New ‘Test-Time Training’ method lets AI keep learning without exploding inference costs

By allowing models to actively update their weights during inference, Test-Time Training (TTT) creates a "compressed memory" ...
EurekAlert!

Introducing GOBI: A breakthrough computational package for inferring causal interactions in complex systems

In the quest to unravel the underlying mechanisms of natural systems, accurately identifying causal interactions is of paramount importance. Leveraging the advancements in time-series data collection ...
ascopubs.org

Postprediction Inference for Clinical Characteristics Extracted With Machine Learning on Electronic Health Records

Real-world data (RWD) derived from electronic health records (EHRs) are often used to understand population-level relationships between patient characteristics and cancer outcomes. Machine learning ...
Diginomica

How causal analysis and AI intersect - methods of causal inference

Our foray into causal analysis is not yet complete. Until we define the methods of causal inference, we can't get to the deeper insights that causal analysis can provide. This article details many of ...
ascopubs.org

Causal Inference in Oncology Comparative Effectiveness Research Using Observational Data: Are Instrumental Variables Underutilized?

In the article that accompanies this editorial, Lu et al 5 conducted a systematic review on the use of instrumental variable (IV) methods in oncology comparative effectiveness research. The main ...
EurekAlert!

Which tool tells the truth? A head-to-head benchmarking of scRNA-seq CNV methods

Evaluation of scCNV inference methods using a clinical small cell lung cancer (SCLC) dataset. (A) Sensitivity and specificity of the four scCNV inference methods applied to primary and relapse SCLC ...
CU Boulder News & Events

Causal inference under selection on observables in operations management research: Matching methods and synthetic controls

The majority of recent empirical papers in operations management (OM) employ observational data to investigate the causal effects of a treatment, such as program or policy adoption. However, as ...
mccormick.northwestern.edu

MLDS 490: Bayesian Methods for Inference and Decision Making

This course offers a rigorous yet practical exploration of Bayesian reasoning for data-driven inference and decision-making. Students will gain a deep understanding of probabilistic modeling, and ...
CU Boulder News & Events

Causal inference under selection on observables in operations management research: Matching methods and synthetic controls

Yılmaz, Övünç; Son, Yoonseock; Shang, Guangzhi; Arslan, Hayri A. Causal inference under selection on observables in operations management research: Matching methods and synthetic controls. Journal of ...