Abstract: In-context learning (ICL) empowers large pre-trained language models (PLMs) to predict outcomes for unseen inputs without parameter updates. However, the efficacy of ICL heavily relies on ...

You train the model once, but you run it every day. Making sure your model has business context and guardrails to guarantee reliability is more valuable than fussing over LLMs. We’re years into the ...

Over the past several years, the lion’s share of artificial intelligence (AI) investment has poured into training infrastructure—massive clusters designed to crunch through oceans of data, where speed ...

1 Department of Orthopedics, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China 2 Department of Pharmacy, the Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, ...

Please join the JHU CFAR Biostatistics and Epidemiology Methodology (BEM) Core on Thursday, September 4, 2025, from 2-3 pm ET for a session covering the fundamentals of causal inference. If you have ...

In forecasting economic time series, statistical models often need to be complemented with a process to impose various constraints in a smooth manner. Systematically imposing constraints and retaining ...

Large Language Models (LLMs) have recently been used as experts to infer causal graphs, often by repeatedly applying a pairwise prompt that asks about the causal relationship of each variable pair.

What is this book about? Causal methods present unique challenges compared to traditional machine learning and statistics. Learning causality can be challenging, but it offers distinct advantages that ...

Many theories and tools abound to aid leaders in decision-making. This is because we often find ourselves caught between two perceived poles: following gut instincts or adopting a data-driven approach ...

At the core of causal inference lies the challenge of determining reliable causal graphs solely based on observational data. Since the well-known backdoor criterion depends on the graph, any errors in ...