activity
20162022
most citedImproving the Expected Improvement Algorithm

39 citations · 154 across the 26 of their papers we have counts for

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15 papers · 1 filter

stat.ML20202 cited

Open Set Domain Adaptation by Extreme Value Theory

Yiming Xu, Diego Klabjan

Common domain adaptation techniques assume that the source domain and the target domain share an identical label space, which is problematic since when target samples are unlabeled…

stat.ML2020

Keyword-based Topic Modeling and Keyword Selection

Xingyu Wang, Lida Zhang, Diego Klabjan

Certain type of documents such as tweets are collected by specifying a set of keywords. As topics of interest change with time it is beneficial to adjust keywords dynamically. The…

stat.ML2018

Unified recurrent neural network for many feature types

Alexander Stec, Diego Klabjan, Jean Utke

There are time series that are amenable to recurrent neural network (RNN) solutions when treated as sequences, but some series, e.g. asynchronous time series, provide a richer vari…

stat.ML2018

Nested multi-instance classification

Alexander Stec, Diego Klabjan, Jean Utke

There are classification tasks that take as inputs groups of images rather than single images. In order to address such situations, we introduce a nested multi-instance deep networ…

stat.ML2018

Forecasting Crime with Deep Learning

Alexander Stec, Diego Klabjan

The objective of this work is to take advantage of deep neural networks in order to make next day crime count predictions in a fine-grain city partition. We make predictions using…

stat.ML2018

Bayesian active learning for choice models with deep Gaussian processes

Jie Yang, Diego Klabjan

In this paper, we propose an active learning algorithm and models which can gradually learn individual's preference through pairwise comparisons. The active learning scheme aims at…