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Recovering from Biased Data: Can Fairness Constraints Improve Accuracy? [article]

Avrim Blum, Kevin Stangl
<span title="2019-12-02">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In this work we consider a different motivation; learning from biased training data.  ...  Given such biased training data, Empirical Risk Minimization (ERM) may produce a classifier that not only is biased but also has suboptimal accuracy on the true data distribution.  ...  The high-level message of this paper is that fairness interventions need not be in competition with accuracy and may improve classification accuracy if training data is unrepresentative or biased; however  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1912.01094v1">arXiv:1912.01094v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/dmuwprewxjbuvhchcyfbr63q2a">fatcat:dmuwprewxjbuvhchcyfbr63q2a</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200901030549/https://arxiv.org/pdf/1912.01094v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/a5/69/a5694d9092838d6d45295fd64f14558d1b33b678.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1912.01094v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Recovering from Biased Data: Can Fairness Constraints Improve Accuracy?

Avrim Blum, Kevin Stangl, Aaron Roth
<span title="">2020</span> <i title="Schloss Dagstuhl - Leibniz-Zentrum für Informatik"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/zh3sjt2zlfdlrmbros3rycsdfi" style="color: black;">Symposium on Foundations of Responsible Computing</a> </i> &nbsp;
In this work we consider a different motivation; learning from biased training data.  ...  Given such biased training data, Empirical Risk Minimization (ERM) may produce a classifier that not only is biased but also has suboptimal accuracy on the true data distribution.  ...  the biased data-generation process.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.4230/lipics.forc.2020.3">doi:10.4230/lipics.forc.2020.3</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/forc/BlumS20.html">dblp:conf/forc/BlumS20</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3aipkf2npve7dnoizxnq2tolg4">fatcat:3aipkf2npve7dnoizxnq2tolg4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201212174322/https://drops.dagstuhl.de/opus/volltexte/2020/12019/pdf/LIPIcs-FORC-2020-3.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/c9/92/c992c031b6f4a036ada0be23c6b87784fcc3ed83.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.4230/lipics.forc.2020.3"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

Does enforcing fairness mitigate biases caused by subpopulation shift? [article]

Subha Maity, Debarghya Mukherjee, Mikhail Yurochkin, Yuekai Sun
<span title="2021-10-26">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In this paper, we study whether enforcing algorithmic fairness during training improves the performance of the trained model in the target domain.  ...  On one hand, we conceive scenarios in which enforcing fairness does not improve performance in the target domain. In fact, it may even harm performance.  ...  Theorem 4.3 characterizes the biases in the training data from which can be completely removed by enforcing appropriate algorithmic fairness constraints.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2011.03173v2">arXiv:2011.03173v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/xvunyynbajc3tarmb2eyowfbmu">fatcat:xvunyynbajc3tarmb2eyowfbmu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20211105092439/https://arxiv.org/pdf/2011.03173v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/c8/de/c8de2553150316a1247302c7c314f5478eaf816e.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2011.03173v2" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

A Confidence-Based Approach for Balancing Fairness and Accuracy [article]

Benjamin Fish, Jeremy Kun, Ádám D. Lelkes
<span title="2016-01-21">2016</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We demonstrate that even hopelessly naive modifications of a biased algorithm, which cannot be reasonably said to be fair, can still achieve low bias and high accuracy.  ...  We demonstrate that RRB distinguishes well between our naive and sensible fairness algorithms. RRB together with bias and accuracy provides a more complete picture of the fairness of an algorithm.  ...  Massaging is done in the previous literature based on a ranking learned from the biased data [6] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1601.05764v1">arXiv:1601.05764v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/igna67ifkjcixcwkgtaw54nyx4">fatcat:igna67ifkjcixcwkgtaw54nyx4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20191024183714/https://arxiv.org/pdf/1601.05764v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/f4/84/f484a58bcd467a237962ae230e0e79bc7bd7d940.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1601.05764v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

The Fairness-Accuracy Pareto Front [article]

Susan Wei, Marc Niethammer
<span title="2021-11-18">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Specifically, we put to use the concept of Pareto optimality from multi-objective optimization and seek the fairness-accuracy Pareto front of a neural network classifier.  ...  Algorithmic fairness seeks to identify and correct sources of bias in machine learning algorithms. Confoundingly, ensuring fairness often comes at the cost of accuracy.  ...  The final deliverable is a set of neural networks spanning the fairness-accuracy space from the high-accuracy-low-fairness corner to the high-fairness-low-accuracy corner.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2008.10797v2">arXiv:2008.10797v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/hkatk5zcubejlbf3qnsnws5ngm">fatcat:hkatk5zcubejlbf3qnsnws5ngm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20211130162920/https://arxiv.org/pdf/2008.10797v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/3e/a7/3ea7965bfcdef58e31e8028515de2a9872d81f0e.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2008.10797v2" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Consider the Alternatives: Navigating Fairness-Accuracy Tradeoffs via Disqualification [article]

Guy N. Rothblum, Gal Yona
<span title="2021-10-02">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Our requirement stipulates that a classifier should be disqualified if it is possible to improve its fairness by switching to another classifier from H without paying "too much" in accuracy.  ...  We show γ-disqualification can be used to easily compare different learning strategies in terms of how they trade-off fairness and accuracy, and we give an efficient reduction from the problem of finding  ...  To see this, consider an extreme case in which the data is highly biased, such that all high-accuracy models are in reality extremely unfair.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2110.00813v1">arXiv:2110.00813v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/dyx7do34wjckvebumenxpftr2q">fatcat:dyx7do34wjckvebumenxpftr2q</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20211006113405/https://arxiv.org/pdf/2110.00813v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/56/ea/56eab2de3b3f22e2ec63fb076968ea2e6e589513.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2110.00813v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Influence of numerical conditioning on the accuracy of relative orientation

Sinisa Segvic, Gerald Schweighofer, Axel Pinz
<span title="">2007</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ilwxppn4d5hizekyd3ndvy2mii" style="color: black;">2007 IEEE Conference on Computer Vision and Pattern Recognition</a> </i> &nbsp;
Then it is shown how conditioning can be used to improve the results of the recent five-point algorithm. This is not straightforward since the conditioning disturbs the calibration of the input data.  ...  The conditioning therefore needs to be reverted before enforcing the internal cubic constraints of the essential matrix.  ...  The desired metric can be expressed as the sum of the Frobenius norms of the matrices D 1 and D 2 obtained as: on the accuracy of the recovered rela- Figure 6 . 6 The dependence of |D1|F + |D2|F from  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/cvpr.2007.383351">doi:10.1109/cvpr.2007.383351</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/cvpr/SegvicSP07.html">dblp:conf/cvpr/SegvicSP07</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ze2mbpbsozcirofqvpsix6pb6m">fatcat:ze2mbpbsozcirofqvpsix6pb6m</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20070610090224/http://www.zemris.fer.hr/~ssegvic/pubs/bencos07www.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/1c/06/1c065bc99f1b3eee801c826387e2aa962322d565.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/cvpr.2007.383351"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Balancing Accuracy and Diversity in Recommendations using Matrix Completion Framework [article]

Anupriya Gogna, Angshul Majumdar
<span title="2019-12-11">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
However, several studies have suggested the need for diversified recommendations, with acceptable level of accuracy, to avoid monotony and improve customers experience.  ...  Design of recommender systems aimed at achieving high prediction accuracy is a widely researched area.  ...  This is based on searching for a lowest rank matrix among all possible matrices subject to the data fidelity constraint.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2001.04349v1">arXiv:2001.04349v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/krhuvj7cnvfmthxp7pnof7aw5i">fatcat:krhuvj7cnvfmthxp7pnof7aw5i</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200321044746/https://arxiv.org/ftp/arxiv/papers/2001/2001.04349.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2001.04349v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Testing the accuracy of reflection-based supermassive black hole spin measurements in AGN

E. S. Kammoun, E. Nardini, G. Risaliti
<span title="">2018</span> <i title="EDP Sciences"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/hn2qalwlofhybedfhzkh4ogcn4" style="color: black;">Astronomy and Astrophysics</a> </i> &nbsp;
The height of the X-ray source (in a lamp-post geometry) instead plays a crucial role in recovering the spin.  ...  The SMBH spin is retrieved with success in 31 cases, some of which (9) are even found among formally inaccurate fits (although with looser constraints).  ...  EN received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 664931.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1051/0004-6361/201732377">doi:10.1051/0004-6361/201732377</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/2g7dol7lvraa3bwatl2soufppu">fatcat:2g7dol7lvraa3bwatl2soufppu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200901032203/https://arxiv.org/pdf/1802.06800v1.pdf" title="fulltext PDF download [not primary version]" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <span style="color: #f43e3e;">&#10033;</span> <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/4b/b2/4bb29395b359685422b494b24de8094a10641081.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1051/0004-6361/201732377"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Effects of Sample Selection Bias on the Accuracy of Population Structure and Ancestry Inference

Suyash Shringarpure, Eric P. Xing
<span title="2014-03-17">2014</span> <i title="Genetics Society of America"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/lmrbfynd2rbtzo6uhqthfvnwci" style="color: black;">G3: Genes, Genomes, Genetics</a> </i> &nbsp;
Using simulated data and real genotype data from cattle, we show that sample selection bias can affect the results of population structure analyses.  ...  However, practical constraints dictate that of a geographical/ethnic population, only a small number of individuals are genotyped. The resulting data are a sample from the entire population.  ...  Due to practical constraints, only a small number of individuals from each population are genotyped and the resulting data is a sample from the entire population.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1534/g3.113.007633">doi:10.1534/g3.113.007633</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/24637351">pmid:24637351</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC4025489/">pmcid:PMC4025489</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/4ed6cdspxvd4fb45yekloobneu">fatcat:4ed6cdspxvd4fb45yekloobneu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170812190848/http://www.g3journal.org/content/ggg/early/2014/03/14/g3.113.007633.full.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/e6/7b/e67bc84a3c327fd03b74d9bb4e5ae3983e30efb3.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1534/g3.113.007633"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4025489" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Fairness Through Causal Awareness: Learning Latent-Variable Models for Biased Data [article]

David Madras, Elliot Creager, Toniann Pitassi, Richard Zemel
<span title="2018-12-03">2018</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We advocate a causal modeling approach to learning from biased data, exploring the relationship between fair classification and intervention.  ...  We further present evidence that estimating these causal effects can help learn policies that are both more accurate and fair, when presented with a historically biased dataset.  ...  Kallus and Zhou [20] explore the idea of learning from biased data, making the point that a "fair" predictor learned on biased data may not be fair under certain forms of distributional shift, while  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1809.02519v3">arXiv:1809.02519v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/4gkvwe4e4ngz7kfe6z257fuvnm">fatcat:4gkvwe4e4ngz7kfe6z257fuvnm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20191022033920/https://arxiv.org/pdf/1809.02519v3.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/fd/63/fd634109b8c203cc28907a7a39dea0bd0f7e5101.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1809.02519v3" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Unshuffling Data for Improved Generalization [article]

Damien Teney, Ehsan Abbasnejad, Anton van den Hengel
<span title="2020-11-20">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We obtain significant improvements on VQA-CP, using environments built from prior knowledge, existing meta data, or unsupervised clustering.  ...  The method makes a step beyond correlation-based learning: the choice of the partitioning allows injecting information about the task that cannot be otherwise recovered from the joint distribution of the  ...  The data collection process can be improved [24, 71, 72] but this option only addresses precisely identified biases and confounders.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2002.11894v3">arXiv:2002.11894v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jjbg5roi2fbyjp7mjxyog3hu6y">fatcat:jjbg5roi2fbyjp7mjxyog3hu6y</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201126093849/https://arxiv.org/pdf/2002.11894v3.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/34/74/3474a942d4d155b26a6e38c899fec72fbe5e84ec.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2002.11894v3" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Accuracy Guarantees for $\ell_1$-Recovery

Anatoli Juditsky, Arkadi Nemirovski
<span title="">2011</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/niovmjummbcwdg4qshgzykkpfu" style="color: black;">IEEE Transactions on Information Theory</a> </i> &nbsp;
We demonstrate how the estimates can be computed using the Non-Euclidean Basis Pursuit algorithm.  ...  We also show how these techniques allow to provide efficiently computable accuracy bounds for Lasso and Dantzig Selector.  ...  Our goal is to recover from , provided that is "nearly -sparse." Specifically, we consider the sets of signals which admit -sparse approximation of -accuracy .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tit.2011.2162569">doi:10.1109/tit.2011.2162569</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/pmajhjec6ndgbem4bu54qy37p4">fatcat:pmajhjec6ndgbem4bu54qy37p4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20120714034357/http://www2.isye.gatech.edu/~nemirovs/IEEEInfTheo2012.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/fa/ff/faff5daac50e10df649b99c2e1ef9b42e9c3e9de.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tit.2011.2162569"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

High-Accuracy Ranging and Localization with Ultra-Wideband Communications for Energy-Constrained Devices [article]

L. Flueratoru, S. Wehrli, M. Magno, E. S. Lohan, D. Niculescu
<span title="2021-11-03">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Therefore, UWB LRP devices can offer high-accuracy ranging and localization even to ultra-low-power devices in the IoT.  ...  Ultra-wideband (UWB) communications have gained popularity in recent years for being able to provide distance measurements and localization with high accuracy, which can enhance the capabilities of devices  ...  for dynamic wearable applications with privacy constraints, http://www.a-wear.eu/).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2104.11042v2">arXiv:2104.11042v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/cjyhzppttfan7kqonphjrd2jz4">fatcat:cjyhzppttfan7kqonphjrd2jz4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20211108204259/https://arxiv.org/pdf/2104.11042v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/88/4e/884e01f9ee2bdfb42405fc6d1573df309c63fdad.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2104.11042v2" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Adaptive Data Debiasing through Bounded Exploration and Fairness [article]

Yifan Yang and Yang Liu and Parinaz Naghizadeh
<span title="2021-10-25">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We further investigate how fairness measures can work in conjunction with such data debiasing efforts.  ...  Our proposed algorithm includes parameters that can be used to balance between the ultimate goal of removing data biases -- which will in turn lead to more accurate and fair decisions, and the exploration  ...  constraint evaluations can be improved.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2110.13054v1">arXiv:2110.13054v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/cif672hgk5ciphm4xk5nd56zv4">fatcat:cif672hgk5ciphm4xk5nd56zv4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20211106141149/https://arxiv.org/pdf/2110.13054v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/a8/6a/a86aebe4cbb4b8ea9aed4ca18c5ea1dd18e0858f.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2110.13054v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>
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