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Tian Tian

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11 papers

1 author row

AAAI Conference 2026 Conference Paper

GARNET: GoT-Based Alert Reduction and Narrative Event Tracing

Yiru Gong
Song Liu
Changzhi Zhao
Junrong Liu
Tian Tian
Xiaobo Yang
Bo Jiang
Zhigang Lu

Alerts generated by Security Operations Centers (SOCs) are often numerous and scattered, requiring significant effort from security analysts to manage, which severely slows response times. While recent alert correlation graph methods can effectively reduce alert volume, these graphs are often too complex for analysts to understand. As a result, analysts are increasingly seeking ways to automatically correlate alerts and generate concise, human-readable attack path summaries. Recently, Large Language Models (LLMs) have demonstrated superior performance due to their advanced capabilities in knowledge reserve and reasoning. In this work, we propose GARNET, a framework that uses LLMs for reasoning on alert correlation graphs. GARNET addresses three key technical challenges: 1) modality alignment between alert graphs and logs; 2) semantic alignment between alert graphs and logs; 3) enabling LLMs reasoning along graph paths. Specifically, we first project the embeddings of the graph and logs into the same vector space using contrastive learning. Then, we design self-supervised graph-log instructions to bridge the semantic gap between the graph and logs by training a novel LLM. Finally, GARNET uses a novel Graph-of-Thought (GoT)-based interaction reasoning approach to guide LLM reasoning along graph paths, ultimately generating structured, concise, and human-readable attack path summaries. Experimental results across six attack scenarios show that GARNET reduces false positives by an average of 80%, lowering the false positive rate to below 0.0037. It outperforms the latest approaches and provides more explainable attribution.

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AAAI Conference 2026 Conference Paper

Modeling Uncertainty Trends for Timely Retrieval in Dynamic RAG

Bo Li
Tian Tian
Zhenghua Xu
Hao Cheng
Shikun Zhang
Wei Ye

Dynamic retrieval-augmented generation (RAG) allows large language models (LLMs) to fetch external knowledge on demand, offering greater adaptability than static RAG. A central challenge in this setting lies in determining the optimal timing for retrieval. Existing methods often trigger retrieval based on low token-level confidence, which may lead to delayed intervention after errors have already propagated. We introduce Entropy-Trend Constraint (ETC), a training-free method that determines optimal retrieval timing by modeling the dynamics of token-level uncertainty. Specifically, ETC utilizes first- and second-order differences of the entropy sequence to detect emerging uncertainty trends, enabling earlier and more precise retrieval. Experiments on six QA benchmarks with three LLM backbones demonstrate that ETC consistently outperforms strong baselines while reducing retrieval frequency. ETC is particularly effective in domain-specific scenarios, exhibiting robust generalization capabilities. Ablation studies and qualitative analyses further confirm that trend-aware uncertainty modeling yields more effective retrieval timing. The method is plug-and-play, model-agnostic, and readily integrable into existing decoding pipelines. Implementation code is included in the supplementary materials.

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NeurIPS Conference 2024 Conference Paper

Confident Natural Policy Gradient for Local Planning in $q_\pi$-realizable Constrained MDPs

Tian Tian
Lin F. Yang
Csaba Szepesvári

The constrained Markov decision process (CMDP) framework emerges as an important reinforcement learning approach for imposing safety or other critical objectives while maximizing cumulative reward. However, the current understanding of how to learn efficiently in a CMDP environment with a potentially infinite number of states remains under investigation, particularly when function approximation is applied to the value functions. In this paper, we address the learning problem given linear function approximation with $q_{\pi}$-realizability, where the value functions of all policies are linearly representable with a known feature map, a setting known to be more general and challenging than other linear settings. Utilizing a local-access model, we propose a novel primal-dual algorithm that, after $\tilde{O}(\text{poly}(d) \epsilon^{-3})$ iterations, outputs with high probability a policy that strictly satisfies the constraints while nearly optimizing the value with respect to a reward function. Here, $d$ is the feature dimension and $\epsilon > 0$ is a given error. The algorithm relies on a carefully crafted off-policy evaluation procedure to evaluate the policy using historical data, which informs policy updates through policy gradients and conserves samples. To our knowledge, this is the first result achieving polynomial sample complexity for CMDP in the $q_{\pi}$-realizable setting.

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NeurIPS Conference 2022 Conference Paper

Doubly-Asynchronous Value Iteration: Making Value Iteration Asynchronous in Actions

Tian Tian
Kenny Young
Richard S. Sutton

Value iteration (VI) is a foundational dynamic programming method, important for learning and planning in optimal control and reinforcement learning. VI proceeds in batches, where the update to the value of each state must be completed before the next batch of updates can begin. Completing a single batch is prohibitively expensive if the state space is large, rendering VI impractical for many applications. Asynchronous VI helps to address the large state space problem by updating one state at a time, in-place and in an arbitrary order. However, Asynchronous VI still requires a maximization over the entire action space, making it impractical for domains with large action space. To address this issue, we propose doubly-asynchronous value iteration (DAVI), a new algorithm that generalizes the idea of asynchrony from states to states and actions. More concretely, DAVI maximizes over a sampled subset of actions that can be of any user-defined size. This simple approach of using sampling to reduce computation maintains similarly appealing theoretical properties to VI without the need to wait for a full sweep through the entire action space in each update. In this paper, we show DAVI converges to the optimal value function with probability one, converges at a near-geometric rate with probability $1-\delta$, and returns a near-optimal policy in computation time that nearly matches a previously established bound for VI. We also empirically demonstrate DAVI's effectiveness in several experiments.

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IJCAI Conference 2020 Conference Paper

Rumor Detection on Social Media with Graph Structured Adversarial Learning

Xiaoyu Yang
Yuefei Lyu
Tian Tian
Yifei Liu
Yudong Liu
Xi Zhang

The wide spread of rumors on social media has caused tremendous effects in both the online and offline world. In addition to text information, recent detection methods began to exploit the graph structure in the propagation network. However, without a rigorous design, rumors may evade such graph models using various camouflage strategies by perturbing the structured data. Our focus in this work is to develop a robust graph-based detector to identify rumors on social media from an adversarial perspective. We first build a heterogeneous information network to model the rich information among users, posts, and user comments for detection. We then propose a graph adversarial learning framework, where the attacker tries to dynamically add intentional perturbations on the graph structure to fool the detector, while the detector would learn more distinctive structure features to resist such perturbations. In this way, our model would be enhanced in both robustness and generalization. Experiments on real-world datasets demonstrate that our model achieves better results than the state-of-the-art methods.

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RLDM Conference 2019 Conference Abstract

MinAtar: An Atari-inspired Testbed for More Efficient Reinforcement Learn- ing Experiments

Kenny Young
Tian Tian

The Arcade Learning Environment (ALE) is a popular platform for evaluating reinforcement learning agents. Much of the appeal comes from the fact that Atari games are varied, showcase aspects of competency we expect from an intelligent agent, and are not biased towards any particular solution approach. The challenge of the ALE includes 1) the representation learning problem of extracting pertinent information from the raw pixels, and 2) the behavioural learning problem of leveraging complex, delayed associations between actions and rewards. In many cases, the research questions we are interested in pertain more to the latter, but the representation learning problem adds significant computational expense. In response, we introduce MinAtar, short for miniature Atari, a new evaluation platform that captures the general mechanics of specific Atari games, while simplifying certain aspects. In particular, we reduce the representational complexity to focus more on the behavioural challenges. MinAtar consists of analogues to five Atari games which play out on a 10x10 grid. MinAtar provides the agent with a 10x10xn state representation. The n channels correspond to game-specific objects, such as ball, paddle and brick in the game Breakout. While significantly simplified, these domains are still rich enough to allow for interesting behaviours, similar to those observed in the ALE. To demonstrate the challenges posed by these domains, we evaluated a smaller version of the DQN architecture. We also tried variants of DQN without experience replay, and without a target network, to assess the impact of those two prominent components in the MinAtar environments. In addition, we evaluated a simpler agent that used actor-critic with eligibility traces, online updating, and no experience replay. We hope that by introducing a set of simplified, Atari-like games we can allow researchers to more efficiently investigate the unique behavioural challenges provided by the ALE.

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AAAI Conference 2018 Conference Paper

Selective Verification Strategy for Learning From Crowds

Tian Tian
Yichi Zhou
Jun Zhu

To deal with the low qualities of web workers in crowdsourcing, many unsupervised label aggregation methods have been investigated but most of them provide inconsistent performance. In this paper, we explore the learning from crowds with selective veriﬁcation problem. In addition to the noisy responses from the crowds, it also collects the ground truths for a well-chosen subset of tasks as the reference, then aggregates the redundant responses based on the patterns provided by both the supervised and unsupervised signal. To improve the labeling efﬁciency, we propose the EBM selecting strategy for choosing the veriﬁcation subset, which is based on the loss error minimization. Speciﬁcally, we ﬁrst establish the expected loss error given the semi-supervised learning estimate, then ﬁnd the subset that minimizes this selecting criterion. We do extensive empirical comparisons on both synthetic and real-world datasets to show the beneﬁts of this new learning setting as well as our proposal.

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NeurIPS Conference 2018 Conference Paper

Semi-crowdsourced Clustering with Deep Generative Models

Yucen Luo
Tian Tian
Jiaxin Shi
Jun Zhu
Bo Zhang

We consider the semi-supervised clustering problem where crowdsourcing provides noisy information about the pairwise comparisons on a small subset of data, i. e. , whether a sample pair is in the same cluster. We propose a new approach that includes a deep generative model (DGM) to characterize low-level features of the data, and a statistical relational model for noisy pairwise annotations on its subset. The two parts share the latent variables. To make the model automatically trade-off between its complexity and fitting data, we also develop its fully Bayesian variant. The challenge of inference is addressed by fast (natural-gradient) stochastic variational inference algorithms, where we effectively combine variational message passing for the relational part and amortized learning of the DGM under a unified framework. Empirical results on synthetic and real-world datasets show that our model outperforms previous crowdsourced clustering methods.

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AAAI Conference 2017 Conference Paper

Learning Attributes from the Crowdsourced Relative Labels

Tian Tian
Ning Chen
Jun Zhu

Finding semantic attributes to describe related concepts is typically a hard problem. The commonly used attributes in most ﬁelds are designed by domain experts, which is expensive and time-consuming. In this paper we propose an efﬁcient method to learn human comprehensible attributes with crowdsourcing. We ﬁrst design an analogical interface to collect relative labels from the crowds. Then we propose a hierarchical Bayesian model, as well as an efﬁcient initialization strategy, to aggregate labels and extract concise attributes. Our experimental results demonstrate promise on discovering diverse and convincing attributes, which signiﬁcantly improve the performance of the challenging zero-shot learning tasks.

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AAAI Conference 2016 Conference Paper

Jointly Modeling Topics and Intents with Global Order Structure

Bei Chen
Jun Zhu
Nan Yang
Tian Tian
Ming Zhou
Bo Zhang

Modeling document structure is of great importance for discourse analysis and related applications. The goal of this research is to capture the document intent structure by modeling documents as a mixture of topic words and rhetorical words. While the topics are relatively unchanged through one document, the rhetorical functions of sentences usually change following certain orders in discourse. We propose GMM-LDA, a topic modeling based Bayesian unsupervised model, to analyze the document intent structure cooperated with order information. Our model is ﬂexible that has the ability to combine the annotations and do supervised learning. Additionally, entropic regularization can be introduced to model the signiﬁcant divergence between topics and intents. We perform experiments in both unsupervised and supervised settings, results show the superiority of our model over several state-of-the-art baselines.

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NeurIPS Conference 2015 Conference Paper

Max-Margin Majority Voting for Learning from Crowds

Tian Tian
Jun Zhu

Learning-from-crowds aims to design proper aggregation strategies to infer the unknown true labels from the noisy labels provided by ordinary web workers. This paper presents max-margin majority voting (M^3V) to improve the discriminative ability of majority voting and further presents a Bayesian generalization to incorporate the flexibility of generative methods on modeling noisy observations with worker confusion matrices. We formulate the joint learning as a regularized Bayesian inference problem, where the posterior regularization is derived by maximizing the margin between the aggregated score of a potential true label and that of any alternative label. Our Bayesian model naturally covers the Dawid-Skene estimator and M^3V. Empirical results demonstrate that our methods are competitive, often achieving better results than state-of-the-art estimators.

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