Learning Graphical Models
9dc5accb1e4f4a9798eae145f2e4869b-Paper-Conference.pdf
Both challenges involve modeling long-term dependencies.
Model Shapley: Equitable Model Valuation with Black-box Access Xinyi Xu, Thanh Lam
ML models call for an equitable model valuation method to price them. In particular, we investigate the black-box access setting which allows querying a model (to observe predictions) without disclosing model-specific information (e.g., architecture and parameters). By exploiting a Dirichlet abstraction of a model's predictions, we propose a novel and equitable model valuation method called
On the Consistency of Maximum Likelihood Estimation of Probabilistic Principal Component Analysis
Probabilistic principal component analysis (PPCA) is currently one of the most used statistical tools to reduce the ambient dimension of the data.
Overleaf Example
Second, we enforce the posterior update model to learn the dynamics of the latent variable.
Efficient and Private Marginal Reconstruction with Local Non-Negativity Brett Mullins 1 Miguel Fuentes
One of the most fundamental problems in differential privacy is answering a workload of linear queries.
A Compositional Atlas for Algebraic Circuits
The key feature of circuits is that they enable one to precisely characterize tractability conditions (structural properties of the circuit) under which a given inference query can be computed exactly and efficiently. One can then enforce these circuit properties when compiling or learning a model to enable tractable inference.
