124 citations · 297 across the 22 of their papers we have counts for
22 papers
Gaussian Mean Field Variational Inference can Overestimate Predictive Variance
James Odgers, Ben Riegler, Siddharth Swaroop +1
Mean Field Variational Inference (MFVI) is widely understood to underestimate posterior variance. By analysing conjugate Bayesian Linear Regression (BLR), we show that this charact…
Fast and Slow Variational Continual Learning
Subarnaduti Paul, Yohan Jung, Mohammad Emtiyaz Khan +3
Continual learning remains a major challenge for modern deep networks, partly because commonly used optimizers lack inherent mechanisms for continual adaptation. One such natural m…
Position: agentic AI orchestration should be Bayes-consistent
Theodore Papamarkou, Pierre Alquier, Matthias Bauer +27
LLMs excel at predictive tasks and complex reasoning tasks, but many high-value deployments rely on decisions under uncertainty, for example, which tool to call, which expert to co…
Measuring and mitigating overreliance to build human-compatible AI
Lujain Ibrahim, Katherine M. Collins, Sunnie S. Y. Kim +14
Large language models (LLMs) distinguish themselves from previous technologies by functioning as collaborative ``thought partners,'' capable of engaging more fluidly in natural lan…
Federated ADMM from Bayesian Duality
Thomas Möllenhoff, Siddharth Swaroop, Finale Doshi-Velez +1
We propose a new Bayesian approach to generalize the federated Alternating Direction Method of Multipliers (ADMM). We show that the solutions of variational-Bayesian (VB) objective…
Rethinking LLM Bias Probing Using Lessons from the Social Sciences
Kirsten N. Morehouse, Siddharth Swaroop, Weiwei Pan
The proliferation of LLM bias probes introduces three significant challenges: (1) we lack principled criteria for choosing appropriate probes, (2) we lack a system for reconciling…