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Zhuotong Chen

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5 papers

2 author rows

AAAI Conference 2026 Conference Paper

Preference Optimization via Contrastive Divergence: Your Policy Is Secretly an NLL Estimator

Zhuotong Chen
Fang Liu
Xuan Zhu
Haozhu Wang
Jiayu Li
Yanjun Qi
Mohammad Ghavamzadeh

Existing studies on preference optimization (PO) have been focused on constructing pairwise preference data following simple heuristics, such as maximizing the margin between chosen and rejected responses based on human (or AI) ratings. In this work, we develop a novel PO framework that provides theoretical guidance to effectively sample rejected responses. To achieve this, we formulate PO as minimizing the negative log-likelihood (NLL) of a probability model and propose a sampling-based solution to estimate its normalization constant via contrastive divergence. We show that these estimative samples can act as rejected responses in PO. Leveraging the connection established between PO and NLL estimation, we propose a novel PO algorithm, called Monte-Carlo-based PO (MC-PO), that applies a MC kernel to sample *hard negatives* w.r.t.~the log-likelihood of the target policy. Intuitively, these hard negatives represent the rejected samples that are most difficult for the current policy to differentiate. We show that MC-PO outperforms existing SOTA baselines on popular alignment benchmarks.

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TMLR Journal 2024 Journal Article

Contrastive Learning with Consistent Representations

Zihu Wang
Yu Wang
Zhuotong Chen
Hanbin Hu
Peng Li

Contrastive learning demonstrates great promise for representation learning. Data augmentations play a critical role in contrastive learning by providing informative views of the data without necessitating explicit labels. Nonetheless, the efficacy of current methodologies heavily hinges on the quality of employed data augmentation (DA) functions, often chosen manually from a limited set of options. While exploiting diverse data augmentations is appealing, the complexities inherent in both DAs and representation learning can lead to performance deterioration. Addressing this challenge and facilitating the systematic incorporation of diverse data augmentations, this paper proposes Contrastive Learning with Consistent Representations (CoCor). At the heart of CoCor is a novel consistency metric termed DA consistency. This metric governs the mapping of augmented input data to the representation space. Moreover, we propose to learn the optimal mapping locations as a function of DA. Experimental results demonstrate that CoCor notably enhances the generalizability and transferability of learned representations in comparison to baseline methods. The implementation of CoCor can be found at https://github.com/zihuwang97/CoCor.

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TMLR Journal 2024 Journal Article

PID Control-Based Self-Healing to Improve the Robustness of Large Language Models

Zhuotong Chen
Zihu Wang
Yifan Yang
Qianxiao Li
Zheng Zhang

Despite the effectiveness of deep neural networks in numerous natural language processing applications, recent findings have exposed the vulnerability of these language models when minor perturbations are introduced. While appearing semantically indistinguishable to humans, these perturbations can significantly reduce the performance of well-trained language models, raising concerns about the reliability of deploying them in safe-critical situations. In this work, we construct a computationally efficient self-healing process to correct undesired model behavior during online inference when perturbations are applied to input data. This is formulated as a trajectory optimization problem in which the internal states of the neural network layers are automatically corrected using a PID (Proportional-Integral-Derivative) control mechanism. The P controller targets immediate state adjustments, while the I and D controllers consider past states and future dynamical trends, respectively. We leverage the geometrical properties of the training data to design effective linear PID controllers. This approach reduces the computational cost to that of using just the P controller, instead of the full PID control. Further, we introduce an analytical method for approximating the optimal control solutions, enhancing the real-time inference capabilities of this controlled system. Moreover, we conduct a theoretical error analysis of the analytic solution in a simplified setting. The proposed PID control-based self-healing is a low-cost framework that improves the robustness of pre-trained large language models, whether standard or robustly trained, against a wide range of perturbations.

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JMLR Journal 2022 Journal Article

Self-Healing Robust Neural Networks via Closed-Loop Control

Zhuotong Chen
Qianxiao Li
Zheng Zhang

Despite the wide applications of neural networks, there have been increasing concerns about their vulnerability issue. While numerous attack and defense techniques have been developed, this work investigates the robustness issue from a new angle: can we design a self-healing neural network that can automatically detect and fix the vulnerability issue by itself? A typical self-healing mechanism is the immune system of a human body. This biology-inspired idea has been used in many engineering designs but has rarely been investigated in deep learning. This paper considers the post-training self-healing of a neural network, and proposes a closed-loop control formulation to automatically detect and fix the errors caused by various attacks or perturbations. We provide a margin-based analysis to explain how this formulation can improve the robustness of a classifier. To speed up the inference, we convert the optimal control problem to Pontryagon's Maximum Principle and solve it via the method of successive approximation. Lastly, we present an error estimation of the proposed framework for neural networks with nonlinear activation functions. We validate the performance of several network architectures against various perturbations. Since the self-healing method does not need a-priori information about data perturbations or attacks, it can handle a broad class of unforeseen perturbations. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2022. ( edit, beta )

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ICLR Conference 2021 Conference Paper

Towards Robust Neural Networks via Close-loop Control

Zhuotong Chen
Qianxiao Li
Zheng Zhang

Despite their success in massive engineering applications, deep neural networks are vulnerable to various perturbations due to their black-box nature. Recent study has shown that a deep neural network can misclassify the data even if the input data is perturbed by an imperceptible amount. In this paper, we address the robustness issue of neural networks by a novel close-loop control method from the perspective of dynamic systems. Instead of modifying the parameters in a fixed neural network architecture, a close-loop control process is added to generate control signals adaptively for the perturbed or corrupted data. We connect the robustness of neural networks with optimal control using the geometrical information of underlying data to design the control objective. The detailed analysis shows how the embedding manifolds of state trajectory affect error estimation of the proposed method. Our approach can simultaneously maintain the performance on clean data and improve the robustness against many types of data perturbations. It can also further improve the performance of robustly trained neural networks against different perturbations. To the best of our knowledge, this is the first work that improves the robustness of neural networks with close-loop control.

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