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pro vyhledávání: '"Lazovich , Tomo"'
Many existing fairness metrics measure group-wise demographic disparities in system behavior or model performance. Calculating these metrics requires access to demographic information, which, in industrial settings, is often unavailable. By contrast,
Externí odkaz:
http://arxiv.org/abs/2409.08135
Autor:
Cai, Vanessa, Prabakar, Pradeep, Rebuelta, Manuel Serrano, Rosen, Lucas, Monti, Federico, Janocha, Katarzyna, Lazovich, Tomo, Raj, Jeetu, Shrinivasan, Yedendra, Li, Hao, Markovich, Thomas
Recommendation systems are a core feature of social media companies with their uses including recommending organic and promoted contents. Many modern recommendation systems are split into multiple stages - candidate generation and heavy ranking - to
Externí odkaz:
http://arxiv.org/abs/2302.13915
Traditionally, recommender systems operate by returning a user a set of items, ranked in order of estimated relevance to that user. In recent years, methods relying on stochastic ordering have been developed to create "fairer" rankings that reduce in
Externí odkaz:
http://arxiv.org/abs/2209.05000
Autor:
Lazovich, Tomo, Belli, Luca, Gonzales, Aaron, Bower, Amanda, Tantipongpipat, Uthaipon, Lum, Kristian, Huszar, Ferenc, Chowdhury, Rumman
The harmful impacts of algorithmic decision systems have recently come into focus, with many examples of systems such as machine learning (ML) models amplifying existing societal biases. Most metrics attempting to quantify disparities resulting from
Externí odkaz:
http://arxiv.org/abs/2202.01615
Autor:
Russell, Rebecca L., Kim, Louis, Hamilton, Lei H., Lazovich, Tomo, Harer, Jacob A., Ozdemir, Onur, Ellingwood, Paul M., McConley, Marc W.
Increasing numbers of software vulnerabilities are discovered every year whether they are reported publicly or discovered internally in proprietary code. These vulnerabilities can pose serious risk of exploit and result in system compromise, informat
Externí odkaz:
http://arxiv.org/abs/1807.04320
Autor:
Harer, Jacob, Ozdemir, Onur, Lazovich, Tomo, Reale, Christopher P., Russell, Rebecca L., Kim, Louis Y., Chin, Peter
Motivated by the problem of automated repair of software vulnerabilities, we propose an adversarial learning approach that maps from one discrete source domain to another target domain without requiring paired labeled examples or source and target do
Externí odkaz:
http://arxiv.org/abs/1805.07475
Autor:
Harer, Jacob A., Kim, Louis Y., Russell, Rebecca L., Ozdemir, Onur, Kosta, Leonard R., Rangamani, Akshay, Hamilton, Lei H., Centeno, Gabriel I., Key, Jonathan R., Ellingwood, Paul M., Antelman, Erik, Mackay, Alan, McConley, Marc W., Opper, Jeffrey M., Chin, Peter, Lazovich, Tomo
Thousands of security vulnerabilities are discovered in production software each year, either reported publicly to the Common Vulnerabilities and Exposures database or discovered internally in proprietary code. Vulnerabilities often manifest themselv
Externí odkaz:
http://arxiv.org/abs/1803.04497
Autor:
Lazovich, Tomo
We present techniques used to estimate the backgrounds in the search for the Standard Model Higgs boson in the H->WW*->lvlv decay channel with the ATLAS experiment at the LHC. The dataset corresponds to 13 fb-1 of integrated luminosity taken at a cen
Externí odkaz:
http://arxiv.org/abs/1301.7660
Autor:
Lazovich, Tomo, Belli, Luca, Gonzales, Aaron, Bower, Amanda, Tantipongpipat, Uthaipon, Lum, Kristian, Huszár, Ferenc, Chowdhury, Rumman
Publikováno v:
In Patterns 11 August 2023 4(8)
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