms for vector-valued Markov games and our theoretical guarantees are near-optimal.

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NeurIPS Conference 2020 Conference Paper

Towards Minimax Optimal Reinforcement Learning in Factored Markov Decision Processes

Yi Tian
Jian Qian
Suvrit Sra

We study minimax optimal reinforcement learning in episodic factored Markov decision processes (FMDPs), which are MDPs with conditionally independent transition components. Assuming the factorization is known, we propose two model-based algorithms. The first one achieves minimax optimal regret guarantees for a rich class of factored structures, while the second one enjoys better computational complexity with a slightly worse regret. A key new ingredient of our algorithms is the design of a bonus term to guide exploration. We complement our algorithms by presenting several structure dependent lower bounds on regret for FMDPs that reveal the difficulty hiding in the intricacy of the structures.

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