The problems studied in the department can be subsumed under the heading of empirical inference, i.e., inference performed on the basis of empirical data. This includes inductive learning (estimation of models such as functional dependencies that generalize to novel data sampled from the same underlying distribution), or the inference of causal structures from statistical data (leading to models that provide insight into the underlying mechanisms, and make predictions about the effect of interventions). Likewise, the type of empirical data can vary, ranging from biomedical measurements to astronomical observations. Our department is conducting theoretical, algorithmic, and experimental studies to advance study of empirical inference.
Empirical Inference Highlights
Cooperate or Collapse: Emergence of Sustainability in a Society of LLM Agents
Causally Testing Gender Bias in LLMs: A Case Study on Occupational Bias
The Odyssey of Commonsense Causality: From Foundational Benchmarks to Cutting-Edge Reasoning
Diffusion-based learning of contact plans for agile locomotion
Do LLMs Think Fast and Slow? A Causal Study on Sentiment Analysis
Implicit Personalization in Language Models: A Systematic Study
Redesigning Information Markets in the Era of Language Models
Competition of Mechanisms: Tracing How Language Models Handle Facts and Counterfactuals
Do Language Models Exhibit the Same Cognitive Biases in Problem Solving as Human Learners?