6 research outputs found
Sales Channel Optimization via Simulations Based on Observational Data with Delayed Rewards: A Case Study at LinkedIn
Training models on data obtained from randomized experiments is ideal for
making good decisions. However, randomized experiments are often
time-consuming, expensive, risky, infeasible or unethical to perform, leaving
decision makers little choice but to rely on observational data collected under
historical policies when training models. This opens questions regarding not
only which decision-making policies would perform best in practice, but also
regarding the impact of different data collection protocols on the performance
of various policies trained on the data, or the robustness of policy
performance with respect to changes in problem characteristics such as action-
or reward- specific delays in observing outcomes. We aim to answer such
questions for the problem of optimizing sales channel allocations at LinkedIn,
where sales accounts (leads) need to be allocated to one of three channels,
with the goal of maximizing the number of successful conversions over a period
of time. A key problem feature constitutes the presence of stochastic delays in
observing allocation outcomes, whose distribution is both channel- and outcome-
dependent. We built a discrete-time simulation that can handle our problem
features and used it to evaluate: a) a historical rule-based policy; b) a
supervised machine learning policy (XGBoost); and c) multi-armed bandit (MAB)
policies, under different scenarios involving: i) data collection used for
training (observational vs randomized); ii) lead conversion scenarios; iii)
delay distributions. Our simulation results indicate that LinUCB, a simple MAB
policy, consistently outperforms the other policies, achieving a 18-47% lift
relative to a rule-based policyComment: Accepted at REVEAL'22 Workshop (16th ACM Conference on Recommender
Systems - RecSys 2022
An Improved Relaxation for Oracle-Efficient Adversarial Contextual Bandits
We present an oracle-efficient relaxation for the adversarial contextual
bandits problem, where the contexts are sequentially drawn i.i.d from a known
distribution and the cost sequence is chosen by an online adversary. Our
algorithm has a regret bound of
and makes at most calls
per round to an offline optimization oracle, where denotes the number of
actions, denotes the number of rounds and denotes the set of
policies. This is the first result to improve the prior best bound of
as obtained by Syrgkanis et
al. at NeurIPS 2016, and the first to match the original bound of Langford and
Zhang at NeurIPS 2007 which was obtained for the stochastic case.Comment: Appears in NeurIPS 202