56 citations · 127 across the 6 of their papers we have counts for
6 papers
Estimating Model Performance on External Samples from Their Limited Statistical Characteristics
Tal El-Hay, Chen Yanover
Methods that address data shifts usually assume full access to multiple datasets. In the healthcare domain, however, privacy-preserving regulations as well as commercial interests…
Adversarial Balancing for Causal Inference
Michal Ozery-Flato, Pierre Thodoroff, Matan Ninio +2
Biases in observational data of treatments pose a major challenge to estimating expected treatment outcomes in different populations. An important technique that accounts for these…
Incorporating Expressive Graphical Models in Variational Approximations: Chain-Graphs and Hidden Variables
Tal El-Hay, Nir Friedman
Global variational approximation methods in graphical models allow efficient approximate inference of complex posterior distributions by using a simpler model. The choice of the ap…
Continuous Time Markov Networks
Tal El-Hay, Nir Friedman, Daphne Koller +1
A central task in many applications is reasoning about processes that change in a continuous time. The mathematical framework of Continuous Time Markov Processes provides the basic…
Gibbs Sampling in Factorized Continuous-Time Markov Processes
Tal El-Hay, Nir Friedman, Raz Kupferman
A central task in many applications is reasoning about processes that change over continuous time. Continuous-Time Bayesian Networks is a general compact representation language fo…
Mean Field Variational Approximation for Continuous-Time Bayesian Networks
Ido Cohn, Tal El-Hay, Nir Friedman +1
Continuous-time Bayesian networks is a natural structured representation language for multicomponent stochastic processes that evolve continuously over time. Despite the compact re…