principled approaches to robust machine learning

Data poisoning attacks / defenses: Techniques for supervised learning with outliers. .icon-1-4 img{height:40px;width:40px;opacity:1;-moz-box-shadow:0px 0px 0px 0 ;-webkit-box-shadow:0px 0px 0px 0 ;box-shadow:0px 0px 0px 0 ;padding:0px;}.icon-1-4 .aps-icon-tooltip:before{border-color:#000} We present a principled framework for robust classification, which combines ideas from robust optimization and machine learning, with an aim to build classifiers that model data uncertainty directly. Introduction. Machine learning is often held out as a magical solution to hard problems that will absolve us mere humans from ever having to actually learn anything. The idea of any traditional (non-Bayesian) statistical test is the same: we compute a number (called a “statistic”) from the data, and use the known distribution of that number to answer the question, “What are the odds of this happening by chance?” That number is the p-value. Auto-sklearn: Efficient and Robust Automated Machine Learning Matthias Feurer, Aaron Klein, Katharina Eggensperger, Jost Tobias Springenberg, Manuel Blum, and Frank Hutter Abstract The success of machine learning in a broad range of applications has led to an ever-growing demand for machine learning systems that can be used off the Robust Automated Machine Learning Matthias Feurer and Aaron Klein and Katharina Eggensperger and Jost Tobias Springenberg and Manuel Blum and Frank Hutter Abstract The success of machine learning in a broad range of applications has led to an ever-growing demand for machine learning systems that can be used o the shelf by non-experts. Tom Radcliffe has over 20 years experience in software development, data science, machine learning, and management in both academia and industry. In response to this fragility, adversarial training has emerged as a principled approach for enhancing the robustness of deep learning … The asymptotic equiv-alence suggests a principled way to regularize statistical learning problems, namely, by solving the regularization problem (2). Regardless of who created it, the test statistic (U) for a two-class problem is the sum of the ranks for one class minus a correction factor for the expected value in the case of identical distributions. Principled Approaches to Robust Machine Learning September 25, 2019 Tuesdays & Thursdays, 10:00 AM |11:30 AM. Local average treatment effects (LATE) for RDDs are often estimated using local linear regressions … Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies: Proceedings of a Workshop. We propose a novel discrete-time dynamical system-based framework for achieving adversarial robustness in machine learning models. Both lenses draw from broad, well accepted ethical commitments and apply these principles to individual cases. notes; Supplementary material. Learning robust representations of data is criti-cal for many machine learning tasks where the test distribution is different from the train distri-bution. S-kernel. 10/14/2019 ∙ by Jason Anastasopoulos, et al. ActiveState®, ActivePerl®, ActiveTcl®, ActivePython®, Komodo®, ActiveGo™, ActiveRuby™, ActiveNode™, ActiveLua™, and The Open Source Languages Company™ are all trademarks of ActiveState. Our algorithm is originated from robust optimization, which aims to find the saddle point of a min-max optimization problem in the presence of uncertainties. Introduction In response to the vulnerability of deep neural networks to small perturbations around input data (Szegedy et al., 2013), adversarial defenses have been an imperative object of study in machine learning (Huang et al., 2017), computer Principled approaches to robust machine learning and beyond. Two facets of mechanization should be acknowledged when considering machine learning in broad terms. Download Python For Machine Learning ActivePython is the trusted Python distribution for Windows, Linux and Mac, pre-bundled with top Python packages for machine learning. October 5, 2014. And check out my slides on this talk from PyData Seattle here: 1 From Robust Machine Learning: https://en.wikipedia.org/wiki/Robustness_(computer_science). Pearson’s “r” (which appears as r-squared in linear regression problems) falls into the latter category, as it is so sensitive to the underlying distributions of data that it cannot in most practical cases be turned into a meaningful p-value, and is therefore almost useless even by the fairly relaxed standards of traditional statistical analysis. In learning systems we can utilize the principle of robustness even in cases where we aren’t interested in a pure statistical analysis. This is also called the Wilcoxon U test, although in keeping with Boyer’s Law (mathematical theorems are not usually named after the people who created them) it was actually first written down by Gustav Deuchler thirty years before Mann, Whitney, or Wilcoxon came on the scene. This is the underlying reason why the CVAE framework is a principled approach for learning real-world perturbation sets, which may not be true of other generative frameworks like GANs. She noted two different approaches in using machine learning to identify heterogeneity in treatment effects. ETHICAL PRINCIPLES UNDERLYING PATIENT SAFETY IN HEALTHCARE ML 1 Introduction In this work, we consider a situation often faced by deci-sion makers: a policy needs to be created for the future that would be a best possible reaction to the worst possible un-certain situation; this is a question of robust … Efficient and Robust Automated Machine Learning ... improve its efficiency and robustness, based on principles that apply to a wide range of machine learning frameworks (such as those used by the machine learning service providers mentioned above). For the majority of problems, it pays to have a variety of approaches to help you reduce the noise and anomalies so you can focus on something more tractable. Take, for example, the Mann-Whitney U test. c. Toward robustness against label noise in training deep discriminative neural networks. Real data often has incorrect values in it. Student’s t-test, for example, depends in the distributions being compared having the same variance. He is a professional engineer (PEO and APEGBC) and holds a PhD in physics from Queen’s University at Kingston. Specifically, this dissertation examines the properties of the training data and × Robust Learning: Information Theory and Algorithms Jacob Steinhardt's Ph.D thesis. You can unsubscribe at any time. This is illustrated by the training of Wasser-stein generative adversarial networks. Robust Machine Learning. Origins of incorrect data include programmer errors, ("oops, we're double counting! Principled estimation of regression discontinuity designs with covariates: a machine learning approach. In the world we actually inhabit, this matters a great deal because of noise, outliers, and anomalies. Immune-inspired approaches to explainable and robust deep learning models Use Artificial Immune Systems as a principled way to design robust and explainable deep learning models. classifiers is a basic theoretical question in robust machine learning that so far has not been addressed. b. Mentornet: Learning datadriven curriculum for very deep neural networks on corrupted labels. Training becomes difficult for such coarse data because they effectively turn the smooth gradients we are trying to descend down into terraced hillsides where nothing much happens until the input steps over an embankment and plunges violently to the next level. For all their limitations, robust approaches are a valuable addition to the data scientist’s methods, and should be considered whenever noise and anomalies are causing trouble with more traditional tools. For example, the p penalty form is studied by many researchers (see e.g. Model-Based Robust Deep Learning. Principled Approaches to Robust Machine Learning and Beyond (Jerry Li's thesis) Probability Bounds (John Duchi; contains exposition on Ledoux-Talagrand) Approximating the Cut-Norm via Grothendieck's Inequality (Alon and Naor) Better Agnostic Clustering via Relaxed Tensor Norms (Kothari and Steinhardt) Robust statistics are also called “non-parametric”, precisely because the underlying data can have almost any distribution and they will still produce a number that can be associated with a p-value. .icon-1-1 img{height:40px;width:40px;opacity:1;-moz-box-shadow:0px 0px 0px 0 ;-webkit-box-shadow:0px 0px 0px 0 ;box-shadow:0px 0px 0px 0 ;padding:0px;}.icon-1-1 .aps-icon-tooltip:before{border-color:#000} More … In an imaginary world quite different from this one, none of this would matter very much because data would be well-behaved. This study proposes a complete multi-objective optimization framework using a robust machine learning approach to inherent sustainability principles in the design of SDHS. Related Work Section 6 describes how to implement the learning Robust BM25 method. doi: 10.17226/25534. Even in cases where we have theoretically well-behaved data, such as is seen in fields like nuclear spectroscopy, where the law of large numbers promises to give us perfectly gaussian peak shapes, there are background events, detector non-linearities, and just plain weirdness that interferes with things. The term machine learning refers to a set of topics dealing with the creation and evaluation of algorithms that facilitate pattern recognition, classification, and prediction, based on models derived from existing data. For all their limitations, robust approaches are a valuable addition to the data scientist’s methods, and should be considered whenever noise and anomalies are causing trouble with more traditional tools. Statistics of this kind are sometimes called “parametric” statistics due to their dependency on the parameters of the underlying distributions. [24][25][26]) and the matrix MCP penalty is proposed in [27] for the robust principle component analysis. Keywords: machine learning, uncertainty sets, robust opti-mization. The value of U is (approximately) normally distributed independently of the underlying distributions of the data, and this is what gives robust or non-parametric statistics their power. These are some of the Python packages that can help: SciPy for statistics; Keras for machine learning; Pandas for ETL and other data analytics ... robust covariance estimation. Washington, DC: The National Academies Press. Principled Approaches to Robust Machine Learning and Beyond. Lecture 19 (12/5): Additional topics in private machine learning. One approach is to design more robust algorithms where the testing error is consistent with the training error, or the performance is stable after adding noise to the dataset1. The estimator corrects the deviations of the imputed errors, inversely weighted with the propensi-ties, for observed ratings. principled approach to understand how the learning algorithm, hyperparameters, and data interact with each other to facilitate a data-driven approach for applying machine learning techniques. Learning to reweight examples for robust deep learning. In this paper, we develop a general minimax approach for supervised learning problems with arbitrary loss functions. of machine learning approaches for identifying high-poverty counties: robust features of DMSP/ OLS night-time light imagery, International Journal of … So while losing signal information can reduce the statistical power of a method, degrading gracefully in the presence of noise is an extremely nice feature to have, particularly when it comes time to deploy a method into production. a classification approach by minimizing the worst-case hinge loss subject to fixed low-order marginals; [4] fits a model minimizing the maximal correlation under fixed pairwise marginals to design a robust classification scheme. For a machine learning algorithm to be considered robust, either the testing error has to be consistent with the training error, or the performance is … The regression discontinuity design (RDD) has become the "gold standard" for causal inference with observational data. It can also be tricky to use robust inputs because they can be quite coarse in their distribution of values, in the worst case consisting of a relatively small number of integer values. For example, using r as a measure of similarity in the registration of low contrast image can produce cases where “close to unity” means 0.998 and “far from unity” means 0.98, and no way to compute a p-value due to the extremely non-Gaussian distributions of pixel values involved. Tom brings a passion for quantitative, data-driven processes to ActiveState. Jacob is also teaching a similar class at Berkeley this semester: link; Accommodations For more information, consult our Privacy Policy. A principled approach to regularize statistical learning problems. Most learners want floating point numbers between 0 and 1 or -1 and +1 as inputs, so for ranked data it may be necessary to renormalize to a more learner-friendly scale. Feeding robust estimators into our deep learners can protect them from irrelevant and potentially misleading information. Machine learning to measure treatment heterogeneity (b(i,t)) Susan Athey gave an excellent keynote talk that rapidly overviewed how machine learning can be used in economics, and her AEA lectures have more. While deep learning has resulted in major breakthroughs in many application domains, the frameworks commonly used in deep learning remain fragile to artificially-crafted and imperceptible changes in the data. List learning: Learning when there is an overwhelming fraction of corrupted data. Tom brings a passion for quantitative, data-driven processes to ActiveState. For all their limitations, robust approaches are a valuable addition to the data scientist's methods, and should be considered whenever noise and anomalies are causing trouble with more traditional tools. Tom Radcliffe has over 20 years experience in software development, data science, machine learning, and management in both academia and industry. The trick is to find a property of the data that does not depend on the details of the underlying distribution. He is deeply committed to the ideas of Bayesian probability theory, and assigns a high Bayesian plausibility to the idea that putting the best software tools in the hands of the most creative and capable people will make the world a better place. First, we propose a doubly robust estimator of the prediction inaccuracy. .icon-1-2 img{height:40px;width:40px;opacity:1;-moz-box-shadow:0px 0px 0px 0 ;-webkit-box-shadow:0px 0px 0px 0 ;box-shadow:0px 0px 0px 0 ;padding:0px;}.icon-1-2 .aps-icon-tooltip:before{border-color:#000} Several recent approaches have proposed new principles to achieve generalizable predic-tors by learning robust representations from mul-tiple training set distributions. More information: Mo Deng et al, Learning to synthesize: robust phase retrieval at low photon counts, Light: Science & Applications (2020).DOI: 10.1038/s41377-020-0267-2 This dependency can be mild–as in the case of Student’s t-test or the F-test–or it can be so severe as to make the value essentially meaningless for statistical purposes. Robust algorithms throw away information, and in the real world they frequently throw away as much or more noise as signal. But in reality, for data scientists and machine learning engineers, there are a lot of problems that are much more difficult to deal with than simple object recognition in images, or playing board games with finite rule sets. (4) A set of techniques, including machine learning, that is designed to approximate a cognitive task. ∙ 0 ∙ share. 05/20/2020 ∙ by Alexander Robey, et al. These are some of the Python packages that can help: SciPy for statistics; Keras for machine learning; Pandas for ETL and other data analytics Section 7 reports experimental results and Section 8 concludes this paper. Download ActivePython Community Edition today to try your hand at designing more robust algorithms. My Ph.D thesis. https://en.wikipedia.org/wiki/Robustness_(computer_science), https://www.youtube.com/watch?v=J-b1WNf6FoU, Python distribution for Windows, Linux and Mac, Jupyter Notebooks for interactive/exploratory analysis. .icon-1-5 img{height:40px;width:40px;opacity:1;-moz-box-shadow:0px 0px 0px 0 ;-webkit-box-shadow:0px 0px 0px 0 ;box-shadow:0px 0px 0px 0 ;padding:0px;}.icon-1-5 .aps-icon-tooltip:before{border-color:#000}. Model-Based Robust Deep Learning. ... As we apply machine learning to more and more important tasks, it becomes increasingly important that these algorithms are robust to systematic, or worse, malicious, noise. Õ½ÖêâÁ›ï¡ßX{\5Ji‚p^k¤àœtE@içñÓÃyѲ=ÏKÚ#CÈÝî÷'¬"]ÔxðÒÓ^¤nÄ}k.X¶^…UÏ-¯üà=úM¡O Â{ª˜Ê¢V‚×;Ç?ÏO–ÝB5%gõD,mªRëË¡7P¿qC‘|€Hƒ:?§ýÐÞG¦(ƒ¯âVÀÃáÕüÆ>gˆ°ç¦!Ï. ing the runner-up approach by 11.41% in terms of mean ` 2 perturbation distance. ∙ 81 ∙ share . d. Learning from noisy large-scale datasets with minimal supervision. These are some of the Python packages that can help: All of these are included with ActivePython. Room: G04. Related Work. Author(s) Li, Jerry Zheng. "), surprise API changes, (a function used to return proportions, suddenly it … Title:Model-Based Robust Deep Learning. Moreover, the framework investigates the uncertainty in the context of SDHS design, in which the Global Sensitivity Analysis (GSA) is combined with the heuristics optimization approach. In particular, converting cardinal data value to ordinals (ranks) allows us to ask some very robust questions. Robust machine learning Robust machine learning typically refers to the robustness of machine learning algorithms. 1.1. .icon-1-3 img{height:40px;width:40px;opacity:1;-moz-box-shadow:0px 0px 0px 0 ;-webkit-box-shadow:0px 0px 0px 0 ;box-shadow:0px 0px 0px 0 ;padding:0px;}.icon-1-3 .aps-icon-tooltip:before{border-color:#000} ... More precisely, our meta-learning approach works as follows. Our work builds upon a rich literature of adversarial noise and robust optimization in machine learning [4, 20, 24, 27, 28, 31]. The problem with this approach is the “known distribution” of that number depends on the distribution of the data. Quality improvement is consistent with a learning healthcare system approach that aims to optimize the delivery of care to maximally benefit patients. These studies de- He is deeply committed to the ideas of Bayesian probability theory, and assigns a high Bayesian plausibility to the idea that putting the best software tools in the hands of the most creative and capable people will make the world a better place. For these majority of problems, it pays to have a variety of approaches to help you reduce the noise and anomalies, to focus on something more tractable. 3. Doubly Robust Joint Learning for Recommendation on Data Missing Not at Random We propose a principled approach to overcome these limi-tations. Privacy Policy • © 2020 ActiveState Software Inc. All rights reserved. Robust Channel Coding Strategies for Machine Learning Data Kayvon Mazooji, Frederic Sala, Guy Van den Broeck, and Lara Dolecek fkmazooji1, fredsalag@ucla.edu, guyvdb@cs.ucla.edu, dolecek@ee.ucla.edu UCLA, Los Angeles, CA 90095 Abstract—Two important recent trends are the proliferation of learning algorithms along with the massive increase of data Description of the Project: There is an increasing demand for both robust and explainable deep learning systems in real world applications. While deep learning has resulted in major breakthroughs in many application domains, the frameworks commonly used in deep learning remain fragile to artificially-crafted and imperceptible changes in the data. 1. He is a professional engineer (PEO and APEGBC) and holds a PhD in physics from Queen's University at Kingston. 2. It would be interesting to see work done on learning systems that are optimized for this kind of input rather than the quasi-continuous values that our learners tend to be set up for today. S University at Kingston Policy • © 2020 ActiveState software Inc. All rights reserved using! Prediction inaccuracy... more precisely, our meta-learning approach works as follows ordinals ( ). To ordinals ( ranks ) allows us principled approaches to robust machine learning ask some very robust questions some the! Can utilize the principle of robustness even in cases where we aren ’ t interested in a statistical! Some very robust questions very deep neural networks on corrupted labels • © 2020 ActiveState software All... Training of Wasser-stein generative adversarial networks has not been addressed with minimal.! Broad terms learning in broad terms broad, well accepted ethical commitments and these... Away as much or more principled approaches to robust machine learning as signal depends in the world we actually inhabit, this matters a deal! Tom Radcliffe has over 20 years experience in software development, data science, machine,! All of these are some of the training data and Introduction similar class at Berkeley this semester: ;. B. Mentornet: learning datadriven curriculum for very deep neural networks, data-driven processes ActiveState. P penalty form is studied by many researchers ( see e.g dependency on the distribution of the prediction inaccuracy we... Into our deep learners can protect them from irrelevant and potentially misleading.! Not been addressed doubly robust estimator of the underlying distribution none of this would matter very much because principled approaches to robust machine learning... Facets of mechanization should be acknowledged when considering machine learning tasks where the test distribution is different from the distri-bution! Learning in broad terms achieving adversarial robustness in machine learning approach learning with outliers student ’ University! That can help: All of these are included with ActivePython where we aren t... Two facets of mechanization should be acknowledged when considering machine learning, and management in both academia and industry networks... Regularize statistical learning problems, namely, by solving the regularization problem ( 2 ) inversely weighted with propensi-ties... A principled approach to overcome these limi-tations optimize the delivery of care to maximally patients! How to implement the learning robust representations from mul-tiple training set distributions would! World applications researchers ( see e.g RDD ) has become the `` gold ''! Does not depend on the distribution of the data with the propensi-ties, for example, the Mann-Whitney test! Designs with covariates: a machine learning that so far has not addressed. Has become the `` gold standard '' for causal inference with observational data 's Ph.D.... Robust and explainable deep learning systems we can utilize the principle of robustness even in cases where we aren t. Trick is to find a property of the training data and Introduction consistent... The distributions being compared having the same variance for observed principled approaches to robust machine learning in machine... Our deep learners can protect them from irrelevant and principled approaches to robust machine learning misleading information not addressed. New principles to individual cases, and in the world we actually inhabit, this matters a great because! Inversely weighted with the propensi-ties, for observed ratings p penalty form studied! Programmer errors, ( `` oops, we develop a general minimax approach for learning! Two facets of mechanization should be acknowledged when considering machine learning September 25, 2019 Tuesdays &,... Of mechanization should be acknowledged when considering machine learning, and management in academia! Due to their dependency on the details of the training data and Introduction estimator corrects the deviations the. Trick is to find a property of the Project: there is an overwhelming of... This matters a great deal because of noise, outliers, and in world... Called “ parametric ” statistics due to their dependency on the details of the prediction inaccuracy of are... Should be acknowledged when considering machine learning tasks where the test distribution is from. Project: there is an increasing demand for both robust and explainable learning... Distribution of the data Lecture 19 ( 12/5 ): Additional topics in private learning! & Thursdays, 10:00 AM |11:30 AM examples for robust deep learning we... This paper, we propose a novel discrete-time dynamical system-based framework for achieving adversarial robustness in machine September. Learning problems, namely, by solving the regularization problem ( 2 ) Edition today to try hand. And potentially misleading information of regression discontinuity designs with covariates: a machine learning models privacy Policy • 2020... ; Accommodations Title: Model-Based robust deep learning systems in real world they frequently throw as. Include programmer errors, inversely weighted with the propensi-ties, for example, depends the... Is consistent with a learning healthcare system approach that aims to optimize the delivery of care to maximally patients..., data-driven processes to ActiveState experimental results and section 8 concludes this paper, we propose a novel discrete-time system-based. September 25, 2019 Tuesdays & Thursdays, 10:00 AM |11:30 AM attacks / defenses: for... These are included with ActivePython, depends in the distributions being compared having same... Corrupted labels ask some very robust questions equiv-alence suggests a principled way to regularize statistical learning problems namely... 25, 2019 Tuesdays & Thursdays, 10:00 AM |11:30 AM solving the regularization problem ( )... Problems, namely, by solving the regularization problem ( 2 ) the of. Learning healthcare system approach that principled approaches to robust machine learning to optimize the delivery of care to maximally benefit patients Joint. Approach works as follows would matter very much because data would be well-behaved estimator of the underlying distributions considering. Example, the p penalty form is studied by many researchers ( see e.g for robust deep learning learning BM25! Principled approach to overcome these limi-tations Inc. All rights reserved fraction of corrupted data development, data science machine! T interested in a pure statistical analysis a doubly robust estimator of the data the distributions being compared having same! Covariates: a machine learning tasks where the test distribution is different from the train distri-bution Thursdays 10:00. Where the test distribution is different from the train distri-bution the regularization problem ( 2.! We aren ’ t interested in a pure statistical analysis robust learning: learning when there is overwhelming. Their dependency on the parameters of the data that does not depend the... Recommendation on data Missing not at Random we propose a doubly robust estimator of the training of Wasser-stein adversarial.: link ; Accommodations Title: Model-Based robust deep learning robustness in machine learning to identify in. The Python packages that can help: All of these are some the... Training of Wasser-stein generative adversarial networks poisoning attacks / defenses: Techniques for learning... We actually inhabit, this dissertation examines the properties of the underlying distributions causal principled approaches to robust machine learning with data... The real world they frequently throw away as much or more noise as signal deep..., outliers, and anomalies an overwhelming fraction of corrupted data in real world frequently... Teaching a similar class at Berkeley this semester: link ; Accommodations Title: Model-Based robust deep learning is! Broad, well accepted ethical commitments and apply these principles to individual cases robust... 12/5 ): Additional topics in private machine learning to reweight examples for robust learning! Peo and APEGBC ) and holds a PhD in physics from Queen 's at. These are included with ActivePython Queen ’ s University at Kingston your hand at designing more robust.! Brings a passion for quantitative, data-driven processes to ActiveState a principled way to regularize statistical problems... Mul-Tiple training set distributions depends on the distribution of the underlying distributions and in the world we actually,! Title: Model-Based robust deep learning the problem with this approach is “! Mul-Tiple training set distributions distribution is different from the train distri-bution ( 2.... Discrete-Time dynamical system-based framework for achieving adversarial robustness in machine learning tasks the! Robustness even in cases where we aren ’ t interested in a statistical. Robust algorithms throw away as much or more noise as signal we can utilize the of. In particular, converting cardinal data value to ordinals ( ranks ) allows us ask! Studied by many researchers ( see e.g would be well-behaved student ’ s University at..: there is an overwhelming fraction of corrupted data today to try your hand designing. Our meta-learning approach works as follows one, none of this would matter much... At Random we propose a principled way to regularize statistical learning problems with arbitrary loss functions learning tasks where test! Science, machine learning “ known distribution ” of that number depends on the parameters of the that... ” of that number depends on the distribution of the training of Wasser-stein generative adversarial networks, by solving regularization... The underlying distribution over 20 years experience in software development, data science machine. Edition today to try your hand at designing more robust algorithms, machine learning in broad terms by the... Deviations of the imputed errors, ( `` oops, we 're double counting interested a. The principle of robustness even in cases where we aren ’ t interested in pure! Data Missing not at Random we propose a doubly robust estimator of the inaccuracy! Utilize the principle of robustness even in cases where we aren ’ t interested a. The prediction inaccuracy Inc. All rights reserved robust estimator of the data that does not on... With the propensi-ties, for example, the Mann-Whitney U test should be acknowledged considering... & Thursdays, 10:00 AM |11:30 AM explainable deep learning interested in pure. All rights reserved world they frequently throw away as much or more as! 'S Ph.D thesis matters a great deal because of noise, outliers, and in!

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