No-regret Algorithms for Capturing Events in Poisson Point Processes
Authors: Mojmir Mutny, Andreas Krause
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the practicality of the method on problems from crime modeling, revenue maximization as well as environmental monitoring. 7. Experiments and Applications In our experiments, we investigate both feedback models as well as different cost models: uniform cost w(A) = |A|, concave cost w(A) = |A| + 0.01, and one-off cost. |
| Researcher Affiliation | Academia | Mojmír Mutný 1 Andreas Krause 1 1Department of Computer Science, ETH Zurich, Zurich, Switzerland. Correspondence to: Mojmír Mutný <mojmir.mutny@inf.ethz.ch>. |
| Pseudocode | Yes | Algorithm 1 CAPTURE-UCB Require: cost function wt, partition (schedule) B, t = 1 1: while t T do 2: Estimate ˆλ as in (3) 3: At = arg max A A ucbt(A) wt(A) as in (10) 4: Sense At for time 5: Receive: ( n(B At) for B B if count-record n(At) if histogram 6: t = t + 1. 7: end while |
| Open Source Code | No | The paper does not explicitly provide access to source code for the methodology described. |
| Open Datasets | Yes | We use a crime dataset released by the San Francisco police department to estimate the intensity of burglary occurrences over space (see Fig. in Appendix D). We use two datasets from Baddeley et al. (2015), one containing locations of an African tree shrub Beilschmiedia, and one containing Gorilla nesting locations in a Cameroon rain forest. |
| Dataset Splits | No | The paper does not provide specific dataset split information (e.g., exact percentages or sample counts for training, validation, and test sets) for the experiments conducted. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies, such as library names with version numbers, used to replicate the experiments. |
| Experiment Setup | Yes | In our experiments, we investigate both feedback models as well as different cost models: uniform cost w(A) = |A|, concave cost w(A) = |A| + 0.01, and one-off cost. Confidence parameter We relax the theoretical requirements of the confidence sets, and simply use β = 4, as well as tweak the past... The remaining experiments are with tweaking, β = 4 and γ = 1. |