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The blind application of machine learning runs the risk of amplifying biases present in data. Such a danger is facing us with word embedding, a popular framework to represent text data as vectors… (More)
- Tolga Bolukbasi, Joseph Wang, Ofer Dekel, Venkatesh Saligrama
- ICML
- 2017
We present an approach to adaptively utilize deep neural networks in order to reduce the evaluation time on new examples without loss of accuracy. Rather than attempting to redesign or approximate… (More)
- Tolga Bolukbasi, Joseph Wang, Ofer Dekel, Venkatesh Saligrama
- ArXiv
- 2017
We present an approach to adaptively utilize deep neural networks in order to reduce the evaluation time on new examples without loss of classification performance. Rather than attempting to redesign… (More)
- Ziming Zhang, Yun Liu, Tolga Bolukbasi, Ming-Ming Cheng, Venkatesh Saligrama
- ArXiv
- 2015
We are motivated by the need for an object proposal generation algorithm that achieves a good balance between proposal localization quality, object recall and computational efficiency. We propose a… (More)
- Tolga Bolukbasi, Kai-Wei Chang, Joseph Wang, Venkatesh Saligrama
- ArXiv
- 2016
We study the problem of structured prediction under test-time budget constraints. We propose a novel approach applicable to a wide range of structured prediction problems in computer vision and… (More)
Budget constraints arise in many computer vision problems. Computational costs limit many automated recognition systems while crowdsourced systems are hindered by monetary costs. We leverage wide… (More)
Machine learning algorithms are optimized to model statistical properties of the training data. If the input data reflects stereotypes and biases of the broader society, then the output of the… (More)
- Pengkai Zhu, Hanxiao Wang, Tolga Bolukbasi, Venkatesh Saligrama
- ArXiv
- 2018
As we move towards large-scale object detection, it is unrealistic to expect annotated training data for all object classes at sufficient scale, and so methods capable of unseen object detection are… (More)
We study the problem of structured prediction under test-time budget constraints. We propose a novel approach based on selectively acquiring computationally costly features during test-time in order… (More)
In this paper, we study a generic learning framework for building robust text classification model that achieves accuracy comparable to standard full models under test-time budget constraints. Our… (More)