In this talk, Bernease Herman speaks about recent explainable ML research
Presented on 06/06/2019
**These slides are from a talk given at Rsqrd AI. Learn more at rsqrdai.org**
Interactive Analysis of Word Vector Embeddingsgleicher
Word vector embeddings present challenges for interactive analysis due to their high-dimensional nature and complex relationships between words. The authors conducted a task analysis of common uses of word embeddings which revealed 7 linguistic tasks. They designed 3 visualizations - Buddy Plots, Concept Axis Plots, and Co-occurrence Matrices - to support the tasks of understanding word similarities, co-occurrences, and semantic directions within concept axes. An online system implements the visualizations to enable interactive exploration of word vector embeddings.
This document summarizes key aspects of experimental design and methods for coding texts from media research. It discusses the components of experimental design including causality, theory, control, and ecological validity. It also outlines different types of intentionalities to analyze in media texts, such as those of the author, text, audience, and interpreter. Finally, it provides guidance on coding texts by establishing the texts of interest, analytical approach, unit of analysis, coding scheme, and analysis.
The document provides a syllabus for the MS and PhD entrance test for the School of Information Technology at IIT Kharagpur. It outlines the topics that will be covered in the test, including basic mathematics, programming basics, computing systems basics, and logical reasoning. The mathematics section covers topics like set theory, matrices, combinatorics, and probability and statistics. The programming section outlines basic concepts like decision structures, recursion, data structures, searching and sorting. The computing systems section covers computer organization, networking, databases, and operating systems. Logical reasoning assesses verbal reasoning through puzzles and non-verbal reasoning through pattern perception and rule detection puzzles.
How hard is this Query? Measuring the Semantic Complexity of Schema-agnostic ...Andre Freitas
The growing size, heterogeneity and complexity of databases demand the creation of strategies to facilitate users and systems to consume data. Ideally, query mechanisms should be schema-agnostic, i.e. they should be able to match user queries in their own vocabulary and syntax to the data, abstracting data consumers from the representation of the data. This work provides an informationtheoretical framework to evaluate the semantic complexity involved in the query-database communication, under a schema-agnostic query scenario. Different entropy measures are introduced to quantify the semantic phenomena involved in the user-database communication, including structural complexity, ambiguity, synonymy and vagueness. The entropy measures are validated using natural language queries over Semantic Web databases. The analysis of the semantic complexity is used to improve the understanding of the core semantic dimensions present at the query-data matching process, allowing the improvement of the design of schema-agnostic query mechanisms and defining measures which can be used to assess the semantic uncertainty or difficulty behind a schema-agnostic querying task.
Get hands-on with Explainable AI at Machine Learning Interpretability(MLI) Gym!Sri Ambati
This meetup took place in Mountain View on January 24th, 2019.
Description:
With the effort and contributions from researchers and practitioners from academia and industry, Machine Learning Interpretation has become a young sub-field of ML. However, the norms around its definition and understanding is still in its infancy and there are numerous different approaches emerging rapidly. However, there seems to be a lack of a consistent explanation framework to evaluate and consistently benchmark different algorithms - evaluating against interpretation, completeness and consistency of the algorithms.
The idea with the gym is to provide a controlled interactive environment for all forms of Machine Learning algorithms, - initially focusing on supervised predictive modeling problems, to allow analysts and data-scientists to explore, debug and generate insightful understanding of the models by
1.Model Validation: Ways to explore and validate black box ML systems enabling model comparison both globally and locally - identifying biases in the training data through interpretation.
2.What-if Analysis: An interactive environment where communication can happen i.e. enable learning through interactions. User having the ability to conduct "What-If" analysis - effect of single or multiple features and their interactions
3.Model Debugging: Ways to analyze the misbehavior of the model by exploring counterfactual examples(adversarial examples and training)
4. Interpretable Models: Ability to build natively interpretable models - with the goal to simplify complex models to enable better understanding.
The central concept with MLI gym is to have an interactive environment where one could explore and simulate variations in the world(a world post a model is operationalized) beyond the defined model metrics point estimates - e.g. ROC-AUC, confusion matrix, RMSE, R2 score and others.
Speaker's Bio:
Pramit is a Lead Data Scientist/ at H2O.ai. His area of interests is building Statistical/Machine Learning models(Bayesian and Frequentist Modeling techniques) to help the business realize their data-driven goals.
Currently, he is exploring "Model Interpretation" as means to efficiently understand the true nature of predictive models to enable model robustness and security. He believes effective Model Inference coupled with Adversarial training could lead to building trustworthy models with known blind spots. He has started an open source project Skater: https://github.com/datascienceinc/Skater to solve the need for Model Inference(The project is still in its early stages of development but check it out, always eager for feedback)
Interpretable machine learning : Methods for understanding complex modelsManojit Nandi
1. Interpretability helps understand complex machine learning models by explaining their outcomes based on inputs. Higher predictive accuracy often reduces interpretability.
2. Methods like LIME and SHAP attribute model outcomes to input features through local surrogate models and game theory.
3. Recourse analysis identifies actions individuals could take to improve outcomes from automated decisions.
Semantics at Scale: A Distributional ApproachAndre Freitas
1) The document discusses using distributional semantics to build robust semantic models that can handle large amounts of data and enable semantic computing at scale.
2) It describes how distributional semantic models can be used to represent word meanings based on their linguistic contexts, allowing semantic knowledge bases to be automatically constructed from large text corpora.
3) The author proposes a schema-agnostic approach using distributional semantics to enable querying databases without prior knowledge of schemas, addressing problems of vocabulary and structural differences between queries and data.
On the Semantic Mapping of Schema-agnostic Queries: A Preliminary StudyAndre Freitas
The growing size, heterogeneity and complexity of databases
demand the creation of strategies to facilitate users and systems to consume
data. Ideally, query mechanisms should be schema-agnostic or
vocabulary-independent, i.e. they should be able to match user queries
in their own vocabulary and syntax to the data, abstracting data consumers
from the representation of the data. Despite being a central requirement across natural language interfaces and entity search, there is a lack on the conceptual analysis of schema-agnosticism and on the associated semantic differences between queries and databases. This work aims at providing an initial conceptualization for schema-agnostic queries aiming at providing a fine-grained classification which can support the scoping, evaluation and development of semantic matching approaches for schema-agnostic queries.
Interactive Analysis of Word Vector Embeddingsgleicher
Word vector embeddings present challenges for interactive analysis due to their high-dimensional nature and complex relationships between words. The authors conducted a task analysis of common uses of word embeddings which revealed 7 linguistic tasks. They designed 3 visualizations - Buddy Plots, Concept Axis Plots, and Co-occurrence Matrices - to support the tasks of understanding word similarities, co-occurrences, and semantic directions within concept axes. An online system implements the visualizations to enable interactive exploration of word vector embeddings.
This document summarizes key aspects of experimental design and methods for coding texts from media research. It discusses the components of experimental design including causality, theory, control, and ecological validity. It also outlines different types of intentionalities to analyze in media texts, such as those of the author, text, audience, and interpreter. Finally, it provides guidance on coding texts by establishing the texts of interest, analytical approach, unit of analysis, coding scheme, and analysis.
The document provides a syllabus for the MS and PhD entrance test for the School of Information Technology at IIT Kharagpur. It outlines the topics that will be covered in the test, including basic mathematics, programming basics, computing systems basics, and logical reasoning. The mathematics section covers topics like set theory, matrices, combinatorics, and probability and statistics. The programming section outlines basic concepts like decision structures, recursion, data structures, searching and sorting. The computing systems section covers computer organization, networking, databases, and operating systems. Logical reasoning assesses verbal reasoning through puzzles and non-verbal reasoning through pattern perception and rule detection puzzles.
How hard is this Query? Measuring the Semantic Complexity of Schema-agnostic ...Andre Freitas
The growing size, heterogeneity and complexity of databases demand the creation of strategies to facilitate users and systems to consume data. Ideally, query mechanisms should be schema-agnostic, i.e. they should be able to match user queries in their own vocabulary and syntax to the data, abstracting data consumers from the representation of the data. This work provides an informationtheoretical framework to evaluate the semantic complexity involved in the query-database communication, under a schema-agnostic query scenario. Different entropy measures are introduced to quantify the semantic phenomena involved in the user-database communication, including structural complexity, ambiguity, synonymy and vagueness. The entropy measures are validated using natural language queries over Semantic Web databases. The analysis of the semantic complexity is used to improve the understanding of the core semantic dimensions present at the query-data matching process, allowing the improvement of the design of schema-agnostic query mechanisms and defining measures which can be used to assess the semantic uncertainty or difficulty behind a schema-agnostic querying task.
Get hands-on with Explainable AI at Machine Learning Interpretability(MLI) Gym!Sri Ambati
This meetup took place in Mountain View on January 24th, 2019.
Description:
With the effort and contributions from researchers and practitioners from academia and industry, Machine Learning Interpretation has become a young sub-field of ML. However, the norms around its definition and understanding is still in its infancy and there are numerous different approaches emerging rapidly. However, there seems to be a lack of a consistent explanation framework to evaluate and consistently benchmark different algorithms - evaluating against interpretation, completeness and consistency of the algorithms.
The idea with the gym is to provide a controlled interactive environment for all forms of Machine Learning algorithms, - initially focusing on supervised predictive modeling problems, to allow analysts and data-scientists to explore, debug and generate insightful understanding of the models by
1.Model Validation: Ways to explore and validate black box ML systems enabling model comparison both globally and locally - identifying biases in the training data through interpretation.
2.What-if Analysis: An interactive environment where communication can happen i.e. enable learning through interactions. User having the ability to conduct "What-If" analysis - effect of single or multiple features and their interactions
3.Model Debugging: Ways to analyze the misbehavior of the model by exploring counterfactual examples(adversarial examples and training)
4. Interpretable Models: Ability to build natively interpretable models - with the goal to simplify complex models to enable better understanding.
The central concept with MLI gym is to have an interactive environment where one could explore and simulate variations in the world(a world post a model is operationalized) beyond the defined model metrics point estimates - e.g. ROC-AUC, confusion matrix, RMSE, R2 score and others.
Speaker's Bio:
Pramit is a Lead Data Scientist/ at H2O.ai. His area of interests is building Statistical/Machine Learning models(Bayesian and Frequentist Modeling techniques) to help the business realize their data-driven goals.
Currently, he is exploring "Model Interpretation" as means to efficiently understand the true nature of predictive models to enable model robustness and security. He believes effective Model Inference coupled with Adversarial training could lead to building trustworthy models with known blind spots. He has started an open source project Skater: https://github.com/datascienceinc/Skater to solve the need for Model Inference(The project is still in its early stages of development but check it out, always eager for feedback)
Interpretable machine learning : Methods for understanding complex modelsManojit Nandi
1. Interpretability helps understand complex machine learning models by explaining their outcomes based on inputs. Higher predictive accuracy often reduces interpretability.
2. Methods like LIME and SHAP attribute model outcomes to input features through local surrogate models and game theory.
3. Recourse analysis identifies actions individuals could take to improve outcomes from automated decisions.
Semantics at Scale: A Distributional ApproachAndre Freitas
1) The document discusses using distributional semantics to build robust semantic models that can handle large amounts of data and enable semantic computing at scale.
2) It describes how distributional semantic models can be used to represent word meanings based on their linguistic contexts, allowing semantic knowledge bases to be automatically constructed from large text corpora.
3) The author proposes a schema-agnostic approach using distributional semantics to enable querying databases without prior knowledge of schemas, addressing problems of vocabulary and structural differences between queries and data.
On the Semantic Mapping of Schema-agnostic Queries: A Preliminary StudyAndre Freitas
The growing size, heterogeneity and complexity of databases
demand the creation of strategies to facilitate users and systems to consume
data. Ideally, query mechanisms should be schema-agnostic or
vocabulary-independent, i.e. they should be able to match user queries
in their own vocabulary and syntax to the data, abstracting data consumers
from the representation of the data. Despite being a central requirement across natural language interfaces and entity search, there is a lack on the conceptual analysis of schema-agnosticism and on the associated semantic differences between queries and databases. This work aims at providing an initial conceptualization for schema-agnostic queries aiming at providing a fine-grained classification which can support the scoping, evaluation and development of semantic matching approaches for schema-agnostic queries.
Schema-agnositc queries over large-schema databases: a distributional semanti...Andre Freitas
This document provides an overview and summary of André Freitas' PhD thesis defense presentation on schema-agnostic queries for large schema databases using distributional semantics. The presentation motivates the need for schema-agnostic queries due to the rise of very large and dynamic database schemas. It proposes using distributional semantics to provide an accurate, comprehensive and low maintenance approach to cope with semantic heterogeneity in schema-agnostic queries. The key aspects of the approach include semantic pivoting to reduce semantic complexity, distributional semantic models to enable semantic matching, and a hybrid distributional-relational semantic model called τ-Space to support the development of a schema-agnostic query mechanism.
An Introduction to XAI! Towards Trusting Your ML Models!Mansour Saffar
The document provides an overview of explainable AI (XAI). It discusses how XAI helps interpret machine learning models by explaining why and how they work. It outlines different categories of ML models and challenges around accuracy vs interpretability. The document then describes various XAI techniques like LIME, SHAP and global/local explanations that help address issues like trust, bias and fairness. Examples show how XAI tools can explain predictions for tasks like image classification.
Scott Wen-Tau Yih will give a talk titled "Learning with Integer Linear Programming Inference for Constrained Output". The talk will first demonstrate how constraints can be incorporated into conditional random fields using a novel inference approach based on integer linear programming. This allows CRF models to efficiently support general constraint structures. Experimental results will be provided for semantic role labeling. The second part will compare simple learning plus inference to inference based training, finding the latter is superior when local classifiers are difficult but requires more examples to show differences.
Cmaps as intellectual prosthesis (GERAS 34, Paris)Lawrie Hunter
The document describes a case study using concept maps (Cmaps) to help EAP students improve their academic writing skills. The students mapped the introduction section of a research paper under constraints. They then critiqued their maps and created a consensus map. Based only on the consensus map, the students rewrote the introduction section. The students found that cycling between mapping and text analysis helped them better understand the paper's structure and argument. The case study suggests Cmaps are useful instructional tools, especially for identifying rhetorical structure in difficult texts.
A Simple Guide to the Item Response Theory (IRT) and Rasch ModelingOpenThink Labs
This document, which is a practical introduction to Item Response Theory (IRT) and Rasch modeling, is
composed of five parts:
I. Item calibration and ability estimation
II. Item Characteristic Curve in one to three parameter models
III. Item Information Function and Test Information Function
IV. Item-Person Map
V. Misfit
Automated Machine Learning and eXplainable Artificial Intelligence are disruptive technologies in Data Science. Here I briefly introduce them and show how DALEXverse may be used in better model development.
Ontology based approach for annotating a corpus of computer science abstractsZainab Almugbel
This presentation was presented in ICCIS 2019 conference. It tacks the issue of searching massive number of papers by answering two questions: what to represent and how to represent
This document provides an overview of the topics covered in a discrete structures course, including logic, sets, relations, functions, sequences, recurrence relations, combinatorics, probability, and graphs. It defines discrete mathematics as the study of mathematical structures that have distinct, separated values rather than varying continuously. Some examples given are problems involving a fixed number of islands/bridges or connecting a set number of cities with telephone lines. Logic is introduced as the study of valid vs. invalid arguments, and basic logical concepts like statements, truth values, compound statements, logical connectives, negation, and truth tables are outlined.
This document summarizes a workshop on data integration using ontologies. It discusses how data integration is challenging due to differences in schemas, semantics, measurements, units and labels across data sources. It proposes that ontologies can help with data integration by providing definitions for schemas and entities referred to in the data. Core challenges discussed include dealing with multiple synonyms for entities and relationships between biological entities that depend on context. The document advocates for shared community ontologies that can be extended and integrated to facilitate flexible and responsive data integration across multiple sources.
Explore, Explain, and Debug aka Interpretable Machine LearningPrzemek Biecek
The document discusses the importance of interpretability and explainability in machine learning models. It provides examples of how "black box" algorithms can have harmful and unsafe outcomes when used without understanding how they work. It advocates for techniques that allow humans to explore model predictions, understand how variables contribute to outcomes, and debug models when needed. These types of interpretable machine learning approaches will change how predictive models are developed and used.
This was presented at the London Artificial Intelligence & Deep Learning Meetup.
https://www.meetup.com/London-Artificial-Intelligence-Deep-Learning/events/245251725/
Enjoy the recording: https://youtu.be/CY3t11vuuOM.
- - -
Kasia discussed complexities of interpreting black-box algorithms and how these may affect some industries. She presented the most popular methods of interpreting Machine Learning classifiers, for example, feature importance or partial dependence plots and Bayesian networks. Finally, she introduced Local Interpretable Model-Agnostic Explanations (LIME) framework for explaining predictions of black-box learners – including text- and image-based models - using breast cancer data as a specific case scenario.
Kasia Kulma is a Data Scientist at Aviva with a soft spot for R. She obtained a PhD (Uppsala University, Sweden) in evolutionary biology in 2013 and has been working on all things data ever since. For example, she has built recommender systems, customer segmentations, predictive models and now she is leading an NLP project at the UK’s leading insurer. In spare time she tries to relax by hiking & camping, but if that doesn’t work ;) she co-organizes R-Ladies meetups and writes a data science blog R-tastic (https://kkulma.github.io/).
https://www.linkedin.com/in/kasia-kulma-phd-7695b923/
Deciphering AI - Unlocking the Black Box of AIML with State-of-the-Art Techno...Analytics India Magazine
Most organizations understand the predictive power and the potential gains from AIML, but AI and ML are still now a black box technology for them. While deep learning and neural networks can provide excellent inputs to businesses, leaders are challenged to use them because of the complete blind faith required to ‘trust’ AI. In this talk we will use the latest technological developments from researchers, the US defense department, and the industry to unbox the black box and provide businesses a clear understanding of the policy levers that they can pull, why, and by how much, to make effective decisions?
Presented at #H2OWorld 2017 in Mountain View, CA.
Enjoy the video: https://youtu.be/TBJqgvXYhfo.
Learn more about H2O.ai: https://www.h2o.ai/.
Follow @h2oai: https://twitter.com/h2oai.
- - -
Abstract:
Machine learning is at the forefront of many recent advances in science and technology, enabled in part by the sophisticated models and algorithms that have been recently introduced. However, as a consequence of this complexity, machine learning essentially acts as a black-box as far as users are concerned, making it incredibly difficult to understand, predict, or "trust" their behavior. In this talk, I will describe our research on approaches that explain the predictions of ANY classifier in an interpretable and faithful manner.
Sameer's Bio:
Dr. Sameer Singh is an Assistant Professor of Computer Science at the University of California, Irvine. He is working on large-scale and interpretable machine learning applied to natural language processing. Sameer was a Postdoctoral Research Associate at the University of Washington and received his PhD from the University of Massachusetts, Amherst, during which he also worked at Microsoft Research, Google Research, and Yahoo! Labs on massive-scale machine learning. He was awarded the Adobe Research Data Science Faculty Award, was selected as a DARPA Riser, won the grand prize in the Yelp dataset challenge, and received the Yahoo! Key Scientific Challenges fellowship. Sameer has published extensively at top-tier machine learning and natural language processing conferences. (http://sameersingh.org)
The document summarizes and compares schema matching and ontology mapping. It discusses how schema matching approaches can be applied to ontology mapping given the similarities between schemas and ontologies. The document outlines different categories of schema matching techniques (element-based, structure-based) and provides examples. It also summarizes several ontology mapping tools and approaches that utilize different matching strategies like string, structure, and semantic similarity.
Automated Education Propositional Logic Tool (AEPLT): Used For Computation in...CSCJournals
This document describes an Automated Education Propositional Logic Tool (AEPLT) that was designed to simplify and automate the calculation of propositional logic for compound propositions involving conjunction, disjunction, conditional, and bi-conditional statements. The AEPLT has an architecture that allows a user to enter propositional variables, which are then mapped to logical connectives by a parser to form formulas. These formulas are evaluated against assumption statements to determine truth values, which are recorded in a truth table. The tool is intended to provide students with accurate results in a user-friendly interface, avoiding human errors that can occur in manual calculations.
The document summarizes a talk given by Valeria de Paiva on intuitionistic modal logic 15 years after its initial development. It discusses the early work developing systems of intuitionistic modal logic like Constructive S4 and their proof theories. It also describes the formation of the Intuitionistic Modal Logic and Applications association aimed at bringing together researchers from different fields to share tools and information. However, the goal of this association being fully realized, with communities still largely talking past each other, is assessed as not having been attained so far.
How the philosophy of mathematical practice can be logic by other means (bris...Brendan Larvor
The document discusses the author's view that informal proofs in mathematics depend on both logical form and content. The author argues that logic should be understood as the study of inferential actions, which can incorporate content and representations. This broader view of logic facilitates connecting logical questions about rigor to the study of mathematical cultures and practices, since logical constraints are enacted as cultural norms. The author claims this approach is needed to address shortcomings in using formal logic to model mathematical proof and to utilize studies of specific mathematical practices.
This document summarizes a research paper that proposes a new representation for relational learning that allows the use of propositional learning algorithms. The paper argues that traditional inductive logic programming (ILP) approaches have limitations like intractability and inefficiency. It presents a representation using a restricted first-order logic and graph structures that can be converted to propositions, enabling the use of propositional and probabilistic learning algorithms. An information extraction system using this approach achieved better performance than other ILP-based systems. The paper contributes a new paradigm for relational learning but did not fully analyze the contributions of its two-stage architecture.
invited talk in the ExUM workshop in the UMAP 2022 conference
abstract:
Explainability has become an important topic both in Data Science and AI in general and in recommender systems in particular, as algorithms have become much less inherently explainable. However, explainability has different interpretations and goals in different fields. For example, interpretability and explanainability tools in machine learning are predominantly developed for Data Scientists to understand and scrutinize their models. Current tools are therefore often quite technical and not very ‘user-friendly’. I will illustrate this with our recent work on improving the explainability of model-agnostic tools such as LIME and SHAP. Another stream of research on explainability in the HCI and XAI fields focuses more on users’ needs for explainability, such as contrastive and selective explanations and explanations that fit with the mental models and beliefs of the user. However, how to satisfy those needs is still an open question. Based on recent work in interactive AI and machine learning, I will propose that explainability goes together with interactivity, and will illustrate this with examples from our own work in music genre exploration, that combines visualizations and interactive tools to help users understand and tune our exploration model.
Schema-agnositc queries over large-schema databases: a distributional semanti...Andre Freitas
This document provides an overview and summary of André Freitas' PhD thesis defense presentation on schema-agnostic queries for large schema databases using distributional semantics. The presentation motivates the need for schema-agnostic queries due to the rise of very large and dynamic database schemas. It proposes using distributional semantics to provide an accurate, comprehensive and low maintenance approach to cope with semantic heterogeneity in schema-agnostic queries. The key aspects of the approach include semantic pivoting to reduce semantic complexity, distributional semantic models to enable semantic matching, and a hybrid distributional-relational semantic model called τ-Space to support the development of a schema-agnostic query mechanism.
An Introduction to XAI! Towards Trusting Your ML Models!Mansour Saffar
The document provides an overview of explainable AI (XAI). It discusses how XAI helps interpret machine learning models by explaining why and how they work. It outlines different categories of ML models and challenges around accuracy vs interpretability. The document then describes various XAI techniques like LIME, SHAP and global/local explanations that help address issues like trust, bias and fairness. Examples show how XAI tools can explain predictions for tasks like image classification.
Scott Wen-Tau Yih will give a talk titled "Learning with Integer Linear Programming Inference for Constrained Output". The talk will first demonstrate how constraints can be incorporated into conditional random fields using a novel inference approach based on integer linear programming. This allows CRF models to efficiently support general constraint structures. Experimental results will be provided for semantic role labeling. The second part will compare simple learning plus inference to inference based training, finding the latter is superior when local classifiers are difficult but requires more examples to show differences.
Cmaps as intellectual prosthesis (GERAS 34, Paris)Lawrie Hunter
The document describes a case study using concept maps (Cmaps) to help EAP students improve their academic writing skills. The students mapped the introduction section of a research paper under constraints. They then critiqued their maps and created a consensus map. Based only on the consensus map, the students rewrote the introduction section. The students found that cycling between mapping and text analysis helped them better understand the paper's structure and argument. The case study suggests Cmaps are useful instructional tools, especially for identifying rhetorical structure in difficult texts.
A Simple Guide to the Item Response Theory (IRT) and Rasch ModelingOpenThink Labs
This document, which is a practical introduction to Item Response Theory (IRT) and Rasch modeling, is
composed of five parts:
I. Item calibration and ability estimation
II. Item Characteristic Curve in one to three parameter models
III. Item Information Function and Test Information Function
IV. Item-Person Map
V. Misfit
Automated Machine Learning and eXplainable Artificial Intelligence are disruptive technologies in Data Science. Here I briefly introduce them and show how DALEXverse may be used in better model development.
Ontology based approach for annotating a corpus of computer science abstractsZainab Almugbel
This presentation was presented in ICCIS 2019 conference. It tacks the issue of searching massive number of papers by answering two questions: what to represent and how to represent
This document provides an overview of the topics covered in a discrete structures course, including logic, sets, relations, functions, sequences, recurrence relations, combinatorics, probability, and graphs. It defines discrete mathematics as the study of mathematical structures that have distinct, separated values rather than varying continuously. Some examples given are problems involving a fixed number of islands/bridges or connecting a set number of cities with telephone lines. Logic is introduced as the study of valid vs. invalid arguments, and basic logical concepts like statements, truth values, compound statements, logical connectives, negation, and truth tables are outlined.
This document summarizes a workshop on data integration using ontologies. It discusses how data integration is challenging due to differences in schemas, semantics, measurements, units and labels across data sources. It proposes that ontologies can help with data integration by providing definitions for schemas and entities referred to in the data. Core challenges discussed include dealing with multiple synonyms for entities and relationships between biological entities that depend on context. The document advocates for shared community ontologies that can be extended and integrated to facilitate flexible and responsive data integration across multiple sources.
Explore, Explain, and Debug aka Interpretable Machine LearningPrzemek Biecek
The document discusses the importance of interpretability and explainability in machine learning models. It provides examples of how "black box" algorithms can have harmful and unsafe outcomes when used without understanding how they work. It advocates for techniques that allow humans to explore model predictions, understand how variables contribute to outcomes, and debug models when needed. These types of interpretable machine learning approaches will change how predictive models are developed and used.
This was presented at the London Artificial Intelligence & Deep Learning Meetup.
https://www.meetup.com/London-Artificial-Intelligence-Deep-Learning/events/245251725/
Enjoy the recording: https://youtu.be/CY3t11vuuOM.
- - -
Kasia discussed complexities of interpreting black-box algorithms and how these may affect some industries. She presented the most popular methods of interpreting Machine Learning classifiers, for example, feature importance or partial dependence plots and Bayesian networks. Finally, she introduced Local Interpretable Model-Agnostic Explanations (LIME) framework for explaining predictions of black-box learners – including text- and image-based models - using breast cancer data as a specific case scenario.
Kasia Kulma is a Data Scientist at Aviva with a soft spot for R. She obtained a PhD (Uppsala University, Sweden) in evolutionary biology in 2013 and has been working on all things data ever since. For example, she has built recommender systems, customer segmentations, predictive models and now she is leading an NLP project at the UK’s leading insurer. In spare time she tries to relax by hiking & camping, but if that doesn’t work ;) she co-organizes R-Ladies meetups and writes a data science blog R-tastic (https://kkulma.github.io/).
https://www.linkedin.com/in/kasia-kulma-phd-7695b923/
Deciphering AI - Unlocking the Black Box of AIML with State-of-the-Art Techno...Analytics India Magazine
Most organizations understand the predictive power and the potential gains from AIML, but AI and ML are still now a black box technology for them. While deep learning and neural networks can provide excellent inputs to businesses, leaders are challenged to use them because of the complete blind faith required to ‘trust’ AI. In this talk we will use the latest technological developments from researchers, the US defense department, and the industry to unbox the black box and provide businesses a clear understanding of the policy levers that they can pull, why, and by how much, to make effective decisions?
Presented at #H2OWorld 2017 in Mountain View, CA.
Enjoy the video: https://youtu.be/TBJqgvXYhfo.
Learn more about H2O.ai: https://www.h2o.ai/.
Follow @h2oai: https://twitter.com/h2oai.
- - -
Abstract:
Machine learning is at the forefront of many recent advances in science and technology, enabled in part by the sophisticated models and algorithms that have been recently introduced. However, as a consequence of this complexity, machine learning essentially acts as a black-box as far as users are concerned, making it incredibly difficult to understand, predict, or "trust" their behavior. In this talk, I will describe our research on approaches that explain the predictions of ANY classifier in an interpretable and faithful manner.
Sameer's Bio:
Dr. Sameer Singh is an Assistant Professor of Computer Science at the University of California, Irvine. He is working on large-scale and interpretable machine learning applied to natural language processing. Sameer was a Postdoctoral Research Associate at the University of Washington and received his PhD from the University of Massachusetts, Amherst, during which he also worked at Microsoft Research, Google Research, and Yahoo! Labs on massive-scale machine learning. He was awarded the Adobe Research Data Science Faculty Award, was selected as a DARPA Riser, won the grand prize in the Yelp dataset challenge, and received the Yahoo! Key Scientific Challenges fellowship. Sameer has published extensively at top-tier machine learning and natural language processing conferences. (http://sameersingh.org)
The document summarizes and compares schema matching and ontology mapping. It discusses how schema matching approaches can be applied to ontology mapping given the similarities between schemas and ontologies. The document outlines different categories of schema matching techniques (element-based, structure-based) and provides examples. It also summarizes several ontology mapping tools and approaches that utilize different matching strategies like string, structure, and semantic similarity.
Automated Education Propositional Logic Tool (AEPLT): Used For Computation in...CSCJournals
This document describes an Automated Education Propositional Logic Tool (AEPLT) that was designed to simplify and automate the calculation of propositional logic for compound propositions involving conjunction, disjunction, conditional, and bi-conditional statements. The AEPLT has an architecture that allows a user to enter propositional variables, which are then mapped to logical connectives by a parser to form formulas. These formulas are evaluated against assumption statements to determine truth values, which are recorded in a truth table. The tool is intended to provide students with accurate results in a user-friendly interface, avoiding human errors that can occur in manual calculations.
The document summarizes a talk given by Valeria de Paiva on intuitionistic modal logic 15 years after its initial development. It discusses the early work developing systems of intuitionistic modal logic like Constructive S4 and their proof theories. It also describes the formation of the Intuitionistic Modal Logic and Applications association aimed at bringing together researchers from different fields to share tools and information. However, the goal of this association being fully realized, with communities still largely talking past each other, is assessed as not having been attained so far.
How the philosophy of mathematical practice can be logic by other means (bris...Brendan Larvor
The document discusses the author's view that informal proofs in mathematics depend on both logical form and content. The author argues that logic should be understood as the study of inferential actions, which can incorporate content and representations. This broader view of logic facilitates connecting logical questions about rigor to the study of mathematical cultures and practices, since logical constraints are enacted as cultural norms. The author claims this approach is needed to address shortcomings in using formal logic to model mathematical proof and to utilize studies of specific mathematical practices.
This document summarizes a research paper that proposes a new representation for relational learning that allows the use of propositional learning algorithms. The paper argues that traditional inductive logic programming (ILP) approaches have limitations like intractability and inefficiency. It presents a representation using a restricted first-order logic and graph structures that can be converted to propositions, enabling the use of propositional and probabilistic learning algorithms. An information extraction system using this approach achieved better performance than other ILP-based systems. The paper contributes a new paradigm for relational learning but did not fully analyze the contributions of its two-stage architecture.
invited talk in the ExUM workshop in the UMAP 2022 conference
abstract:
Explainability has become an important topic both in Data Science and AI in general and in recommender systems in particular, as algorithms have become much less inherently explainable. However, explainability has different interpretations and goals in different fields. For example, interpretability and explanainability tools in machine learning are predominantly developed for Data Scientists to understand and scrutinize their models. Current tools are therefore often quite technical and not very ‘user-friendly’. I will illustrate this with our recent work on improving the explainability of model-agnostic tools such as LIME and SHAP. Another stream of research on explainability in the HCI and XAI fields focuses more on users’ needs for explainability, such as contrastive and selective explanations and explanations that fit with the mental models and beliefs of the user. However, how to satisfy those needs is still an open question. Based on recent work in interactive AI and machine learning, I will propose that explainability goes together with interactivity, and will illustrate this with examples from our own work in music genre exploration, that combines visualizations and interactive tools to help users understand and tune our exploration model.
Statistical Modeling in 3D: Describing, Explaining and PredictingGalit Shmueli
This document discusses statistical modeling approaches for explaining, predicting, and describing. It notes that explanatory modeling focuses on testing causal hypotheses, predictive modeling focuses on predicting new observations, and descriptive modeling approximates distributions or relationships. The document argues that these goals are different and the best model for one purpose is not necessarily best for another. It cautions against conflating explanation and prediction, and notes that explanatory power does not necessarily indicate predictive power or vice versa. The document examines differences in how data is approached and models are designed and evaluated for these different purposes.
To Explain, To Predict, or To Describe?Galit Shmueli
1) The document discusses the differences between explanatory, predictive, and descriptive modeling and evaluation. Explanatory modeling tests causal hypotheses, predictive modeling predicts new observations, and descriptive modeling approximates distributions or relationships.
2) It notes that these goals are different and the same model is not best for all three. Social sciences often focus on explanation while machine learning focuses on prediction.
3) The key aspects that differ for these three types of modeling are the theory, causation versus association, retrospective versus prospective analysis, and focusing on the average unit versus individual units. The best model for one goal is not necessarily best for the others.
ML-Aided Simulation: A Conceptual Framework for Integrating Simulation Models...Mahmoud Elbattah
ML-Aided Simulation: A Conceptual Framework for Integrating Simulation Models with Machine Learning
Paper presented at ACM 2018 Conference on Principles of Advanced Discrete Simulation (PADS)
https://dl.acm.org/citation.cfm?id=3200933
Authors:
Mahmoud Elbattah and Owen Molloy
National University of Ireland Galway
Short Story: Unveiling the Reasoning Abilities of Large Language Models by Ke...kellynguyen01
Diving deep into “Towards Reasoning in Large Language Models: A Survey”, a survey paper written by Jie Huang and Kevin Chen-Chuan Chang at the University of Illinois at Urbana-Champaign in 2023
Presents Natural Language Processing (NLP) algorithms for for Bay Area NLP reading group. Survey of Probabilistic Topic Modeling such as Latent Dirichlet Allocation (LDA). Includes practical references explaining the algorithm along with software libraries for Python, Spark, and R.
The effect of number of concepts on readability of schemas 2Saman Sara
The document describes an empirical study that investigated the effect of the number of concepts (NOC) on the readability of entity-relationship model (ERM) schemas. Two ERMs with different NOC (base 5 concepts vs. higher 8 concepts) were tested on subjects. Results found that higher NOC models led to more accurate understanding but took more time to comprehend, and improved learnability of concepts over time, partially supporting the hypotheses. The study provided empirical validation on how NOC impacts readability dimensions of ERM schemas.
The document describes a comparative study of various machine learning and neural network models for detecting abusive language on Twitter. It finds that a bidirectional GRU network trained on word-level features, with a Latent Topic Clustering module, achieves the most accurate results with an F1 score of 0.805 for detecting abusive tweets. Additionally, it explores using context tweets as additional features and finds this improves some models' performance.
Tips for Scale Development: Evaluating Automatic PersonasJoni Salminen
This document discusses research on automatically generating persona profiles from online data. It describes an Automatic Persona Generation (APG) system that aims to computationally analyze vast amounts of online data to discover useful representations of personas. Various techniques are discussed for different aspects of persona generation, including information architecture, commenting analysis, profile picture generation, topic classification, and temporal analysis of how personas change over time. It also discusses challenges in evaluating generated personas, both in terms of objective accuracy and subjective user perceptions. The document provides tips and guidelines for developing a persona perception scale to systematically measure how users view automatically generated personas.
Usage of AI and machine learning models is likely to become more commonplace as larger swaths of the economy embrace automation and data-driven decision-making. While these predictive systems can be quite accurate, they have been treated as inscrutable black boxes in the past, that produce only numeric predictions with no accompanying explanations. Unfortunately, recent studies and recent events have drawn attention to mathematical and sociological flaws in prominent weak AI and ML systems, but practitioners usually don’t have the right tools to pry open machine learning black-boxes and debug them.
This presentation introduces several new approaches to that increase transparency, accountability, and trustworthiness in machine learning models. If you are a data scientist or analyst and you want to explain a machine learning model to your customers or managers (or if you have concerns about documentation, validation, or regulatory requirements), then this presentation is for you!
Big Data - To Explain or To Predict? Talk at U Toronto's Rotman School of Ma...Galit Shmueli
Slide from Prof. Galit Shmueli's talk at University of Toronto's Rotman School of Management, March 4, 2016. This talk is part of Rotman's Big Data Expert Speaker Series.
https://www.rotman.utoronto.ca/ProfessionalDevelopment/Events/UpcomingEvents/20160304GalitShmueli.aspx
Evolving Swings (topics) from Social Streams using Probability ModelIJERA Editor
This document presents a probability model for detecting evolving topics from social media streams. It focuses on the social aspects of posts reflected in user mentioning behavior, rather than textual content. The model captures the number of mentions per post and frequency of mentioned users. It analyzes individual posting anomalies and aggregates anomaly scores. SDNML change point analysis and burst detection are then used to identify evolving topics from the aggregated scores. Experimental results show that link-based detection using this approach performs better than key-based detection using textual content alone. The model overcomes limitations of prior frequency-based approaches and can detect topics from both textual and non-textual social media posts.
UNIT V TEXT AND OPINION MINING
Text Mining in Social Networks -Opinion extraction – Sentiment classification and clustering -
Temporal sentiment analysis - Irony detection in opinion mining - Wish analysis – Product review mining – Review Classification – Tracking sentiments towards topics over time
A Categorisation of Post-hoc Explanations for Predictive ModelsJane Dane
This work is highly influenced by work previously completed by Zachary Lipton in Mythos of Model Interpretability. Essentially we are arguing that as long as there's no consensus and formal standardisation of what people mean by interpretability it will prevent us from having a pragmatic and influential progress in this direction. At the time of the presentation there was no consesus on validation metrics, datasets or methodologies to evaluate and compare interpretability methods in the literature. We highly emphasised the need of an axiomatic and formal approach relating to earlier efforts in interpretability in fuzzy systems in order to enforce the healthy habit of thinking about formal definitions and standardisations.
GDG Cloud Southlake #17: Meg Dickey-Kurdziolek: Explainable AI is for EveryoneJames Anderson
If Artificial Intelligence (AI) is a black-box, how can a human comprehend and trust the results of Machine Learning (ML) alogrithms? Explainable AI (XAI) tries to shed light into that AI black-box so humans can trust what is going on. Our speaker Meg Dickey-Kurdziolek is currently a UX Researcher for Google Cloud AI and Industry Solutions, where she focuses her research on Explainable AI and Model Understanding. Recording of the presentation: https://youtu.be/6N2DNN_HDWU
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Rsqrd AI: Recent Advances in Explainable Machine Learning Research
1. Recent advances in
explainable machine
learning research
Bernease Herman, Research Scientist
University of Washington eScience Institute and
Paul G. Allen School of Computer Science & Engineering
June 6, 2019 - Allen Institute for Artificial Intelligence
2. Recent advances in
explainable machine
learning research
Bernease Herman, Research Scientist
University of Washington eScience Institute and
Paul G. Allen School of Computer Science & Engineering
June 6, 2019 - Allen Institute for Artificial Intelligence
Speed run edition
9. Defining model explainability
No single definition within community.
“component of interpretable modeling process informing
how model works in understandable form” - Me
“process of giving explanations [of ML] to humans”
- Kim & Doshi-Valez 2017
to left, formal definition (I)
that extends beyond humans
- Dhurandhar et al. 2017
10. Linear regression models
(with a certain number of parameters)
Decision trees (or similar)
(with a certain depth/number of parameters)
Text explanations
Visualizations (e.g., saliency maps)
more
Explanations come in many forms
12. from “The Mythos of Model Interpretability”, Lipton 2016
1. Simulatability, comprehend the entire model
at once; model complexity
2. Decomposability, comprehend the individual
components/parameters; intelligibility [2]
3. Algorithmic transparency, comprehend
the algorithm behavior; loss surface and randomness
Three levels of transparency
14. Interpretable methods survey
heavily borrowed from Kim & Doshi-Valez ICML 2017 tutorial
1. Fitting new models that are
intrinsically interpretable
2. Post-hoc analysis of existing model
3. Interpretable analysis of raw data
(or model architecture)
15. Inherently interpretable models
1. Fitting new models that are
intrinsically interpretable
Decision trees, rule lists, rule sets
Generalized linear models (and feature manipulation)
Case-based methods
Sparsity-based methods
Monotonicity-based methods
Conceptual and hierarchical models
16. 1. Fitting new models that are
intrinsically interpretable.
Decision trees, rule lists, rule sets
Generalized linear models (and feature manipulation)
table above from Gehrke et al. 2012
Inherently interpretable models
17. figure above from Gupta et al. 2016
Monotonicity constraints
Conceptual and hierarchical models
Inherently interpretable models
18. 2. Post-hoc analysis of existing model
Sensitivity analysis
Surrogate models
Gradient-based methods
Hidden layer investigations
Post-hoc for existing models
24. 3. Interpretable analysis of raw data
Visualization
Variable Importance
Partial dependence plots
Correlation analysis
Interpretable raw data analysis
27. Explanations can be persuasive
Lipton 2016, Herman 2017
When tailoring our model explanations to
human preferences and judges, our models
may learn to prioritize persuasive
explanations over introspective ones.
31. How AI detectives are cracking open the black box of
deep learning, Science Magazine, July 2017
Background reading
32. Ideas on interpreting machine learning,
O’Reilly Ideas, March 2017
Background reading
33. Thank you!
Let’s discuss this more.
Bernease Herman
bernease@uw.edu
@bernease on Twitter, Github, MSDSE Slack, everything
34. Splitting model form from simplicity
Herman 2017
Simultaneously
coerced into suitable
model form
(e.g., decision tree)
and reduced in
complexity
(e.g., model size).
Difficult to evaluate
across complexity
preferences.
35. Splitting model form from simplicity
Herman 2017
Keeps model form
and reduction of
complexity separate.
Improves evaluation
and adaptability.