25 citations · 147 across the 70 of their papers we have counts for
14 papers · 1 filter
Exploring Transferability of Self-Supervised Learning by Task Conflict Calibration
Huijie Guo, Jingyao Wang, Peizheng Guo +3
In this paper, we explore the transferability of SSL by addressing two central questions: (i) what is the representation transferability of SSL, and (ii) how can we effectively mod…
A Generalized Learning Framework for Self-Supervised Contrastive Learning
Lingyu Si, Jingyao Wang, Wenwen Qiang
Self-supervised contrastive learning (SSCL) has recently demonstrated superiority in multiple downstream tasks. In this paper, we generalize the standard SSCL methods to a Generali…
Group Causal Policy Optimization for Post-Training Large Language Models
Ziyin Gu, Jingyao Wang, Ran Zuo +4
Recent advances in large language models (LLMs) have broadened their applicability across diverse tasks, yet specialized domains still require targeted post training. Among existin…
Causal Reward Adjustment: Mitigating Reward Hacking in External Reasoning via Backdoor Correction
Ruike Song, Zeen Song, Huijie Guo +1
External reasoning systems combine language models with process reward models (PRMs) to select high-quality reasoning paths for complex tasks such as mathematical problem solving.…
COPO: Causal-Oriented Policy Optimization for Hallucinations of MLLMs
Peizheng Guo, Jingyao Wang, Wenwen Qiang +3
Despite Multimodal Large Language Models (MLLMs) having shown impressive capabilities, they may suffer from hallucinations. Empirically, we find that MLLMs attend disproportionatel…
On the Transferability and Discriminability of Repersentation Learning in Unsupervised Domain Adaptation
Wenwen Qiang, Ziyin Gu, Lingyu Si +4
In this paper, we addressed the limitation of relying solely on distribution alignment and source-domain empirical risk minimization in Unsupervised Domain Adaptation (UDA). Our in…
Reward Model Generalization for Compute-Aware Test-Time Reasoning
Zeen Song, Wenwen Qiang, Siyu Zhao +2
External test-time reasoning enhances large language models (LLMs) by decoupling generation and selection. At inference time, the model generates multiple reasoning paths, and an a…
On the Out-of-Distribution Generalization of Self-Supervised Learning
Wenwen Qiang, Jingyao Wang, Zeen Song +2
In this paper, we focus on the out-of-distribution (OOD) generalization of self-supervised learning (SSL). By analyzing the mini-batch construction during the SSL training phase, w…
Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation
Jingyao Wang, Jianqi Zhang, Wenwen Qiang +1
Despite the strength of the Segment Anything Model (SAM), it struggles with generalization issues in open-vocabulary multi-entity segmentation (OVMS). Through empirical and causal…
Rethinking Multi-Modal Learning from Gradient Uncertainty
Peizheng Guo, Jingyao Wang, Wenwen Qiang +3
Multi-Modal Learning (MML) integrates information from diverse modalities to improve predictive accuracy. While existing optimization strategies have made significant strides by mi…
Beyond All-to-All: Causal-Aligned Transformer with Dynamic Structure Learning for Multivariate Time Series Forecasting
Xingyu Zhang, Hanyun Du, Zeen Song +3
Most existing multivariate time series forecasting methods adopt an all-to-all paradigm that feeds all variable histories into a unified model to predict their future values withou…
Learning to Think: Information-Theoretic Reinforcement Fine-Tuning for LLMs
Jingyao Wang, Wenwen Qiang, Zeen Song +2
Large language models (LLMs) excel at complex tasks thanks to advances in their reasoning abilities. However, existing methods overlook the trade-off between reasoning effectivenes…
Enhancing Human Motion Prediction via Multi-range Decoupling Decoding with Gating-adjusting Aggregation
Jiexin Wang, Wenwen Qiang, Zhao Yang +1
Expressive representation of pose sequences is crucial for accurate motion modeling in human motion prediction (HMP). While recent deep learning-based methods have shown promise in…
Fewer yet critical: Reducing Redundant Token Dependencies for Transformer-based Time Series Forecasting
Jianqi Zhang, Yuchan Liu, Zeen Song +2
Time series forecasting (TSF) is important in real-world applications. Recently, Transformer-based methods have achieved strong performance by modeling token dependencies through a…