80 citations · 107 across the 17 of their papers we have counts for
19 papers
Drafting Agents, Not Just Tokens: Speculative Recursive Self-Improvement for Agentic Systems
Xin Xu, Ke Cheng, Hao Wang +11
Improving an agent through successive revisions requires balancing exploration and evaluation under a limited budget. A narrow search can miss useful sequences of edits, while full…
Rubrics on Trial: Evolving Rubrics from a Single Query via Synthetic Pairwise Evidence
Haocheng Yang, Licheng Pan, Xiaoxi Li +8
Rubric evolution offers a promising approach to improving the quality of rubrics generated by large language models (LLMs). Central to this process is rubric comparison, which iden…
Unbiased Reward Modeling from Implicit Feedback for LLM Alignment
Hao Wang, Haocheng Yang, Licheng Pan +7
Despite the success of reinforcement learning from human feedback (RLHF), existing reward modeling methods largely rely on explicit feedback, which is costly to collect and difficu…
Deep Time-Series Forecasting in 10 Years: A Survey
Hao Wang, Licheng Pan, Qingsong Wen +12
Autocorrelation is a common property of time-series, where each observation is dependent on its predecessors. In deep time-series forecasting, it raises two central challenges: (1)…
CausalRM: Causal-Theoretic Reward Modeling for RLHF from Observational User Feedbacks
Hao Wang, Licheng Pan, Zhichao Chen +7
Despite the success of reinforcement learning from human feedback (RLHF) in aligning language models, current reward modeling heavily relies on experimental feedback data collected…
Analyzing and Improving Diffusion Models for Time-Series Data Imputation: A Proximal Recursion Perspective
Zhichao Chen, Hao Wang, Fangyikang Wang +5
Diffusion models (DMs) have shown promise for Time-Series Data Imputation (TSDI); however, their performance remains inconsistent in complex scenarios. We attribute this to two pri…
Rethinking the Flow-Based Gradual Domain Adaptation: A Semi-Dual Optimal Transport Perspective
Zhichao Chen, Zhan Zhuang, Yunfei Teng +6
Gradual domain adaptation (GDA) aims to mitigate domain shift by progressively adapting models from the source domain to the target domain via intermediate domains. However, real i…
Deep Time-series Forecasting Needs Kernelized Moment Balancing
Licheng Pan, Hao Wang, Haocheng Yang +12
Deep time-series forecasting can be formulated as a distribution balancing problem aimed at aligning the distribution of the forecasts and ground truths. According to Imbens' crite…
Quadratic Direct Forecast for Training Multi-Step Time-Series Forecast Models
Hao Wang, Licheng Pan, Yuan Lu +7
The design of learning objectives is central to training time-series forecasting models. Existing learning objectives such as mean squared error mostly treat each future step as an…
DistDF: Time-Series Forecasting Needs Joint-Distribution Wasserstein Alignment
Hao Wang, Licheng Pan, Yuan Lu +7
Training time-series forecasting models requires aligning the conditional distribution of model forecasts with that of the label sequence. The standard direct forecast (DF) approac…
From Text to Talk: Audio-Language Model Needs Non-Autoregressive Joint Training
Tianqiao Liu, Xueyi Li, Hao Wang +4
Recent advances in large language models (LLMs) have attracted significant interest in extending their capabilities to multimodal scenarios, particularly for speech-to-speech conve…
Mixture of Low Rank Adaptation with Partial Parameter Sharing for Time Series Forecasting
Licheng Pan, Zhichao Chen, Haoxuan Li +5
Multi-task forecasting has become the standard approach for time-series forecasting (TSF). However, we show that it suffers from an Expressiveness Bottleneck, where predictions at…
Time-o1: Time-Series Forecasting Needs Transformed Label Alignment
Hao Wang, Licheng Pan, Zhichao Chen +5
Training time-series forecasting models poses unique challenges in loss function design. Most existing approaches adopt temporal mean squared error, but this study reveals two crit…
DeepFilter: A Transformer-style Framework for Accurate and Efficient Process Monitoring
Hao Wang, Zhichao Chen, Licheng Pan +4
The process monitoring task is characterized by stringent demands for accuracy and efficiency. Current transformer-based methods, characterized by self-attention for temporal fusio…
FreDF: Learning to Forecast in the Frequency Domain
Hao Wang, Licheng Pan, Zhichao Chen +6
Time series modeling presents unique challenges due to autocorrelation in both historical data and future sequences. While current research predominantly addresses autocorrelation…
Optimal Transport for Treatment Effect Estimation
Hao Wang, Zhichao Chen, Jiajun Fan +7
Estimating conditional average treatment effect from observational data is highly challenging due to the existence of treatment selection bias. Prevalent methods mitigate this issu…
Monotonic Neural Ordinary Differential Equation: Time-series Forecasting for Cumulative Data
Zhichao Chen, Leilei Ding, Zhixuan Chu +3
Time-Series Forecasting based on Cumulative Data (TSFCD) is a crucial problem in decision-making across various industrial scenarios. However, existing time-series forecasting meth…
TMoE-P: Towards the Pareto Optimum for Multivariate Soft Sensors
Licheng Pan, Hao Wang, Zhichao Chen +2
Multi-variate soft sensor seeks accurate estimation of multiple quality variables using measurable process variables, which have emerged as a key factor in improving the quality of…
ESCM: Entire Space Counterfactual Multi-Task Model for Post-Click Conversion Rate Estimation
Hao Wang, Tai-Wei Chang, Tianqiao Liu +5
Accurate estimation of post-click conversion rate is critical for building recommender systems, which has long been confronted with sample selection bias and data sparsity issues.…