Search Results for author: Benjamin Moseley

Found 20 papers, 6 papers with code

Learning-Augmented Algorithms for Online Steiner Tree

1 code implementation10 Dec 2021 Chenyang Xu, Benjamin Moseley

Steiner tree is known to have strong lower bounds in the online setting and any algorithm's worst-case guarantee is far from desirable.

Robust Online Correlation Clustering

no code implementations NeurIPS 2021 Silvio Lattanzi, Benjamin Moseley, Sergei Vassilvitskii, Yuyan Wang, Rudy Zhou

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.

Faster Matchings via Learned Duals

no code implementations NeurIPS 2021 Michael Dinitz, Sungjin Im, Thomas Lavastida, Benjamin Moseley, Sergei Vassilvitskii

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.

Combinatorial Optimization

Learnable and Instance-Robust Predictions for Online Matching, Flows and Load Balancing

no code implementations23 Nov 2020 Thomas Lavastida, Benjamin Moseley, R. Ravi, Chenyang Xu

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.

An Objective for Hierarchical Clustering in Euclidean Space and its Connection to Bisecting K-means

1 code implementation30 Aug 2020 Benjamin Moseley, Yuyan Wang

The paper builds a theoretical connection between this objective and the bisecting k-means algorithm.

Relational Algorithms for k-means Clustering

no code implementations1 Aug 2020 Benjamin Moseley, Kirk Pruhs, Alireza Samadian, Yuyan Wang

Few relational algorithms are known and this paper offers techniques for designing relational algorithms as well as characterizing their limitations.

Relational Reasoning

Fair Hierarchical Clustering

no code implementations NeurIPS 2020 Sara Ahmadian, Alessandro Epasto, Marina Knittel, Ravi Kumar, Mohammad Mahdian, Benjamin Moseley, Philip Pham, Sergei Vassilvitskii, Yuyan Wang

As machine learning has become more prevalent, researchers have begun to recognize the necessity of ensuring machine learning systems are fair.

Fairness

A Relational Gradient Descent Algorithm For Support Vector Machine Training

no code implementations11 May 2020 Mahmoud Abo-Khamis, Sungjin Im, Benjamin Moseley, Kirk Pruhs, Alireza Samadian

We consider gradient descent like algorithms for Support Vector Machine (SVM) training when the data is in relational form.

Approximate Aggregate Queries Under Additive Inequalities

no code implementations24 Mar 2020 Mahmoud Abo-Khamis, Sungjin Im, Benjamin Moseley, Kirk Pruhs, Alireza Samadian

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.

Cost Effective Active Search

1 code implementation NeurIPS 2019 Shali Jiang, Roman Garnett, Benjamin Moseley

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.

Active Learning

Backprop with Approximate Activations for Memory-efficient Network Training

1 code implementation ICLR 2019 Ayan Chakrabarti, Benjamin Moseley

Training convolutional neural network models is memory intensive since back-propagation requires storing activations of all intermediate layers.

Efficient nonmyopic batch active search

no code implementations NeurIPS 2018 Shali Jiang, Gustavo Malkomes, Matthew Abbott, Benjamin Moseley, Roman Garnett

A critical target scenario is high-throughput screening for scientific discovery, such as drug or materials discovery.

Drug Discovery

Efficient nonmyopic active search with applications in drug and materials discovery

no code implementations21 Nov 2018 Shali Jiang, Gustavo Malkomes, Benjamin Moseley, Roman Garnett

We also study the batch setting for the first time, where a batch of $b>1$ points can be queried at each iteration.

Drug Discovery

Pre-Synaptic Pool Modification (PSPM): A Supervised Learning Procedure for Spiking Neural Networks

1 code implementation7 Oct 2018 Bryce Bagley, Blake Bordelon, Benjamin Moseley, Ralf Wessel

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

no code implementations ICML 2017 Shali Jiang, Gustavo Malkomes, Geoff Converse, Alyssa Shofner, Benjamin Moseley, Roman Garnett

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.

Active Learning Drug Discovery

Bargaining for Revenue Shares on Tree Trading Networks

no code implementations22 Apr 2013 Arpita Ghosh, Satyen Kale, Kevin Lang, Benjamin Moseley

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.

Scalable K-Means++

2 code implementations29 Mar 2012 Bahman Bahmani, Benjamin Moseley, Andrea Vattani, Ravi Kumar, Sergei Vassilvitskii

The recently proposed k-means++ initialization algorithm achieves this, obtaining an initial set of centers that is provably close to the optimum solution.

Databases

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