Bayesian Learning
Bias Detection via Signaling
We introduce and study the problem of detecting whether an agent is updating their prior beliefs given new evidence in an optimal way that is Bayesian, or whether they are biased towards their own prior. In our model, biased agents form posterior beliefs that are a convex combination of their prior and the Bayesian posterior, where the more biased an agent is, the closer their posterior is to the prior. Since we often cannot observe the agent's beliefs directly, we take an approach inspired by information design . Specifically, we measure an agent's bias by designing a signaling scheme and observing the actions the agent takes in response to different signals, assuming that the agent maximizes their own expected utility. Our goal is to detect bias with a minimum number of signals. Our main results include a characterization of scenarios where a single signal suffices and a computationally efficient algorithm to compute optimal signaling schemes.
FuseMoE: Mixture-of-Experts Transformers for Fleximodal Fusion
As machine learning models in critical fields increasingly grapple with multi-modal data, they face the dual challenges of handling a wide array of modalities, often incomplete due to missing elements, and the temporal irregularity and sparsity of collected samples. Successfully leveraging this complex data, while overcoming the scarcity of high-quality training samples, is key to improving these models' predictive performance. We introduce "FuseMoE", a mixture-of-experts framework incorporated with an innovative gating function. Designed to integrate a diverse number of modalities, FuseMoE is effective in managing scenarios with missing modalities and irregularly sampled data trajectories. Theoretically, our unique gating function contributes to enhanced convergence rates, leading to better performance in multiple downstream tasks. The practical utility of FuseMoE in the real world is validated by a diverse set of challenging prediction tasks.
Aligning Language Models with Human Preferences via a Bayesian Approach Jiashuo W ANG 1, Haozhao W ANG
"Rule-of-Thumb" generation, show that our method consistently exceeds previous
BMRS: Bayesian Model Reduction for Structured Pruning
In this work, we address this challenge by proposing BMRS: B ayesian M odel R eduction for S tructured pruning.
Unscrambling disease progression at scale: fast inference of event permutations with optimal transport
Disease progression models infer group-level temporal trajectories of change in patients' features as a chronic degenerative condition plays out.
Robust Gaussian Processes via Relevance Pursuit
We compare the model's performance relative to other approaches on diverse regression and Bayesian optimization tasks, including the challenging but common