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Chen Cheng

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7 papers

2 author rows

AAAI Conference 2026 Conference Paper

Multi-Value Alignment for LLMs via Value Decorrelation and Extrapolation

Hefei Xu
Le Wu
Chen Cheng
Hao Liu

With the rapid advancement of large language models (LLMs), aligning them with human values for safety and ethics has become a critical challenge. This problem is especially challenging when multiple, potentially conflicting human values must be considered and balanced. Although several variants of existing alignment methods (such as Reinforcement Learning from Human Feedback (RLHF) and Direct Preference Optimization (DPO)) have been proposed to address multi-value alignment, they suffer from notable limitations: 1) they are often unstable and inefficient in multi-value optimization; and 2) they fail to effectively handle value conflicts. As a result, these approaches typically struggle to achieve optimal trade-offs when aligning multiple values. To address this challenge, we propose a novel framework called Multi-Value Alignment (MVA). It mitigates alignment degradation caused by parameter interference among diverse human values by minimizing their mutual information. Furthermore, we propose a value extrapolation strategy to efficiently explore the Pareto frontier, thereby constructing a set of LLMs with diverse value preferences. Extensive experiments demonstrate that MVA consistently outperforms existing baselines in aligning LLMs with multiple human values.

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AAMAS Conference 2024 Conference Paper

ODEs Learn to Walk: ODE-Net based Data-Driven Modeling for Crowd Dynamics

Chen Cheng
Jinglai Li

Predicting the behaviors of pedestrian crowds is of critical importance for a variety of real-world problems. Data driven modeling, which aims to learn the mathematical models from observed data, is a promising tool to construct models that can make accurate predictions of such systems. In this work, we present a data-driven modeling approach based on the ODE-Net framework, for constructing continuous-time models of crowd dynamics. We discuss some challenging issues in applying the ODE-Net method to such problems, which are primarily associated with the dimensionality of the underlying crowd system, and we propose to address these issues by incorporating the social-force concept in the ODE-Net framework. Finally application examples are provided to demonstrate the performance of the proposed method.

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NeurIPS Conference 2023 Conference Paper

Collaboratively Learning Linear Models with Structured Missing Data

Chen Cheng
Gary Cheng
John C. Duchi

We study the problem of collaboratively learning least squares estimates for $m$ agents. Each agent observes a different subset of the features---e. g. , containing data collected from sensors of varying resolution. Our goal is to determine how to coordinate the agents in order to produce the best estimator for each agent. We propose a distributed, semi-supervised algorithm Collab, consisting of three steps: local training, aggregation, and distribution. Our procedure does not require communicating the labeled data, making it communication efficient and useful in settings where the labeled data is inaccessible. Despite this handicap, our procedure is nearly asymptotically, local-minimax optimal---even among estimators allowed to communicate the labeled data such as imputation methods. We test our method on US Census data. We also discuss generalizations of our method to non-Gaussian feature settings, non-linear settings, and Federated Learning.

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TIST Journal 2016 Journal Article

A Unified Point-of-Interest Recommendation Framework in Location-Based Social Networks

Chen Cheng
Haiqin Yang
Irwin King
Michael R. Lyu

Location-based social networks (LBSNs), such as Gowalla, Facebook, Foursquare, Brightkite, and so on, have attracted millions of users to share their social friendship and their locations via check-ins in the past few years. Plenty of valuable information is accumulated based on the check-in behaviors, which makes it possible to learn users’ moving patterns as well as their preferences. In LBSNs, point-of-interest (POI) recommendation is one of the most significant tasks because it can help targeted users explore their surroundings as well as help third-party developers provide personalized services. Matrix factorization is a promising method for this task because it can capture users’ preferences to locations and is widely adopted in traditional recommender systems such as movie recommendation. However, the sparsity of the check-in data makes it difficult to capture users’ preferences accurately. Geographical influence can help alleviate this problem and have a large impact on the final recommendation result. By studying users’ moving patterns, we find that users tend to check in around several centers and different users have different numbers of centers. Based on this, we propose a Multi-center Gaussian Model (MGM) to capture this pattern via modeling the probability of a user’s check-in on a location. Moreover, users are usually more interested in the top 20 or even top 10 recommended POIs, which makes personalized ranking important in this task. From previous work, directly optimizing for pairwise ranking like Bayesian Personalized Ranking (BPR) achieves better performance in the top- k recommendation than directly using matrix matrix factorization that aims to minimize the point-wise rating error. To consider users’ preferences, geographical influence and personalized ranking, we propose a unified POI recommendation framework, which unifies all of them together. Specifically, we first fuse MGM with matrix factorization methods and further with BPR using two different approaches. We conduct experiments on Gowalla and Foursquare datasets, which are two large-scale real-world LBSN datasets publicly available online. The results on both datasets show that our unified POI recommendation framework can produce better performance.

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IROS Conference 2015 Conference Paper

Kinematics, stiffness and natural frequency of a redundantly actuated masticatory robot constrained by two point-contact higher kinematic pairs

Chen Cheng
Weiliang Xu 0001
Jianzhong Shang

This paper presents kinematics and two dynamic characteristics including stiffness and natural frequency of a four degrees-of-freedom (DOFs) masticatory robot. Based on the biological architecture of human masticatory system, this robot is modeled in the form of a redundantly actuated parallel mechanism constrained by two point-contact higher kinematic pairs (HKPs). The aim of this robot is to chew samples of newly developed foods in a human way while the food textures can be characterized. The kinematics including inverse kinematics, workspace and singularity are studied systematically. The inverse kinematics is given in closed-form solutions. The workspace is generated by a numerical search method. In virtue of the Jacobian matrix derived from differential kinematics, the singularity and the stiffness are investigated. Via the derived stiffness matrix and mass matrix, natural frequency is also computed. Finally, simulation is carried out, one real incisor trajectory of a healthy human subject is reproduced, illustrating the robot is able to reproduce kinematic behaviors of the human masticatory system, and it owns a satisfactory performance in terms of stiffness and natural frequency. Comparison between the redundantly actuated masticatory robot and its two non-redundantly actuated counterparts also exhibits that the former owns better performance, in terms of a similar incisor's workspace as the Posselt envelope, no singularity, larger stiffness and higher natural frequency.

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IJCAI Conference 2013 Conference Paper

Where You Like to Go Next: Successive Point-of-Interest Recommendation

Chen Cheng
Haiqin Yang
Michael R. Lyu
Irwin King

Personalized point-of-interest (POI) recommendation is a signiﬁcant task in location-based social networks (LBSNs) as it can help provide better user experience as well as enable third-party services, e. g. , launching advertisements. To provide a good recommendation, various research has been conducted in the literature. However, pervious efforts mainly consider the “check-ins” in a whole and omit their temporal relation. They can only recommend POI globally and cannot know where a user would like to go tomorrow or in the next few days. In this paper, we consider the task of successive personalized POI recommendation in LB- SNs, which is a much harder task than standard personalized POI recommendation or prediction. To solve this task, we observe two prominent properties in the check-in sequence: personalized Markov chain and region localization. Hence, we propose a novel matrix factorization method, namely FPMC- LR, to embed the personalized Markov chains and the localized regions. Our proposed FPMC-LR not only exploits the personalized Markov chain in the check-in sequence, but also takes into account users’ movement constraint, i. e. , moving around a localized region. More importantly, utilizing the information of localized regions, we not only reduce the computation cost largely, but also discard the noisy information to boost recommendation. Results on two real-world LBSNs datasets demonstrate the merits of our proposed FPMC-LR.

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AAAI Conference 2012 Conference Paper

Fused Matrix Factorization with Geographical and Social Influence in Location-Based Social Networks

Chen Cheng
Haiqin Yang
Irwin King
Michael Lyu

Recently, location-based social networks (LBSNs), such as Gowalla, Foursquare, Facebook, and Brightkite, etc. , have attracted millions of users to share their social friendship and their locations via check-ins. The available check-in information makes it possible to mine users’ preference on locations and to provide favorite recommendations. Personalized Point-of-interest (POI) recommendation is a significant task in LBSNs since it can help targeted users explore their surroundings as well as help third-party developers to provide personalized services. To solve this task, matrix factorization is a promising tool due to its success in recommender systems. However, previously proposed matrix factorization (MF) methods do not explore geographical influence, e. g. , multi-center check-in property, which yields suboptimal solutions for the recommendation. In this paper, to the best of our knowledge, we are the first to fuse MF with geographical and social influence for POI recommendation in LBSNs. We first capture the geographical influence via modeling the probability of a user’s check-in on a location as a Multi-center Gaussian Model (MGM). Next, we include social information and fuse the geographical influence into a generalized matrix factorization framework. Our solution to POI recommendation is efficient and scales linearly with the number of observations. Finally, we conduct thorough experiments on a large-scale real-world LBSNs dataset and demonstrate that the fused matrix factorization framework with MGM utilizes the distance information sufficiently and outperforms other state-of-the-art methods significantly.

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