Search Results for author:
Found 23 papers, 8 papers with code
Backprop with Approximate Activations for Memory-efficient Network Training
Training convolutional neural network models is memory intensive since back-propagation requires storing activations of all intermediate layers.
Scalable K-Means++
The recently proposed k-means++ initialization algorithm achieves this, obtaining an initial set of centers that is provably close to the optimum solution.
Databases
Cost Effective Active Search
We study a special paradigm of active learning, called cost effective active search, where the goal is to find a given number of positive points from a large unlabeled pool with minimum labeling cost.
An Objective for Hierarchical Clustering in Euclidean Space and its Connection to Bisecting K-means
The paper builds a theoretical connection between this objective and the bisecting k-means algorithm.
Algorithms with Prediction Portfolios
For each of these problems we introduce new algorithms that take advantage of multiple predictors, and prove bounds on the resulting performance.
Bargaining for Revenue Shares on Tree Trading Networks
We study trade networks with a tree structure, where a seller with a single indivisible good is connected to buyers, each with some value for the good, via a unique path of intermediaries.
Pre-Synaptic Pool Modification (PSPM): A Supervised Learning Procedure for Spiking Neural Networks
Learning synaptic weights of spiking neural network (SNN) models that can reproduce target spike trains from provided neural firing data is a central problem in computational neuroscience and spike-based computing.
Efficient nonmyopic active search with applications in drug and materials discovery
We also study the batch setting for the first time, where a batch of $b>1$ points can be queried at each iteration.
Efficient nonmyopic batch active search
A critical target scenario is high-throughput screening for scientific discovery, such as drug or materials discovery.
Approximation Bounds for Hierarchical Clustering: Average Linkage, Bisecting K-means, and Local Search
Hierarchical clustering is a data analysis method that has been used for decades.
Fast Distributed k-Center Clustering with Outliers on Massive Data
Clustering large data is a fundamental problem with a vast number of applications.
Efficient Nonmyopic Active Search
Active search is an active learning setting with the goal of identifying as many members of a given class as possible under a labeling budget.
Functional Aggregate Queries with Additive Inequalities
This new width is sandwiched between the submodular and the fractional hypertree widths.
Approximate Aggregate Queries Under Additive Inequalities
In contrast, we show that the situation with two additive inequalities is quite different, by showing that it is NP-hard to evaluate simple aggregation queries, with two additive inequalities, with any bounded relative error.
A Relational Gradient Descent Algorithm For Support Vector Machine Training
We consider gradient descent like algorithms for Support Vector Machine (SVM) training when the data is in relational form.
Fair Hierarchical Clustering
As machine learning has become more prevalent, researchers have begun to recognize the necessity of ensuring machine learning systems are fair.
Relational Algorithms for k-means Clustering
Few relational algorithms are known and this paper offers techniques for designing relational algorithms as well as characterizing their limitations.
Learnable and Instance-Robust Predictions for Online Matching, Flows and Load Balancing
Instance robustness ensures that the prediction is robust to modest changes in the problem input, where the measure of the change may be problem specific.
Faster Matchings via Learned Duals
Second, once the duals are feasible, they may not be optimal, so we show that they can be used to quickly find an optimal solution.
Robust Online Correlation Clustering
In correlation clustering we are given a set of points along with recommendations whether each pair of points should be placed in the same cluster or into separate clusters.
Learning-Augmented Algorithms for Online Steiner Tree
Steiner tree is known to have strong lower bounds in the online setting and any algorithm's worst-case guarantee is far from desirable.
Online Dynamic Acknowledgement with Learned Predictions
This elegant model studies the trade-off between acknowledgement cost and waiting experienced by requests.
Best of Many in Both Worlds: Online Resource Allocation with Predictions under Unknown Arrival Model
We take the shadow price of each resource as prediction, which can be obtained by predictions on future requests.
