Learn More
We describe the design, implementation, and evaluation of EMBERS, an automated, 24x7 continuous system for forecasting civil unrest across 10 countries of Latin America using open source indicators such as tweets, news sources, blogs, economic indicators, and other data sources. Unlike retrospective studies, EMBERS has been making forecasts into the future(More)
Dependent rounding is a useful technique for optimization problems with hard budget constraints. This framework naturally leads to negative correlation properties. However, what if an application naturally calls for dependent rounding on the one hand and desires positive correlation on the other? More generally, we develop algorithms that guarantee the(More)
Knapsack median is a generalization of the classic k-median problem in which we replace the cardinality constraint with a knapsack constraint. It is currently known to be 32-approximable. We improve on the best known algorithms in several ways, including adding randomization and applying sparsification as a preprocessing step. The latter improvement(More)
In this paper, we represent an empirical study of multipass decoding for Vietnamese LVCSR. We report our experiments with N-best, lattice and consensus decoding on the VNBN data. Results from this study indicate that our acoustic model for Vietnamese was precise. The results could be investigated in further steps to improve the performance of our system.(More)
<lb>We consider an issue of much current concern: could fairness, an issue that is already difficult to<lb>guarantee, worsen when algorithms run much of our lives? We consider this in the context of resource-<lb>allocation problems; we show that algorithms can guarantee certain types of fairness in a verifiable way.<lb>Our conceptual contribution is a(More)
In this paper, we study a real-world variant of the Vehicle Routing Problem, arising from the work of distributing products of an industrial corporation. Given information about vehicles, depots, types of products, and the location of customers, we wish to determine the minimum number of vehicles required to pick up and deliver products to customers within(More)
In this paper, we give tight approximation algorithms for the k-center and matroid center problems with outliers. Unfairness arises naturally in this setting: certain clients could always be considered as outliers. To address this issue, we introduce a lottery model in which each client j is allowed to submit a parameter pj ∈ [0, 1] and we look for a random(More)
k-median is a classic optimization problem which has been studied extensively in the last few decades. The current best known approximation ratio for this problem is (2.675 + ). A more generalized version of this problem also considers the capacities of the given facilities. That is, each facility cannot serve more clients than its own capacity.(More)
Dependent rounding is a popular technique in designing approximation algorithms. It allows us to randomly round a fractional solution x to an integral vector X such that E[X] = x, all Xi’s are (“cylindrically”) negatively correlated, and the cardinality constraint ∑ iXi = ∑ i xi holds with probability one. One can also essentially replace the cardinality(More)