10 research outputs found
Bayesian Optimization-based Combinatorial Assignment
We study the combinatorial assignment domain, which includes combinatorial
auctions and course allocation. The main challenge in this domain is that the
bundle space grows exponentially in the number of items. To address this,
several papers have recently proposed machine learning-based preference
elicitation algorithms that aim to elicit only the most important information
from agents. However, the main shortcoming of this prior work is that it does
not model a mechanism's uncertainty over values for not yet elicited bundles.
In this paper, we address this shortcoming by presenting a Bayesian
Optimization-based Combinatorial Assignment (BOCA) mechanism. Our key technical
contribution is to integrate a method for capturing model uncertainty into an
iterative combinatorial auction mechanism. Concretely, we design a new method
for estimating an upper uncertainty bound that can be used as an acquisition
function to determine the next query to the agents. This enables the mechanism
to properly explore (and not just exploit) the bundle space during its
preference elicitation phase. We run computational experiments in several
spectrum auction domains to evaluate BOCA's performance. Our results show that
BOCA achieves higher allocative efficiency than state-of-the-art approaches
Curve Your Enthusiasm: Concurvity Regularization in Differentiable Generalized Additive Models
Generalized Additive Models (GAMs) have recently experienced a resurgence in
popularity due to their interpretability, which arises from expressing the
target value as a sum of non-linear transformations of the features. Despite
the current enthusiasm for GAMs, their susceptibility to concurvity - i.e.,
(possibly non-linear) dependencies between the features - has hitherto been
largely overlooked. Here, we demonstrate how concurvity can severly impair the
interpretability of GAMs and propose a remedy: a conceptually simple, yet
effective regularizer which penalizes pairwise correlations of the non-linearly
transformed feature variables. This procedure is applicable to any
differentiable additive model, such as Neural Additive Models or NeuralProphet,
and enhances interpretability by eliminating ambiguities due to self-canceling
feature contributions. We validate the effectiveness of our regularizer in
experiments on synthetic as well as real-world datasets for time-series and
tabular data. Our experiments show that concurvity in GAMs can be reduced
without significantly compromising prediction quality, improving
interpretability and reducing variance in the feature importances
Monotone-Value Neural Networks: Exploiting Preference Monotonicity in Combinatorial Assignment
Many important resource allocation problems involve the combinatorial assignment of items, e.g., auctions or course allocation. Because the bundle space grows exponentially in the number of items, preference elicitation is a key challenge in these domains. Recently, researchers have proposed ML-based mechanisms that outperform traditional mechanisms while reducing preference elicitation costs for agents. However, one major shortcoming of the ML algorithms that were used is their disregard of important prior knowledge about agents' preferences. To address this, we introduce monotone-value neural networks (MVNNs), which are designed to capture combinatorial valuations, while enforcing monotonicity and normality. On a technical level, we prove that our MVNNs are universal in the class of monotone and normalized value functions, and we provide a mixed-integer linear program (MILP) formulation to make solving MVNN-based winner determination problems (WDPs) practically feasible. We evaluate our MVNNs experimentally in spectrum auction domains. Our results show that MVNNs improve the prediction performance, they yield state-of-the-art allocative efficiency in the auction, and they also reduce the run-time of the WDPs. Our code is available on GitHub: https://github.com/marketdesignresearch/MVNN
Monotone-Value Neural Networks: Exploiting Preference Monotonicity in Combinatorial Assignment
Many important resource allocation problems involve the combinatorial assignment of items, e.g., auctions or course allocation. Because the bundle space grows exponentially in the number of items, preference elicitation is a key challenge in these domains. Recently, researchers have proposed ML-based mechanisms that outperform traditional mechanisms while reducing preference elicitation costs for agents. However, one major shortcoming of the ML algorithms that were used is their disregard of important prior knowledge about agents' preferences. To address this, we introduce monotone-value neural networks (MVNNs), which are designed to capture combinatorial valuations, while enforcing monotonicity and normality. On a technical level, we prove that our MVNNs are universal in the class of monotone and normalized value functions, and we provide a mixed-integer linear program (MILP) formulation to make solving MVNN-based winner determination problems (WDPs) practically feasible. We evaluate our MVNNs experimentally in spectrum auction domains. Our results show that MVNNs improve the prediction performance, they yield state-of-the-art allocative efficiency in the auction, and they also reduce the run-time of the WDPs. Our code is available on GitHub: https://github.com/marketdesignresearch/MVNN
A study on the low-altitude clouds over the Southern Ocean using the DARDAR-MASK
International audienceA climatology of the thermodynamic phase of the clouds over the Southern Ocean (40-65S,100-160E) has been constructed with the A-Train merged data product DARDAR-MASK for the four-year period 2006-2009 during Austral winter and summer. Low-elevation clouds with little seasonal cycle dominate this climatology, with the cloud-tops commonly found at heights less than 1km. Such clouds are problematic for the DARDAR-MASK in that the Cloud Profiling Radar (CPR) of CloudSat is unable to distinguish returns from the lowest four bins (heights up to 720 - 960m), and the CALIOP lidar of CALIPSO may suffer from heavy extinction. The CPR is further limited for all of the low-altitude clouds (tops below 3km) as they are predominantly in the temperature range from freezing to -20 C, where understanding the CPR reflectivity becomes difficult due to the unknown thermodynamic phase. These shortcomings are seen to flow through to the merged CloudSat-CALIPSO product. A cloud-top phase climatology comparison has been made between CALIPSO, the DARDAR-MASK and MODIS. All three products highlight the extensive presence of supercooled liquid water over the Southern Ocean, particularly during summer. The DARDAR-MASK recorded substantially more ice at cloud-tops as well as mixed phase in the low-elevation cloud-tops in comparison to CALIPSO and MODIS. Moving beneath the cloud-top, the DARDAR-MASK finds ice to be dominant at heights greater than 1 km, once the lidar signal is attenuated. The limitations demonstrated in this study highlight the enormous challenge that remains in better defining the energy and water budget over the Southern Ocean