From 908f12a36be25d42d6098aa4fb0b08076b85a2c1 Mon Sep 17 00:00:00 2001 From: AL-JiongYang Date: Sat, 25 May 2024 12:31:16 +0800 Subject: [PATCH 1/8] add news for SAT24 --- content/talk/23052024.md | 39 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 39 insertions(+) create mode 100644 content/talk/23052024.md diff --git a/content/talk/23052024.md b/content/talk/23052024.md new file mode 100644 index 0000000..7b6c6f6 --- /dev/null +++ b/content/talk/23052024.md @@ -0,0 +1,39 @@ ++++ +date = 2024-05-23T00:00:00 # Schedule page publish date. + +title='Our work on Quantum Circuit Mapping Based on Incremental and Parallel SAT Solving has been accepted to SAT 2024.
We introduce a novel circuit mapping method by combining incremental and parallel solving for Boolean Satisfiability (SAT). We present an innovative SAT encoding for circuit mapping problems, which significantly improves solver-based mapping methods and provides a smooth trade-off between compilation quality and compilation time.
Authors: Jiong Yang, Yaroslav A. Kharkov, Yunong Shi, Marijn Heule, and Bruno Dutertre
' +time_start = 2024-05-23T01:00:00 +#time_end = 2030-06-01T15:00:00 +abstract = "" +abstract_short = "" +event = "" +event_url = "" +location = "" + +# Is this a selected talk? (true/false) +selected = false + +# Projects (optional). +# Associate this talk with one or more of your projects. +# Simply enter the filename (excluding '.md') of your project file in `content/project/`. +projects = [] + +# Links (optional). +url_pdf = "" +url_slides = "" +url_video = "" +url_code = "" + +# Does the content use math formatting? +math = true + +# Does the content use source code highlighting? +highlight = true + +# Featured image +# Place your image in the `static/img/` folder and reference its filename below, e.g. `image = "example.jpg"`. +[header] +image = "" +caption = "" + ++++ From 447721c52ddcc8451713ff8f1b77583ea44ce9af Mon Sep 17 00:00:00 2001 From: AL-JiongYang Date: Sat, 25 May 2024 13:51:15 +0800 Subject: [PATCH 2/8] rebuilding site Sat May 25 13:51:14 MPST 2024 --- content/publication/AAAI23frp/cite.bib | 12 ++++++++ content/publication/AAAI23frp/index.md | 18 ++++++++++++ content/publication/IJAR23/cite.bib | 13 +++++++++ content/publication/IJAR23/index.md | 21 ++++++++++++++ content/publication/IJCAI23jt/cite.bib | 6 ++++ content/publication/IJCAI23jt/index.md | 17 +++++++++++ content/talk/19092023.md | 39 ++++++++++++++++++++++++++ 7 files changed, 126 insertions(+) create mode 100644 content/publication/AAAI23frp/cite.bib create mode 100644 content/publication/AAAI23frp/index.md create mode 100644 content/publication/IJAR23/cite.bib create mode 100644 content/publication/IJAR23/index.md create mode 100644 content/publication/IJCAI23jt/cite.bib create mode 100644 content/publication/IJCAI23jt/index.md create mode 100644 content/talk/19092023.md diff --git a/content/publication/AAAI23frp/cite.bib b/content/publication/AAAI23frp/cite.bib new file mode 100644 index 0000000..e96733b --- /dev/null +++ b/content/publication/AAAI23frp/cite.bib @@ -0,0 +1,12 @@ +@inproceedings{AAAI23frp, + author = {Xuanxiang Huang and + Yacine Izza and + Joao Marques{-}Silva}, + title = {Solving Explainability Queries with Quantification: The Case of Feature + Relevancy}, + booktitle = {Thirty-Seventh {AAAI} Conference on Artificial Intelligence, {AAAI} + 2023}, + pages = {3996--4006}, + publisher = {{AAAI} Press}, + year = {2023} +} \ No newline at end of file diff --git a/content/publication/AAAI23frp/index.md b/content/publication/AAAI23frp/index.md new file mode 100644 index 0000000..5aa8d7c --- /dev/null +++ b/content/publication/AAAI23frp/index.md @@ -0,0 +1,18 @@ +--- +abstract: "Trustable explanations of machine learning (ML) models are vital in high-risk uses of artificial intelligence (AI). Apart from the computation of trustable explanations, a number of explainability queries have been identified and studied in recent work. Some of these queries involve solving quantification problems, either in propositional or in more expressive logics. This paper investigates one of these quantification problems, namely the feature relevancy problem (FRP), i.e.\ to decide whether a (possibly sensitive) feature can occur in some explanation of a prediction. In contrast with earlier work, that studied FRP for specific classifiers, this paper proposes a novel algorithm for the FRP quantification problem which is applicable to any ML classifier that meets minor requirements. Furthermore, the paper shows that the novel algorithm is efficient in practice. The experimental results, obtained using random forests (RFs) induced from well-known publicly available datasets, demonstrate that the proposed solution outperforms existing state-of-the-art solvers for Quantified Boolean Formulas (QBF) by orders of magnitude. Finally, the paper also identifies a novel family of formulas that are challenging for currently state-of-the-art QBF solvers." + +authors: +- Xuanxiang Huang +- Yacine Izza +- Joao Marques-Silva +date: 2023-02-01 00:00:00 +highlight: true +image_preview: '' +math: true +publication: In *Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, (AAAI)* +publication_types: +- '1' +selected: true +title: 'Solving Explainability Queries with Quantification: The Case of Feature Relevancy' +url_pdf: https://ojs.aaai.org/index.php/AAAI/article/view/25514 +--- \ No newline at end of file diff --git a/content/publication/IJAR23/cite.bib b/content/publication/IJAR23/cite.bib new file mode 100644 index 0000000..6d11a85 --- /dev/null +++ b/content/publication/IJAR23/cite.bib @@ -0,0 +1,13 @@ +@article{ijar23, + author = {Yacine Izza and + Xuanxiang Huang and + Alexey Ignatiev and + Nina Narodytska and + Martin C. Cooper and + Joao Marques{-}Silva}, + title = {On computing probabilistic abductive explanations}, + journal = {Int. J. Approx. Reason.}, + volume = {159}, + pages = {108939}, + year = {2023} +} \ No newline at end of file diff --git a/content/publication/IJAR23/index.md b/content/publication/IJAR23/index.md new file mode 100644 index 0000000..9ddf9aa --- /dev/null +++ b/content/publication/IJAR23/index.md @@ -0,0 +1,21 @@ +--- +abstract: 'The most widely studied explainable AI (XAI) approaches are unsound. This is the case with well-known model-agnostic explanation approaches, and it is also the case with approaches based on saliency maps. One solution is to consider intrinsic interpretability, which does not exhibit the drawback of unsoundness. Unfortunately, intrinsic interpretability can display unwieldy explanation redundancy. Formal explainability represents the alternative to these non-rigorous approaches, with one example being PI-explanations. Unfortunately, PI-explanations also exhibit important drawbacks, the most visible of which is arguably their size. Recently, it has been observed that the (absolute) rigor of PI-explanations can be traded off for a smaller explanation size, by computing the so-called relevant sets. Given some positive {\delta}, a set S of features is {\delta}-relevant if, when the features in S are fixed, the probability of getting the target class exceeds {\delta}. However, even for very simple classifiers, the complexity of computing relevant sets of features is prohibitive, with the decision problem being NPPP-complete for circuit-based classifiers. In contrast with earlier negative results, this paper investigates practical approaches for computing relevant sets for a number of widely used classifiers that include Decision Trees (DTs), Naive Bayes Classifiers (NBCs), and several families of classifiers obtained from propositional languages. Moreover, the paper shows that, in practice, and for these families of classifiers, relevant sets are easy to compute. Furthermore, the experiments confirm that succinct sets of relevant features can be obtained for the families of classifiers considered.' + +authors: +- Yacine Izza +- Xuanxiang Huang +- Alexey Ignatiev +- Nina Narodytska +- Martin C. Cooper +- Joao Marques-Silva +date: 2023-04-01 00:00:00 +highlight: true +image_preview: '' +math: true +publication: In *Int. J. Approx. Reason. (IJAR)* +publication_types: +- '2' +selected: true +title: 'On computing probabilistic abductive explanations' +url_pdf: https://www.sciencedirect.com/science/article/abs/pii/S0888613X23000701?via%3Dihub +--- diff --git a/content/publication/IJCAI23jt/cite.bib b/content/publication/IJCAI23jt/cite.bib new file mode 100644 index 0000000..ab4761b --- /dev/null +++ b/content/publication/IJCAI23jt/cite.bib @@ -0,0 +1,6 @@ +@inproceedings{IJCAI23jt, +author={Yacine Izza, Alexey Ignatiev, Joao Marques-Silva}, +title={On Tackling Explanation Redundancy in Decision Trees}, +booktitle={Proceedings of the International Joint Conference on Artificial Intelligence, {IJCAI}}, +year={2023} +} \ No newline at end of file diff --git a/content/publication/IJCAI23jt/index.md b/content/publication/IJCAI23jt/index.md new file mode 100644 index 0000000..201bd21 --- /dev/null +++ b/content/publication/IJCAI23jt/index.md @@ -0,0 +1,17 @@ +--- +abstract: "Claims about the interpretability of decision trees can be traced back to the origins of machine learning (ML). Indeed, given some input consistent with a decision tree's path, the explanation for the resulting prediction consists of the features in that path. Moreover, a growing number of works propose the use of decision trees, and of other so-called interpretable models, as a possible solution for deploying ML models in high-risk applications. This paper overviews recent theoretical and practical results which demonstrate that for most decision trees, tree paths exhibit so-called explanation redundancy, in that logically sound explanations can often be significantly more succinct than what the features in the path dictates. More importantly, such decision tree explanations can be computed in polynomial-time, and so can be produced with essentially no effort other than traversing the decision tree. The experimental results, obtained on a large range of publicly available decision trees, support the paper's claims." + +authors: +- Yacine Izza +- Alexey Ignatiev +- Joao Marques-Silva +date: 2023-05-01 00:00:00 +highlight: true +image_preview: '' +math: true +publication: In *Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI)* +publication_types: +- '1' +selected: true +title: 'On Tackling Explanation Redundancy in Decision Trees' +--- \ No newline at end of file diff --git a/content/talk/19092023.md b/content/talk/19092023.md new file mode 100644 index 0000000..87157ed --- /dev/null +++ b/content/talk/19092023.md @@ -0,0 +1,39 @@ ++++ +date = 2023-09-19T00:00:00 # Schedule page publish date. + +title = ' Our paper on explaining the sources of bias in machine learning via influence functions has been accepted in FAccT 2023. Authors: Bishwamittra Ghosh, Debabrota Basu and Kuldeep S. Meel.
We combine explainability with fairness in machine learning, where we compute the influence of individual features and the intersectional effect of multiple features on the resulting bias of a classifier on a dataset. This allows us to have a higher granular depiction of sources of bias than earlier methods.' +time_start = 2023-09-19T00:00:00 +#time_end = 2030-06-01T15:00:00 +abstract = "" +abstract_short = "" +event = "" +event_url = "" +location = "" + +# Is this a selected talk? (true/false) +selected = false + +# Projects (optional). +# Associate this talk with one or more of your projects. +# Simply enter the filename (excluding '.md') of your project file in `content/project/`. +projects = [] + +# Links (optional). +url_pdf = "" +url_slides = "" +url_video = "" +url_code = "" + +# Does the content use math formatting? +math = true + +# Does the content use source code highlighting? +highlight = true + +# Featured image +# Place your image in the `static/img/` folder and reference its filename below, e.g. `image = "example.jpg"`. +[header] +image = "" +caption = "" + ++++ \ No newline at end of file From 8a97c221c570ce4ed681a4927b4c69b93da405eb Mon Sep 17 00:00:00 2001 From: AL-JiongYang Date: Sat, 25 May 2024 13:57:42 +0800 Subject: [PATCH 3/8] Revert "rebuilding site Sat May 25 13:51:14 MPST 2024" This reverts commit 447721c52ddcc8451713ff8f1b77583ea44ce9af. --- content/publication/AAAI23frp/cite.bib | 12 -------- content/publication/AAAI23frp/index.md | 18 ------------ content/publication/IJAR23/cite.bib | 13 --------- content/publication/IJAR23/index.md | 21 -------------- content/publication/IJCAI23jt/cite.bib | 6 ---- content/publication/IJCAI23jt/index.md | 17 ----------- content/talk/19092023.md | 39 -------------------------- 7 files changed, 126 deletions(-) delete mode 100644 content/publication/AAAI23frp/cite.bib delete mode 100644 content/publication/AAAI23frp/index.md delete mode 100644 content/publication/IJAR23/cite.bib delete mode 100644 content/publication/IJAR23/index.md delete mode 100644 content/publication/IJCAI23jt/cite.bib delete mode 100644 content/publication/IJCAI23jt/index.md delete mode 100644 content/talk/19092023.md diff --git a/content/publication/AAAI23frp/cite.bib b/content/publication/AAAI23frp/cite.bib deleted file mode 100644 index e96733b..0000000 --- a/content/publication/AAAI23frp/cite.bib +++ /dev/null @@ -1,12 +0,0 @@ -@inproceedings{AAAI23frp, - author = {Xuanxiang Huang and - Yacine Izza and - Joao Marques{-}Silva}, - title = {Solving Explainability Queries with Quantification: The Case of Feature - Relevancy}, - booktitle = {Thirty-Seventh {AAAI} Conference on Artificial Intelligence, {AAAI} - 2023}, - pages = {3996--4006}, - publisher = {{AAAI} Press}, - year = {2023} -} \ No newline at end of file diff --git a/content/publication/AAAI23frp/index.md b/content/publication/AAAI23frp/index.md deleted file mode 100644 index 5aa8d7c..0000000 --- a/content/publication/AAAI23frp/index.md +++ /dev/null @@ -1,18 +0,0 @@ ---- -abstract: "Trustable explanations of machine learning (ML) models are vital in high-risk uses of artificial intelligence (AI). Apart from the computation of trustable explanations, a number of explainability queries have been identified and studied in recent work. Some of these queries involve solving quantification problems, either in propositional or in more expressive logics. This paper investigates one of these quantification problems, namely the feature relevancy problem (FRP), i.e.\ to decide whether a (possibly sensitive) feature can occur in some explanation of a prediction. In contrast with earlier work, that studied FRP for specific classifiers, this paper proposes a novel algorithm for the FRP quantification problem which is applicable to any ML classifier that meets minor requirements. Furthermore, the paper shows that the novel algorithm is efficient in practice. The experimental results, obtained using random forests (RFs) induced from well-known publicly available datasets, demonstrate that the proposed solution outperforms existing state-of-the-art solvers for Quantified Boolean Formulas (QBF) by orders of magnitude. Finally, the paper also identifies a novel family of formulas that are challenging for currently state-of-the-art QBF solvers." - -authors: -- Xuanxiang Huang -- Yacine Izza -- Joao Marques-Silva -date: 2023-02-01 00:00:00 -highlight: true -image_preview: '' -math: true -publication: In *Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, (AAAI)* -publication_types: -- '1' -selected: true -title: 'Solving Explainability Queries with Quantification: The Case of Feature Relevancy' -url_pdf: https://ojs.aaai.org/index.php/AAAI/article/view/25514 ---- \ No newline at end of file diff --git a/content/publication/IJAR23/cite.bib b/content/publication/IJAR23/cite.bib deleted file mode 100644 index 6d11a85..0000000 --- a/content/publication/IJAR23/cite.bib +++ /dev/null @@ -1,13 +0,0 @@ -@article{ijar23, - author = {Yacine Izza and - Xuanxiang Huang and - Alexey Ignatiev and - Nina Narodytska and - Martin C. Cooper and - Joao Marques{-}Silva}, - title = {On computing probabilistic abductive explanations}, - journal = {Int. J. Approx. Reason.}, - volume = {159}, - pages = {108939}, - year = {2023} -} \ No newline at end of file diff --git a/content/publication/IJAR23/index.md b/content/publication/IJAR23/index.md deleted file mode 100644 index 9ddf9aa..0000000 --- a/content/publication/IJAR23/index.md +++ /dev/null @@ -1,21 +0,0 @@ ---- -abstract: 'The most widely studied explainable AI (XAI) approaches are unsound. This is the case with well-known model-agnostic explanation approaches, and it is also the case with approaches based on saliency maps. One solution is to consider intrinsic interpretability, which does not exhibit the drawback of unsoundness. Unfortunately, intrinsic interpretability can display unwieldy explanation redundancy. Formal explainability represents the alternative to these non-rigorous approaches, with one example being PI-explanations. Unfortunately, PI-explanations also exhibit important drawbacks, the most visible of which is arguably their size. Recently, it has been observed that the (absolute) rigor of PI-explanations can be traded off for a smaller explanation size, by computing the so-called relevant sets. Given some positive {\delta}, a set S of features is {\delta}-relevant if, when the features in S are fixed, the probability of getting the target class exceeds {\delta}. However, even for very simple classifiers, the complexity of computing relevant sets of features is prohibitive, with the decision problem being NPPP-complete for circuit-based classifiers. In contrast with earlier negative results, this paper investigates practical approaches for computing relevant sets for a number of widely used classifiers that include Decision Trees (DTs), Naive Bayes Classifiers (NBCs), and several families of classifiers obtained from propositional languages. Moreover, the paper shows that, in practice, and for these families of classifiers, relevant sets are easy to compute. Furthermore, the experiments confirm that succinct sets of relevant features can be obtained for the families of classifiers considered.' - -authors: -- Yacine Izza -- Xuanxiang Huang -- Alexey Ignatiev -- Nina Narodytska -- Martin C. Cooper -- Joao Marques-Silva -date: 2023-04-01 00:00:00 -highlight: true -image_preview: '' -math: true -publication: In *Int. J. Approx. Reason. (IJAR)* -publication_types: -- '2' -selected: true -title: 'On computing probabilistic abductive explanations' -url_pdf: https://www.sciencedirect.com/science/article/abs/pii/S0888613X23000701?via%3Dihub ---- diff --git a/content/publication/IJCAI23jt/cite.bib b/content/publication/IJCAI23jt/cite.bib deleted file mode 100644 index ab4761b..0000000 --- a/content/publication/IJCAI23jt/cite.bib +++ /dev/null @@ -1,6 +0,0 @@ -@inproceedings{IJCAI23jt, -author={Yacine Izza, Alexey Ignatiev, Joao Marques-Silva}, -title={On Tackling Explanation Redundancy in Decision Trees}, -booktitle={Proceedings of the International Joint Conference on Artificial Intelligence, {IJCAI}}, -year={2023} -} \ No newline at end of file diff --git a/content/publication/IJCAI23jt/index.md b/content/publication/IJCAI23jt/index.md deleted file mode 100644 index 201bd21..0000000 --- a/content/publication/IJCAI23jt/index.md +++ /dev/null @@ -1,17 +0,0 @@ ---- -abstract: "Claims about the interpretability of decision trees can be traced back to the origins of machine learning (ML). Indeed, given some input consistent with a decision tree's path, the explanation for the resulting prediction consists of the features in that path. Moreover, a growing number of works propose the use of decision trees, and of other so-called interpretable models, as a possible solution for deploying ML models in high-risk applications. This paper overviews recent theoretical and practical results which demonstrate that for most decision trees, tree paths exhibit so-called explanation redundancy, in that logically sound explanations can often be significantly more succinct than what the features in the path dictates. More importantly, such decision tree explanations can be computed in polynomial-time, and so can be produced with essentially no effort other than traversing the decision tree. The experimental results, obtained on a large range of publicly available decision trees, support the paper's claims." - -authors: -- Yacine Izza -- Alexey Ignatiev -- Joao Marques-Silva -date: 2023-05-01 00:00:00 -highlight: true -image_preview: '' -math: true -publication: In *Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI)* -publication_types: -- '1' -selected: true -title: 'On Tackling Explanation Redundancy in Decision Trees' ---- \ No newline at end of file diff --git a/content/talk/19092023.md b/content/talk/19092023.md deleted file mode 100644 index 87157ed..0000000 --- a/content/talk/19092023.md +++ /dev/null @@ -1,39 +0,0 @@ -+++ -date = 2023-09-19T00:00:00 # Schedule page publish date. - -title = ' Our paper on explaining the sources of bias in machine learning via influence functions has been accepted in FAccT 2023. Authors: Bishwamittra Ghosh, Debabrota Basu and Kuldeep S. Meel.
We combine explainability with fairness in machine learning, where we compute the influence of individual features and the intersectional effect of multiple features on the resulting bias of a classifier on a dataset. This allows us to have a higher granular depiction of sources of bias than earlier methods.' -time_start = 2023-09-19T00:00:00 -#time_end = 2030-06-01T15:00:00 -abstract = "" -abstract_short = "" -event = "" -event_url = "" -location = "" - -# Is this a selected talk? (true/false) -selected = false - -# Projects (optional). -# Associate this talk with one or more of your projects. -# Simply enter the filename (excluding '.md') of your project file in `content/project/`. -projects = [] - -# Links (optional). -url_pdf = "" -url_slides = "" -url_video = "" -url_code = "" - -# Does the content use math formatting? -math = true - -# Does the content use source code highlighting? -highlight = true - -# Featured image -# Place your image in the `static/img/` folder and reference its filename below, e.g. `image = "example.jpg"`. -[header] -image = "" -caption = "" - -+++ \ No newline at end of file From 8e8721744f6a991b73d2bafe6b5db5d46b195707 Mon Sep 17 00:00:00 2001 From: AL-JiongYang Date: Sat, 25 May 2024 14:31:37 +0800 Subject: [PATCH 4/8] fix author name of IJCAI23 totvardist --- content/publication/IJCAI23_totvardist/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/publication/IJCAI23_totvardist/index.md b/content/publication/IJCAI23_totvardist/index.md index 9e8a12b..b804ab8 100644 --- a/content/publication/IJCAI23_totvardist/index.md +++ b/content/publication/IJCAI23_totvardist/index.md @@ -5,7 +5,7 @@ authors: - Sutanu Gayen - Kuldeep S. Meel - Dimitrios Myrisiotis -- Pavan A. +- A. Pavan - N. V. Vinodchandran date: 2023-07-01 00:00:00 highlight: true From fb95851123f8a12367fa8fc4c67ca9a6f3de1aa8 Mon Sep 17 00:00:00 2001 From: Yacine IZZA Date: Thu, 25 Jul 2024 11:48:51 +0800 Subject: [PATCH 5/8] add ecai24 paper --- content/publication/ECAI24/index.md | 19 +++++++++++++++++++ 1 file changed, 19 insertions(+) create mode 100644 content/publication/ECAI24/index.md diff --git a/content/publication/ECAI24/index.md b/content/publication/ECAI24/index.md new file mode 100644 index 0000000..15da9cf --- /dev/null +++ b/content/publication/ECAI24/index.md @@ -0,0 +1,19 @@ +--- +Formal abductive explanations offer crucial guarantees of rigor and so find application in high-stakes uses of machine learning (ML). One drawback of abductive explanations is explanation size, justified by the cognitive limits of human decision-makers. Probabilistic abductive explanations (PAXps) address this limitation, but their theoretical and practical complexity makes their exact computation most often unrealistic. This paper proposes novel efficient algorithms for the computation of locally-minimal PXAps, which offer high-quality approximations of PXAps in practice. The experimental results demonstrate the practical efficiency of the proposed algorithms. +" + +authors: +- Yacine Izza, +- Kuldeep S Meel, +- Joao Marques-Silva +date: 2024-10-21 00:00:00 +highlight: true +image_preview: '' +math: false +publication: In *Proceedings of ECAI - European Conference on Artificial Intelligence* +publication_types: +- '1' +selected: true +title: 'Locally-Minimal Probabilistic Explanations' +url_pdf: 'https://arxiv.org/pdf/2312.11831' +--- \ No newline at end of file From 4f07f0199e9fbfb25644f0b9b0db072f2d1c5537 Mon Sep 17 00:00:00 2001 From: Yacine IZZA Date: Thu, 25 Jul 2024 11:51:26 +0800 Subject: [PATCH 6/8] add citation ecai24 --- content/publication/ECAI24/cite.bib | 6 ++++++ 1 file changed, 6 insertions(+) create mode 100644 content/publication/ECAI24/cite.bib diff --git a/content/publication/ECAI24/cite.bib b/content/publication/ECAI24/cite.bib new file mode 100644 index 0000000..5f7a17e --- /dev/null +++ b/content/publication/ECAI24/cite.bib @@ -0,0 +1,6 @@ +@inproceedings{IMS24, + title={Locally-Minimal Probabilistic Explanations}, + author={Yacine Izza and Kuldeep S Meel and Joao Marques-Silva}, + booktitle={Proceedings of ECAI}, + year={2024}, +} From e2abcfcbb03f4a8954a9224826bd934bbe912a45 Mon Sep 17 00:00:00 2001 From: Yacine IZZA Date: Thu, 25 Jul 2024 12:03:46 +0800 Subject: [PATCH 7/8] update profile/status --- content/authors/yacine/_index.md | 2 +- content/authors/yacine/avatar.jpg | Bin 214377 -> 9352 bytes 2 files changed, 1 insertion(+), 1 deletion(-) diff --git a/content/authors/yacine/_index.md b/content/authors/yacine/_index.md index c35e7a7..a15a396 100644 --- a/content/authors/yacine/_index.md +++ b/content/authors/yacine/_index.md @@ -10,7 +10,7 @@ authors: # Organizational groups that you belong to (for People widget) # Set this to `[]` or comment out if you are not using People widget. user_groups: -- Postdoctoral Researchers +- Alumni - Postdoc graduates # Is this the primary user of the site? superuser: 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b/content/publication/ECAI24/index.md index 15da9cf..00c0984 100644 --- a/content/publication/ECAI24/index.md +++ b/content/publication/ECAI24/index.md @@ -1,6 +1,6 @@ --- -Formal abductive explanations offer crucial guarantees of rigor and so find application in high-stakes uses of machine learning (ML). One drawback of abductive explanations is explanation size, justified by the cognitive limits of human decision-makers. Probabilistic abductive explanations (PAXps) address this limitation, but their theoretical and practical complexity makes their exact computation most often unrealistic. This paper proposes novel efficient algorithms for the computation of locally-minimal PXAps, which offer high-quality approximations of PXAps in practice. The experimental results demonstrate the practical efficiency of the proposed algorithms. -" +abstract: Formal abductive explanations offer crucial guarantees of rigor and so find application in high-stakes uses of machine learning (ML). One drawback of abductive explanations is explanation size, justified by the cognitive limits of human decision-makers. Probabilistic abductive explanations (PAXps) address this limitation, but their theoretical and practical complexity makes their exact computation most often unrealistic. This paper proposes novel efficient algorithms for the computation of locally-minimal PXAps, which offer high-quality approximations of PXAps in practice. The experimental results demonstrate the practical efficiency of the proposed algorithms. + authors: - Yacine Izza, @@ -14,6 +14,6 @@ publication: In *Proceedings of ECAI - European Conference on Artificial Intell publication_types: - '1' selected: true -title: 'Locally-Minimal Probabilistic Explanations' -url_pdf: 'https://arxiv.org/pdf/2312.11831' ---- \ No newline at end of file +title: Locally-Minimal Probabilistic Explanations +url_pdf: https://arxiv.org/pdf/2312.11831 +---