Paper tackles online facility location with user requests and provides a quasi-linear time algorithm.
problem Online facility location problem with user requests and site selection costs.
method Design of a novel quasi-linear time algorithm based on the Hedge algorithm.
result Good theoretical guarantees on the performance of the novel algorithm.
Proposes a new method to find overconfident predictions in classifiers.
problem Challenges in assessing classifier accuracy without labeled test data.
method Facility locations utility model and greedy query algorithm.
result Greedy query algorithm finds overconfident unknown unknowns more effectively.
MPNN improves on UniFL approximation with provable guarantees.
problem Uniform Facility Location (UniFL) optimization problem.
method Graph Neural Network (MPNN) incorporating approximation-algorithmic principles.
result Empirically outperforms standard approximation algorithms.
The paper addresses fairness in facility location by considering local population densities.
problem Ensuring fair distribution of facilities based on local population sizes.
method Formulated a fairness concept involving neighborhood radius and applied an approximation algorithm.
result The algorithm guarantees a factor of at most 2 in all metric spaces and outperforms standard clustering algorithms.
New tools analyze data selection methods, reducing information losses.
problem Analyzing and reducing information losses in data selection.
method Information theoretic losses framework applied to Facility Location Selection and Transductive Experimental Design.
result Proves Facility Location Selection and Transductive Experimental Design reduce information losses.
Apricot selects subsets from large data sets efficiently using submodular optimization.
problem Efficiently selecting representative subsets from large data sets.
method Submodular optimization with efficient greedy algorithm.
result Strong theoretical guarantees on the quality of selected subsets.
Two diversity models improve subset selection for image classification tasks.
problem Data scarcity and high costs in human labeling for supervised learning.
method Facility-Location and Disparity-Min models for training data subset selection and active learning.
result Subset selection improves accuracy by 2-3% with less training data.
The facility location problem is widely used for summarizing large datasets and has additional applications in sensor placement, image retrieval, and clustering. One difficulty of this problem is that submodular optimization algorithms require the calculation of pairwise benefits for all items in the dataset. This is i…
Paper develops a gradient-like proposal for discrete distributions without requiring natural differentiability.
problem Lack of natural differentiability in proposal distributions for discrete distributions.
method Locally-balanced proposal combined with Newton's series expansion for efficient exploration.
result Method guarantees convergence rate and outperforms alternatives in various experiments.
Integrates learning and optimization on graphs, improving prediction accuracy.
problem Combining learning and optimization on graphs with partially observed data.
method Proposes a decision-focused learning approach integrating a differentiable proxy for optimization problems.
result ClusterNet system outperforms pure end-to-end and standard approaches.
Unified approach tackles logical constraints in mixed-integer optimization.
problem Logical constraints in mixed-integer optimization problems.
method Express logical constraints non-linearly, reformulate as convex binary optimization, solve using outer-approximation.
result Solves problems faster and at larger scale than existing methods.
The paper introduces submodular information measures for machine learning applications.
problem Generalizing information-theoretic measures to non-random variables.
method Developing combinatorial information measures based on submodular functions.
result Submodular mutual information is submodular in one argument for certain submodular functions.
A new algorithm finds optimal centers for sets in metric spaces.
problem Finding optimal centers for sets in metric spaces.
method Proved existence and computed an O(log2n)-size core-set for sets-k-means problem. result First PTAS for sets-k-means problem with near-linear time complexity. Cloud-native simulator studies global trading latencies.
problem Understanding latencies in planetary-scale automated trading systems.
method Cloud-native, open-source simulator with distributed clients.
result Demonstrates latencies in real-world trading systems.