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Found 16 papers, 2 papers with code
Identifying the Reward Function by Anchor Actions
Moreover, the method allows for the state transitions to be stochastic.
Counterfactual Evaluation of Ads Ranking Models through Domain Adaptation
We propose a domain-adapted reward model that works alongside an Offline A/B testing system for evaluating ranking models.
Best of Three Worlds: Adaptive Experimentation for Digital Marketing in Practice
Adaptive experimental design (AED) methods are increasingly being used in industry as a tool to boost testing throughput or reduce experimentation cost relative to traditional A/B/N testing methods.
On Neural Networks as Infinite Tree-Structured Probabilistic Graphical Models
Deep neural networks (DNNs) lack the precise semantics and definitive probabilistic interpretation of probabilistic graphical models (PGMs).
A Data-Driven State Aggregation Approach for Dynamic Discrete Choice Models
We use these estimated Q-functions, along with a clustering algorithm, to select a subset of states that are the most pivotal for driving changes in Q-functions.
Neural Insights for Digital Marketing Content Design
In digital marketing, experimenting with new website content is one of the key levers to improve customer engagement.
Adaptive Experimental Design and Counterfactual Inference
Adaptive experimental design methods are increasingly being used in industry as a tool to boost testing throughput or reduce experimentation cost relative to traditional A/B/N testing methods.
Instance-optimal PAC Algorithms for Contextual Bandits
In the stochastic contextual bandit setting, regret-minimizing algorithms have been extensively researched, but their instance-minimizing best-arm identification counterparts remain seldom studied.
Improved Confidence Bounds for the Linear Logistic Model and Applications to Linear Bandits
Specifically, our confidence bound avoids a direct dependence on $1/\kappa$, where $\kappa$ is the minimal variance over all arms' reward distributions.
Deep PQR: Solving Inverse Reinforcement Learning using Anchor Actions
We name our method PQR, as it sequentially estimates the Policy, the $Q$-function, and the Reward function by deep learning.
Bayesian Meta-Prior Learning Using Empirical Bayes
Our Empirical Bayes method clamps features in each group together and uses the deployed model's observed data to empirically compute a hierarchical prior in hindsight.
Seeker: Real-Time Interactive Search
On the other hand, users often have a mental picture of the desired item and are able to answer ordinal questions of the form: "Is this item similar to what you have in mind?"
An Efficient Bandit Algorithm for Realtime Multivariate Optimization
We further apply our algorithm to optimize a message that promotes adoption of an Amazon service.
Contextual Multi-Armed Bandits for Causal Marketing
Following uplift modeling results, we optimize over the incremental business metric.
Adaptive, Personalized Diversity for Visual Discovery
Search queries are appropriate when users have explicit intent, but they perform poorly when the intent is difficult to express or if the user is simply looking to be inspired.
An Inductive Logic Programming Approach to Validate Hexose Binding Biochemical Knowledge
Hexoses are simple sugars that play a key role in many cellular pathways, and in the regulation of development and disease mechanisms.
