5 papers
Causal Representation Meets Stochastic Modeling under Generic Geometry
Jiaxu Ren, Yixin Wang, Biwei Huang
Learning meaningful causal representations from observations has emerged as a crucial task for facilitating machine learning applications and driving scientific discoveries in fiel…
Counterfactual Voting Adjustment for Quality Assessment and Fairer Voting in Online Platforms with Helpfulness Evaluation
Chang Liu, Yixin Wang, Moontae Lee
Efficient access to high-quality information is vital for online platforms. To promote more useful information, users not only create new content but also evaluate existing content…
Let Me Grok for You: Accelerating Grokking via Embedding Transfer from a Weaker Model
Zhiwei Xu, Zhiyu Ni, Yixin Wang +1
''Grokking'' is a phenomenon where a neural network first memorizes training data and generalizes poorly, but then suddenly transitions to near-perfect generalization after prolong…
Explanation Design in Strategic Learning: Sufficient Explanations that Induce Non-harmful Responses
Kiet Q. H. Vo, Siu Lun Chau, Masahiro Kato +2
We study explanation design in algorithmic decision making with strategic agents, individuals who may modify their inputs in response to explanations of a decision maker's (DM's) p…
Posterior Mean Matching: Generative Modeling through Online Bayesian Inference
Sebastian Salazar, Michal Kucer, Yixin Wang +2
This paper introduces posterior mean matching (PMM), a new method for generative modeling that is grounded in Bayesian inference. PMM uses conjugate pairs of distributions to model…