16 citations · 22 across the 16 of their papers we have counts for
19 papers
From Observation to Intervention: Distilling Self-Generated In-Context Demonstrations via Executable Causal Intervention
Minming Ye, Min Liu, Haoang Chi +2
Self-generated In-Context Learning (ICL) enables LLMs to synthesize their own few-shot demonstrations, often outperforming manual prompts. However, existing methods filter demonstr…
Autoregressive Decoding as a Martingale: Characterizing the Stochastic Dynamics of LLM Generation
Xiahui Zhu, Zhenyi Qi, Wanwei Liu +4
Large language models (LLMs) have achieved remarkable success across a wide range of language tasks. At their core, autoregressive LLMs are conditional probability models that lear…
You Only Edit Once: Incentivizing In-Context Capability of LLMs via Local Demonstration Refinement
Jiarong Wen, Qi Wang, Yun Qu +8
In-context learning (ICL) is crucial for boosting the inference performance of large language models (LLMs). However, the effectiveness of ICL in LLMs is greatly influenced by the…
Gene Ontology-Guided Hierarchical Spatial Gene Expression Prediction from Histopathology Images
Zhiwen Xu, Xiaoming Yan, Chengkun Wu +3
Predicting spatial gene expression from histopathology images enables large-scale transcriptomic profiling without the cost of direct measurement. Existing methods decode the targe…
LimICE: Integrating LLM into ICE Framework for Efficient Loop Invariant Inference
Kai Fan, ShiWen Yu, GuangSheng Fan +3
Loop invariant synthesis is a fundamental problem in program verification, yet the inherent undecidability makes it highly challenging. Recent studies have increasingly employed va…
SpaCellAgent: A Self-Evolving LLM-Based Multi-Agent Framework for Trajectory Analysis
Songhan Wang, Haoang Chi, He Li +6
Spatial and Single-cell transcriptomics are transformative in deciphering cellular dynamics. As the fundamental paradigm for reconstructing cell developmental paths, trajectory inf…
Denoised Conformal Alignment for Reliable Selection of Conditional Average Treatment Effect Predictions
Xinyun Lu, Haoang Chi, Zhiheng Zhang
In selective deployment, practitioners act only on a model-chosen subset of individuals based on predicted conditional average treatment effects, but marginal conformal guarantees…
MLUBench: A Benchmark for Lifelong Unlearning Evaluation in MLLMs
He Li, Haoang Chi, Qizhou Wang +6
Multimodal large language models (MLLMs) are trained on massive multimodal data, making data unlearning increasingly important as data owners may request the removal of specific co…
Causal Representation Learning with Optimal Compression under Complex Treatments
Wanting Liang, Haoang Chi, Zhiheng Zhang
Estimating Individual Treatment Effects (ITE) in multi-treatment scenarios faces two critical challenges: the Hyperparameter Selection Dilemma for balancing weights and the Curse o…
Shapley-Guided Neural Repair Approach via Derivative-Free Optimization
Xinyu Sun, Wanwei Liu, Haoang Chi +7
DNNs are susceptible to defects like backdoors, adversarial attacks, and unfairness, undermining their reliability. Existing approaches mainly involve retraining, optimization, con…
Individualized Causal Effects under Network Interference with Combinatorial Treatments
Yunping Lu, Haoang Chi, Qirui Hu +1
Modern causal decision-making increasingly demands individualized treatment-effect estimation in networks where interventions are high-dimensional, combinatorial vectors. While net…
Breaking the Gradient Barrier: Unveiling Large Language Models for Strategic Classification
Xinpeng Lv, Yunxin Mao, Haoxuan Li +9
Strategic classification~(SC) explores how individuals or entities modify their features strategically to achieve favorable classification outcomes. However, existing SC methods, w…
Unveiling Causal Reasoning in Large Language Models: Reality or Mirage?
Haoang Chi, He Li, Wenjing Yang +5
Causal reasoning capability is critical in advancing large language models (LLMs) toward strong artificial intelligence. While versatile LLMs appear to have demonstrated capabiliti…
Transformer-Based Spatial-Temporal Counterfactual Outcomes Estimation
He Li, Haoang Chi, Mingyu Liu +3
The real world naturally has dimensions of time and space. Therefore, estimating the counterfactual outcomes with spatial-temporal attributes is a crucial problem. However, previou…
Look Within, Why LLMs Hallucinate: A Causal Perspective
He Li, Haoang Chi, Mingyu Liu +1
The emergence of large language models (LLMs) is a milestone in generative artificial intelligence, achieving significant success in text comprehension and generation tasks. Despit…
Mixture Data for Training Cannot Ensure Out-of-distribution Generalization
Songming Zhang, Yuxiao Luo, Qizhou Wang +4
Deep neural networks often face generalization problems to handle out-of-distribution (OOD) data, and there remains a notable theoretical gap between the contributing factors and t…
Diversity-enhancing Generative Network for Few-shot Hypothesis Adaptation
Ruijiang Dong, Feng Liu, Haoang Chi +5
Generating unlabeled data has been recently shown to help address the few-shot hypothesis adaptation (FHA) problem, where we aim to train a classifier for the target domain with a…
TOHAN: A One-step Approach towards Few-shot Hypothesis Adaptation
Haoang Chi, Feng Liu, Wenjing Yang +5
In few-shot domain adaptation (FDA), classifiers for the target domain are trained with accessible labeled data in the source domain (SD) and few labeled data in the target domain…
Meta Discovery: Learning to Discover Novel Classes given Very Limited Data
Haoang Chi, Feng Liu, Bo Han +6
In novel class discovery (NCD), we are given labeled data from seen classes and unlabeled data from unseen classes, and we train clustering models for the unseen classes. However,…