Yingke Chen

AAAI Conference 2026 Conference Paper

Ambiguity-Tolerant Cross-Modal Hashing with Partial Labels

• Chao Su
• Yanan Li
• Xu Wang
• Yingke Chen
• Huiming Zheng
• Dezhong Peng
• Yuan Sun

Cross-modal hashing (CMH) has achieved remarkable success in large-scale cross-modal retrieval due to its low storage cost and high computational efficiency. However, most existing CMH methods rely on accurately annotated training data, which is often impractical in real-world applications due to the high cost and limited scalability of data annotation. In practice, annotators typically assign a candidate label set rather than a single precise label to each sample pair, resulting in partial labels with inherent ambiguity. Such ambiguous supervision poses significant challenges to conventional CMH methods that assume reliable and unambiguous labels. In this paper, we investigate a less-touched yet meaningful problem, i.e., cross-modal hashing with partial labels (PLCMH). PLCMH faces two major challenges: label ambiguity and modality-alignment barriers induced by misleading supervision. To address these issues, we propose a new approach named Ambiguity-Tolerant Cross-Modal Hashing (ATCH). Specifically, ATCH presents a Local Consensus Disambiguation (LCD) mechanism that resolves label ambiguity by effectively inferring stable and accurate label confidence based on local consensus within the Hamming space. Moreover, ATCH proposes a Confidence-Aware Contrastive Hashing (CACH) mechanism that derives both pseudo labels and trustworthiness scores from the label confidence vectors to learn discriminative hash codes, leading to effective modality alignment. Extensive experiments on three multimodal datasets demonstrate the superiority of ATCH.

AAAI Conference 2026 Conference Paper

Learning Beyond Domains: Misleading Prompts and Pseudo-Label Contrast for Text Domain Generalization

• Qizhi Li
• Xuyang Wang
• Yingke Chen
• Ming Yan
• Dezhong Peng
• Xi Peng
• Xu Wang

Recent advancements in Pre-trained Language Models (PLMs) have significantly enhanced performance across various Natural Language Processing (NLP) tasks. However, the variability in data distributions across different domains presents challenges in generalizing these models to unseen domains. Domain generalization offers a promising solution, but existing text domain generalization methods typically rely on adversarial training to learn domain-invariant features, which often leads to models with high computational and memory overhead. To address this issue, this paper proposes a novel solution named Generalization via Prompts and Contrastive Learning (GenPromptCL) to enhance the generalization capability in domain generalization. GenPromptCL consists of two key components: Domain-Misleading Prompt Learning (DMPL) and Pseudo Label-based Contrastive Learning (PCL). Specifically, DMPL disrupts domain labels randomly, misleading the model into producing incorrect domain labels. This forces the model to learn domain-invariant features. Meanwhile, PCL generates pseudo labels within a single mini-batch, enabling the model to learn both intra-class and inter-class discriminative representations with low time and space complexity. Extensive experimental results demonstrate that GenPromptCL achieves state-of-the-art performance on three distinct text classification tasks (sentiment analysis, rumor detection, and natural language inference) while significantly improving model operation efficiency.

AAAI Conference 2026 Conference Paper

Neighbor-aware Instance Refining with Noisy Labels for Cross-Modal Retrieval

• Yizhi Liu
• Ruitao Pu
• Shilin Xu
• Yingke Chen
• Quan-Hui Liu
• Yuan Sun

In recent years, Cross-Modal Retrieval (CMR) has made significant progress in the field of multi-modal analysis. However, since it is time-consuming and labor-intensive to collect large-scale and well-annotated data, the annotation of multi-modal data inevitably contains some noise. This will degrade the retrieval performance of the model. To tackle the problem, numerous robust CMR methods have been developed, including robust learning paradigms, label calibration strategies, and instance selection mechanisms. Unfortunately, they often fail to simultaneously satisfy model performance ceilings, calibration reliability, and data utilization rate. To overcome the limitations, we propose a novel robust cross-modal learning framework, namely Neighbor-aware Instance Refining with Noisy Labels (NIRNL). Specifically, we first propose Cross-modal Margin Preserving (CMP) to adjust the relative distance between positive and negative pairs, thereby enhancing the discrimination between sample pairs. Then, we propose Neighbor-aware Instance Refining (NIR) to identify pure subset, hard subset, and noisy subset through cross-modal neighborhood consensus. Afterward, we construct different tailored optimization strategies for this fine-grained partitioning, thereby maximizing the utilization of all available data while mitigating error propagation. Extensive experiments on three benchmark datasets demonstrate that NIRNL achieves state-of-the-art performance, exhibiting remarkable robustness, especially under high noise rates.

AAAI Conference 2024 Conference Paper

DiDA: Disambiguated Domain Alignment for Cross-Domain Retrieval with Partial Labels

• Haoran Liu
• Ying Ma
• Ming Yan
• Yingke Chen
• Dezhong Peng
• Xu Wang

Driven by generative AI and the Internet, there is an increasing availability of a wide variety of images, leading to the significant and popular task of cross-domain image retrieval. To reduce annotation costs and increase performance, this paper focuses on an untouched but challenging problem, i.e., cross-domain image retrieval with partial labels (PCIR). Specifically, PCIR faces great challenges due to the ambiguous supervision signal and the domain gap. To address these challenges, we propose a novel method called disambiguated domain alignment (DiDA) for cross-domain retrieval with partial labels. In detail, DiDA elaborates a novel prototype-score unitization learning mechanism (PSUL) to extract common discriminative representations by simultaneously disambiguating the partial labels and narrowing the domain gap. Additionally, DiDA proposes a prototype-based domain alignment mechanism (PBDA) to further bridge the inherent cross-domain discrepancy. Attributed to PSUL and PBDA, our DiDA effectively excavates domain-invariant discrimination for cross-domain image retrieval. We demonstrate the effectiveness of DiDA through comprehensive experiments on three benchmarks, comparing it to existing state-of-the-art methods. Code available: https://github.com/lhrrrrrr/DiDA.

AAAI Conference 2024 Conference Paper

Dual Self-Paced Cross-Modal Hashing

• Yuan Sun
• Jian Dai
• Zhenwen Ren
• Yingke Chen
• Dezhong Peng
• Peng Hu

Cross-modal hashing~(CMH) is an efficient technique to retrieve relevant data across different modalities, such as images, texts, and videos, which has attracted more and more attention due to its low storage cost and fast query speed. Although existing CMH methods achieve remarkable processes, almost all of them treat all samples of varying difficulty levels without discrimination, thus leaving them vulnerable to noise or outliers. Based on this observation, we reveal and study dual difficulty levels implied in cross-modal hashing learning, \ie instance-level and feature-level difficulty. To address this problem, we propose a novel Dual Self-Paced Cross-Modal Hashing (DSCMH) that mimics human cognitive learning to learn hashing from ``easy'' to ``hard'' in both instance and feature levels, thereby embracing robustness against noise/outliers. Specifically, our DSCMH assigns weights to each instance and feature to measure their difficulty or reliability, and then uses these weights to automatically filter out the noisy and irrelevant data points in the original space. By gradually increasing the weights during training, our method can focus on more instances and features from ``easy'' to ``hard'' in training, thus mitigating the adverse effects of noise or outliers. Extensive experiments are conducted on three widely-used benchmark datasets to demonstrate the effectiveness and robustness of the proposed DSCMH over 12 state-of-the-art CMH methods.

AAMAS Conference 2019 Conference Paper

Self-Improving Generative Adversarial Reinforcement Learning

• Yang Liu
• Yifeng Zeng
• Yingke Chen
• Jing Tang
• Yinghui Pan

The lack of data efficiency and stability is one of the main challenges in end-to-end model free reinforcement learning (RL) methods. Recent researches solve the problem resort to supervised learning methods by utilizing human expert demonstrations, e. g. imitation learning. In this paper we present a novel framework which builds a self-improving process upon a policy improvement operator, which is used as a black box such that it has multiple implementation options for various applications. An agent is trained to iteratively imitate behaviors that are generated by the operator. Hence the agent can learn by itself without domain knowledge from human. We employ generative adversarial networks (GAN) to implement the imitation module in the new framework. We evaluate the framework performance over multiple application domains and provide comparison results in support.

IJCAI Conference 2018 Conference Paper

A Group-based Approach to Improve Multifactorial Evolutionary Algorithm

• Jing Tang
• Yingke Chen
• Zixuan Deng
• Yanping Xiang
• Colin Paul Joy

Multifactorial evolutionary algorithm (MFEA) exploits the parallelism of population-based evolutionaryalgorithm and provides an efficient way to evolve individuals for solving multiple tasks concurrently. Its efficiency is derived by implicitly transferring the genetic information among tasks. However, MFEA doesn? t distinguish the information quality in the transfer compromising the algorithmperformance. We propose a group-based MFEA that groups tasks of similar types and selectivelytransfers the genetic information only within the groups. We also develop a new selection criterionand an additional mating selection mechanism in order to strengthen the effectiveness andefficiency of the improved MFEA. We conduct the experiments in both the cross-domain and intra-domainproblems.

JAIR Journal 2017 Journal Article

Decision-Theoretic Planning Under Anonymity in Agent Populations

• Ekhlas Sonu
• Yingke Chen
• Prashant Doshi

We study the problem of self-interested planning under uncertainty in settings shared with more than a thousand other agents, each of which plans at its own individual level. We refer to such large numbers of agents as an agent population. The decision-theoretic formalism of interactive partially observable Markov decision process (I-POMDP) is used to model the agent's self-interested planning. The first contribution of this article is a method for drastically scaling the finitely-nested I-POMDP to certain agent populations for the first time. Our method exploits two types of structure that is often exhibited by agent populations -- anonymity and context-specific independence. We present a variant called the many-agent I-POMDP that models both these types of structure to plan efficiently under uncertainty in multiagent settings. In particular, the complexity of the belief update and solution in the many-agent I-POMDP is polynomial in the number of agents compared with the exponential growth that challenges the original framework. While exploiting structure helps mitigate the curse of many agents, the well-known curse of history that afflicts I-POMDPs continues to challenge scalability in terms of the planning horizon. The second contribution of this article is an application of the branch-and-bound scheme to reduce the exponential growth of the search tree for look ahead. For this, we introduce new fast-computing upper and lower bounds for the exact value function of the many-agent I-POMDP. This speeds up the look-ahead computations without trading off optimality, and reduces both memory and run time complexity. The third contribution is a comprehensive empirical evaluation of the methods on three new problems domains -- policing large protests, controlling traffic congestion at a busy intersection, and improving the AI for the popular Clash of Clans multiplayer game. We demonstrate the feasibility of exact self-interested planning in these large problems, and that our methods for speeding up the planning are effective. Altogether, these contributions represent a principled and significant advance toward moving self-interested planning under uncertainty to real-world applications.

AAAI Conference 2017 Conference Paper

On Markov Games Played by Bayesian and Boundedly-Rational Players

• Muthukumaran Chandrasekaran
• Yingke Chen
• Prashant Doshi

We present a new game-theoretic framework in which Bayesian players with bounded rationality engage in a Markov game and each has private but incomplete information regarding other players’ types. Instead of utilizing Harsanyi’s abstract types and a common prior, we construct intentional player types whose structure is explicit and induces a finite-level belief hierarchy. We characterize an equilibrium in this game and establish the conditions for existence of the equilibrium. The computation of finding such equilibria is formalized as a constraint satisfaction problem and its effectiveness is demonstrated on two cooperative domains.

AAAI Conference 2016 Conference Paper

Bayesian Markov Games with Explicit Finite-Level Types

• Muthukumaran Chandrasekaran
• Yingke Chen
• Prashant Doshi

JAAMAS Journal 2016 Journal Article

Can bounded and self-interested agents be teammates? Application to planning in ad hoc teams

• Muthukumaran Chandrasekaran
• Prashant Doshi
• Yingke Chen

Abstract Planning for ad hoc teamwork is challenging because it involves agents collaborating without any prior coordination or communication. The focus is on principled methods for a single agent to cooperate with others. This motivates investigating the ad hoc teamwork problem in the context of self-interested decision-making frameworks. Agents engaged in individual decision making in multiagent settings face the task of having to reason about other agents’ actions, which may in turn involve reasoning about others. An established approximation that operationalizes this approach is to bound the infinite nesting from below by introducing level 0 models. For the purposes of this study, individual, self-interested decision making in multiagent settings is modeled using interactive dynamic influence diagrams ( I-DID ). These are graphical models with the benefit that they naturally offer a factored representation of the problem, allowing agents to ascribe dynamic models to others and reason about them. We demonstrate that an implication of bounded, finitely-nested reasoning by a self-interested agent is that we may not obtain optimal team solutions in cooperative settings, if it is part of a team. We address this limitation by including models at level 0 whose solutions involve reinforcement learning. We show how the learning is integrated into planning in the context of I-DID s. This facilitates optimal teammate behavior, and we demonstrate its applicability to ad hoc teamwork on several problem domains and configurations.

ICAPS Conference 2015 Conference Paper

Individual Planning in Agent Populations: Exploiting Anonymity and Frame-Action Hypergraphs

• Ekhlas Sonu
• Yingke Chen
• Prashant Doshi

Interactive partially observable Markov decision processes (I-POMDP) provide a formal framework for planning for a self-interested agent in multiagent settings. An agent operating in a multiagent environment must deliberate about the actions that other agents may take and the effect these actions have on the environment and the rewards it receives. Traditional I-POMDPs model this dependence on the actions of other agents using joint action and model spaces. Therefore, the solution complexity grows exponentially with the number of agents thereby complicating scalability. In this paper, we model and extend anonymity and context-specific independence — problem structures often present in agent populations — for computational gain. We empirically demonstrate the efficiency from exploiting these problem structures by solving a new multiagent problem involving more than 1, 000 agents.

IJCAI Conference 2015 Conference Paper

Learning Behaviors in Agents Systems with Interactive Dynamic Influence Diagrams

• Ross Conroy
• Yifeng Zeng
• Marc Cavazza
• Yingke Chen

Interactive dynamic influence diagrams (I-DIDs) are a well recognized decision model that explicitly considers how multiagent interaction affects individual decision making. To predict behavior of other agents, I-DIDs require models of the other agents to be known ahead of time and manually encoded. This becomes a barrier to I-DID applications in a human-agent interaction setting, such as development of intelligent non-player characters (NPCs) in real-time strategy (RTS) games, where models of other agents or human players are often inaccessible to domain experts. In this paper, we use automatic techniques for learning behavior of other agents from replay data in RTS games. We propose a learning algorithm with improvement over existing work by building a full profile of agent behavior. This is the first time that data-driven learning techniques are embedded into the I-DID decision making framework. We evaluate the performance of our approach on two test cases.

AAMAS Conference 2011 Conference Paper

Approximating Behavioral Equivalence of Models Using Top-K Policy Paths

• Yifeng Zeng
• Yingke Chen
• Prashant Doshi

Decision making and game play in multiagent settings must often contend with behavioral models of other agents in order to predict their actions. One approach that reduces the complexity of the unconstrained model space is to group models that tend to be behaviorally equivalent. In this paper, we seek to further compress the model space by introducing an approximate measure of behavioral equivalence and using it to group models.