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Found 5 papers, 1 papers with code
Bayesian Counterfactual Risk Minimization
We present a Bayesian view of counterfactual risk minimization (CRM) for offline learning from logged bandit feedback.
A PAC-Bayesian Analysis of Randomized Learning with Application to Stochastic Gradient Descent
This inspires an adaptive sampling algorithm for SGD that optimizes the posterior at runtime.
Hinge-loss Markov Random Fields: Convex Inference for Structured Prediction
Graphical models for structured domains are powerful tools, but the computational complexities of combinatorial prediction spaces can force restrictions on models, or require approximate inference in order to be tractable.
Multi-relational Learning Using Weighted Tensor Decomposition with Modular Loss
For the typical cases of real-valued functions and binary relations, we propose several loss functions and derive the associated parameter gradients.
Graph-based Generalization Bounds for Learning Binary Relations
We investigate the generalizability of learned binary relations: functions that map pairs of instances to a logical indicator.
