Caesar Wu
23 papers 122 citations h-index 4 2016–2026
Strategic Decisions Survey, Taxonomy, and Future Directions from Artificial Intelligence Perspective
Strategic Decision-Making is always challenging because it is inherently uncertain, ambiguous, risky, and complex. It is the art of possibility. We develop a systematic taxonomy of decision-making...
A Unified Hyperparameter Optimization Pipeline for Transformer-Based Time Series Forecasting Models
Transformer-based models for time series forecasting (TSF) have attracted significant attention in recent years due to their effectiveness and versatility. However, these models often require...
Survey and Taxonomy: The Role of Data-Centric AI in Transformer-Based Time Series Forecasting
Alongside the continuous process of improving AI performance through the development of more sophisticated models, researchers have also focused their attention to the emerging concept of...
Trustworthiness of Stochastic Gradient Descent in Distributed Learning
Distributed learning (DL) uses multiple nodes to accelerate training, enabling efficient optimization of large-scale models. Stochastic Gradient Descent (SGD), a key optimization algorithm, plays...
Transformer Multivariate Forecasting: Less is More?
In the domain of multivariate forecasting, transformer models stand out as powerful apparatus, displaying exceptional capabilities in handling messy datasets from real-world contexts. However, the...
Lightweight Trustworthy Distributed Clustering
Ensuring data trustworthiness within individual edge nodes while facilitating collaborative data processing poses a critical challenge in edge computing systems (ECS), particularly in...
Trustworthy AI: Deciding What to Decide
When engaging in strategic decision-making, we are frequently confronted with overwhelming information and data. The situation can be further complicated when certain pieces of evidence contradict...
Survey of Trustworthy AI: A Meta Decision of AI
When making strategic decisions, we are often confronted with overwhelming information to process. The situation can be further complicated when some pieces of evidence are contradicted each other...
Big Data Analytics = Machine Learning + Cloud Computing
Big Data can mean different things to different people. The scale and challenges of Big Data are often described using three attributes, namely Volume, Velocity and Variety (3Vs), which only...
Solving Without Stopping: On-Policy Distillation at Small Scale
On-policy distillation, where a student learns from a stronger teacher's feedback on its own outputs, is a common way to pass reasoning to smaller models. We analyze what it transfers at small...
Unlocking the Critic: Reward-Free Policy Optimization for LLM Post-Training
Recent approaches to reinforcement learning (RL) post-training for large language models increasingly remove the critic to reduce training instability and memory overhead. Even where a critic is...
What Does Post-Training Change in Multilingual Reasoning?
Open-source reasoning models provide unequal access to reasoning capability across languages. When a model can solve a problem but cannot deliver a complete solution in the user's language,...
Beyond Compression: Diagnosing How Post-Training Changes Mathematical Reasoning
Post-training is central to mathematical reasoning in modern large language models (LLMs), but endpoint pass@1 alone underidentifies what has changed. Gains may reflect newly reachable solutions,...