Amazing talk from @ta_broderick on whether dropping a little data can change conclusions of a statistical analysis. The delivery was a masterclass on patient teaching of new and essential concepts that leaves us all with new insights and lines of research.
@shakir_za
-
Bayesian Causal Discovery: Advanced Machine Learning Research
By
–
Bayesian Causal discovery from @haeggee @arkrause #AISTATS2023
-
Peter Grünwald e-values statistical testing methods AISTATS
By
–
Peter Grünwald still an inspiration . We all need to go an lean about e-values and a better ways of doing statistical testing from this work. #AISTATS2023 https://
safestatistics.com -
Temporal Graph Neural Networks for Irregular Data
By
–
Temporal graph neural nets for irregular data from @joel_oskarsson
-
Positional Encoder for Graph Networks in Geographic Data
By
–
Positional encoder for graph networks for geographic data. @kklmmr
-
AISTATS 2023 Conference: Poster Sessions and Research Presentations
By
–
Warm Velencia evening and poster sessions as always are busy busy busy #AISTATS2023. Sharing some posters in the thread.
-
AI Evaluation Discussion: Technical and Sociotechnical Perspectives
By
–
Doing some final fixes for my talk tomorrow at #AISTATS2023 hoping to have a discussion on all things evaluation, from technical to sociotechnical.
-
Building Research Niches in AI: AISTATS Panel Discussion
By
–
And now at #AISTATS2023 a panel on the important topic of building a niche for yourself in research . With Marc Deisenroth · Jessica Schrouff · Santiago VELASCO-FORERO · Laura Montoya.
-
Causal Effect Estimation with Context and Confounders keynote
By
–
First keynote for #AISTATS2023 @ArthurGretton on Causal Effect Estimation with Context and Confounders.
