Real-Time Optimisation for Online Learning in Auctions
Authors: Lorenzo Croissant, Marc Abeille, Clement Calauzenes
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We study performance for stationary bidders in Sec. 4 with 1/t convergence rate to the monopoly price, and for nonstationary bidders in Sec. 5 with O(T) dynamic regret. ... On Fig. 1, we illustrate the effect of this smoothing on p, ΠˆFt, and ΠF. ... Figure 2. Stationary case. Numerical behaviour of V-CONVOGA for different σt on i.i.d samples from a Kumaraswamy (1, 0.4). Top: averaged convergence speeds of instant regret (log-log scale). Bottom: representative reserve price trajectories. ... Figure 3. Non-stationary case. Tracking by CONV-OGA of three Kumaraswamy distributions (with parameters (1, 4), (1, 0.4), and (1, 1) resp.) with different Gaussian kernels and learning rates. |
| Researcher Affiliation | Industry | Lorenzo Croissant 1 Marc Abeille 1 Cl ement Calauz enes 1 1Criteo AI Lab, Paris, France. Correspondence to: Lorenzo Croissant <ld.croissant@criteo.com>. |
| Pseudocode | Yes | Algorithm 1 CONV-OGA input: r0, {γt}t N, k K, C [0, b] for t = 1 to + do observe bt rt proj C (rt 1 + γt pk(rt 1, bt)) ... Algorithm 2 V-CONV-OGA input: r0, {γt}t N, {kt}t N KN, C [0, b] for t = 1 to + do observe bt rt proj C (rt 1 + γt pkt(rt 1, bt)) |
| Open Source Code | No | No explicit statement or link indicating the availability of the authors' source code was found. |
| Open Datasets | No | The paper mentions generating samples from a Kumaraswamy distribution for numerical behavior analysis, but does not refer to a publicly available dataset with concrete access information (link, DOI, citation). |
| Dataset Splits | No | The paper does not explicitly describe training, validation, and test dataset splits with specific percentages or counts. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) used for running experiments were mentioned. |
| Software Dependencies | No | No specific software dependencies with version numbers were provided. |
| Experiment Setup | Yes | Figure 2. Stationary case. Numerical behaviour of V-CONVOGA for different σt on i.i.d samples from a Kumaraswamy (1, 0.4). Top: averaged convergence speeds of instant regret (log-log scale). Bottom: representative reserve price trajectories. ... To optimise the stationary regime we face a bias-variance trade-off. ... For zero-mean Gaussian kernels, we have Cor. 1. If we fix γt 1/t, and let {kt}t N be Gaussian (0, 1/t) kernels in Thm. 2 we have for all t 2 that: E rt r 2 = O t 1/2 . ... Figure 3. Non-stationary case. Tracking by CONV-OGA of three Kumaraswamy distributions (with parameters (1, 4), (1, 0.4), and (1, 1) resp.) with different Gaussian kernels and learning rates. ... Increasing γ shortens it but increases the width of the band of the asymptotic regime as C(γ, k) increases with γ (blue vs. green curves). For a fixed γ, the stationary regime in terms of k exhibits a bias-variance trade-off: K 1R+ 1 corresponds to the bias and k to the variance (see Prop. 3). In the case of a Gaussian kernel, increasing σ reduces variance but increases bias (green vs. red curve). |