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pro vyhledávání: '"Lawless, Connor"'
Mixed integer linear programming (MILP) solvers ship with a staggering number of parameters that are challenging to select a priori for all but expert optimization users, but can have an outsized impact on the performance of the MILP solver. Existing
Externí odkaz:
http://arxiv.org/abs/2412.12038
Autor:
Lawless, Connor, Gunluk, Oktay
Clustering is an unsupervised learning task that aims to partition data into a set of clusters. In many applications, these clusters correspond to real-world constructs (e.g., electoral districts, playlists, TV channels) whose benefit can only be att
Externí odkaz:
http://arxiv.org/abs/2409.02963
Autor:
Lawless, Connor, Schoeffer, Jakob, Le, Lindy, Rowan, Kael, Sen, Shilad, Hill, Cristina St., Suh, Jina, Sarrafzadeh, Bahareh
Publikováno v:
ACM Trans. Interact. Intell. Syst., Vol. 14, No. 3, Article 22. 2024
A critical factor in the success of decision support systems is the accurate modeling of user preferences. Psychology research has demonstrated that users often develop their preferences during the elicitation process, highlighting the pivotal role o
Externí odkaz:
http://arxiv.org/abs/2312.06908
Autor:
Lawless, Connor, Gunluk, Oktay
Clustering is an unsupervised learning task that aims to partition data into a set of clusters. In many applications, these clusters correspond to real-world constructs (e.g. electoral districts) whose benefit can only be attained by groups when they
Externí odkaz:
http://arxiv.org/abs/2302.03151
Autor:
Lawless, Connor, Zhou, Angela
In this short technical note we propose a baseline for decision-aware learning for contextual linear optimization, which solves stochastic linear optimization when cost coefficients can be predicted based on context information. We propose a decision
Externí odkaz:
http://arxiv.org/abs/2211.05116
Autor:
Lawless, Connor, Gunluk, Oktay
Clustering is an unsupervised learning problem that aims to partition unlabelled data points into groups with similar features. Traditional clustering algorithms provide limited insight into the groups they find as their main focus is accuracy and no
Externí odkaz:
http://arxiv.org/abs/2210.08798
Clustering is a popular unsupervised learning tool often used to discover groups within a larger population such as customer segments, or patient subtypes. However, despite its use as a tool for subgroup discovery and description - few state-of-the-a
Externí odkaz:
http://arxiv.org/abs/2112.05653
Publikováno v:
Journal of Machine Learning Research 2023 Volume 24, Number 229, Pages 1-50
This paper considers the learning of Boolean rules in disjunctive normal form (DNF, OR-of-ANDs, equivalent to decision rule sets) as an interpretable model for classification. An integer program is formulated to optimally trade classification accurac
Externí odkaz:
http://arxiv.org/abs/2111.08466
Autor:
Lawless, Connor, Gunluk, Oktay
In recent years, machine learning has begun automating decision making in fields as varied as college admissions, credit lending, and criminal sentencing. The socially sensitive nature of some of these applications together with increasing regulatory
Externí odkaz:
http://arxiv.org/abs/2107.01325
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