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Found 9 papers, 1 papers with code
Multi-Armed Bandits with Interference
Prior research on experimentation with interference has concentrated on the final output of a policy.
Clustered Switchback Experiments: Near-Optimal Rates Under Spatiotemporal Interference
We consider experimentation in the presence of non-stationarity, inter-unit (spatial) interference, and carry-over effects (temporal interference), where we wish to estimate the global average treatment effect (GATE), the difference between average outcomes having exposed all units at all times to treatment or to control.
Optimal Decision Tree with Noisy Outcomes
In pool-based active learning, the learner is given an unlabeled data set and aims to efficiently learn the unknown hypothesis by querying the labels of the data points.
Short-lived High-volume Multi-A(rmed)/B(andits) Testing
We aim to minimize the loss due to not knowing the mean rewards, averaged over instances generated from a given prior distribution.
Markdown Pricing Under an Unknown Parametric Demand Model
Without monotonicity, the minimax regret is $\tilde O(n^{2/3})$ for the Lipschitz demand family and $\tilde O(n^{1/2})$ for a general class of parametric demand models.
From Stream to Pool: Dynamic Pricing Beyond i.i.d. Arrivals
The dynamic pricing problem has been extensively studied under the \textbf{stream} model: A stream of customers arrives sequentially, each with an independently and identically distributed valuation.
Smooth Non-Stationary Bandits
In many applications of online decision making, the environment is non-stationary and it is therefore crucial to use bandit algorithms that handle changes.
Greedy Approximation Algorithms for Active Sequential Hypothesis Testing
In the problem of active sequential hypothesis testing (ASHT), a learner seeks to identify the true hypothesis from among a known set of hypotheses.
Input Aggregated Network for Face Video Representation
To sufficiently discover the useful information contained in face videos, we present a novel network architecture called input aggregated network which is able to learn fixed-length representations for variable-length face videos.
