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Found 9 papers, 2 papers with code
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.
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.
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.
Bayesian Counterfactual Risk Minimization
We present a Bayesian view of counterfactual risk minimization (CRM) for offline learning from logged bandit feedback.
Boosted Off-Policy Learning
We propose the first boosting algorithm for off-policy learning from logged bandit feedback.
Off-policy evaluation for learning-to-rank via interpolating the item-position model and the position-based model
A critical need for industrial recommender systems is the ability to evaluate recommendation policies offline, before deploying them to production.
Practical Bandits: An Industry Perspective
The research literature on these topics is broad and vast, but this can overwhelm practitioners, whose primary aim is to solve practical problems, and therefore need to decide on a specific instantiation or approach for each project.
Offline Recommender System Evaluation under Unobserved Confounding
Because the data collection policy is typically under the practitioner's control, the unconfoundedness assumption is often left implicit, and its violations are rarely dealt with in the existing literature.
