#InterpretableML

🚀Just published our new paper in @EarthsFutureEiC! 🌍 We propose how #InterpretableML can be more broadly and effectively integrated into geoscientific research, highlighting key do's and don'ts when using IML for process understanding. Check it out:

The first version of my online book on Interpretable Machine Learning is out! I am very excited to release it. It's a guide for making machine learning models explainable. #interpretableML #iml #ExplainableAI #xai #MachineLearning #DataScience

Our #ICLR paper, “Efficient & Accurate Explanation Estimation with Distribution Compression” made the top 5.1% of submissions and was selected as a Spotlight! Congrats to the first author @hbaniecki #xAI #interpretableML Paper:

Thank you @codechrysalis and @__MLT__ for giving me the opportunity to share some knowledge on #XAI #interpretableML Here are the presentation slides

#ICML2023 #BayesDL #InterpretableML Can we train self-interpretable time series models that generate actionable explanations? Come check out our Counterfactual Time Series (CounTS) in the oral session C2 at 3pm~4:30pm, July 27 and poster session 11:00am~1:30pm on July 25, Hall 1.

We are having a mini-session in #causality at #pbdw2019 with first two talks by Rich Caruana and Yi Luo! #interpretableML #radonc @UMichRadOnc @MSFTResearch

CALL FOR PAPERS AND ABSTRACTS “Explainable Artificial Intelligence For Unveiling The Brain: From The Black-Box To The Glass-Box” BrainInformatics2023 #explainableAI #explainableML #interpretableML #XAI #ArtificialIntelligence #MachineLearning #neuroscience #neuroimaging #brain

🚨 Are you at #INFORMS2024? Don't miss our session on Emerging Trends in Interpretable Machine Learning today at 2:15 PM! 🌟 Our speakers will dive into theoretical and applied aspects of interpretability and model multiplicity. 💡#InterpretableML #TrustworthyAI #Multiplicity

Decision-support systems need user confidence 🧐 to go into production. This is especially critical for hybrid #MachineLearning + #ORMS pipelines. So, how to explain data-driven decisions based on contextual information? We introduce new methodologies:

Our current approach is applicable to pipelines with random forests and nearest-neighbor predictors. The resulting explanation problems can be solved at scale for a variety of applications: we present experiments on multi-item newsvendor, 2-stages shipment planning. CVaR SPs.

Ultimately, we hope it contributes to bridging the gap between #InterpretableML and #ORMS. To facilitate future work and experimental reproducibility, all our algorithms and scripts are provided in open source at

Time for some afterthoughts #EURO2022 #euro2022espoo #ORMS Long thread.

Day 1. Roundtable with Forums. Do not forget Ms Nidhi Sawhney advice on the role of monitoring and delivering assurance (vs risk of ambitious innovation) in company engagement. From a different angle, this reinforces @juanmi82mg and Felici about project management skills. .#ORMS

Day 2. Keynote by Prof Emilio Carrizosa @emiliocarrizosa Very emotional after 20 years since he, @DoloresRomeroM and myself started the journey on “Mathematical Programming for Data Mining”! I learned so much from them back then and I still do. #XAI #interpretableML #ORMS

#Newpaper [Article] Deterministic Local Interpretable Model-Agnostic Explanations for Stable Explainability #ExplainableArtificialIntelligence (XAI) #InterpretableML 👉Please find more details via: @MAKE_MDPI

#BayesianDeepLearning #InterpretableML #ConceptInterpretation #HumanAICollaboration Achieving the balance between accuracy and interpretability in machine learning models is a notable challenge. Models that are accurate often lack interpretability,

⚡Interested in making pretrained generative models interpretable with minimal training and annotations? I'll be presenting our paper, Interpretable Generative Models through Post-hoc Concept Bottlenecks, at #CVPR2025 today in Poster Session 2 (4 pm - 6 pm CDT) at poster #266!

🚀 Paper: 🚀 Code: 🚀 Project site: Thanks to my co-authors, Ge Yan, @sun_chung_en, @tuomasoi, and my PhD advisor @LilyWeng_

#CVPR2025 #TrustworthyML #InterpretableML #GenAI #AI #MachineLearning #AIResearch #DeepLearning #ComputerVision

Looking forward to the 1st #XAI day webinar tomorrow, Sept. 3rd, at @DIAL_UniCam. Many thanks to the speakers @grau_isel, Eric S. Vorm and @leilanigilpin who will talk about the essentials of #interpretableML and its applications.

We have 2 open #PhD positions where fellows will work with 24/7 recordings of physical activity: one focusing on #InterpretableML and one on the risk of common non-communicable diseases. Plz share the post in your network & help us find talented students:

NEW GUEST BLOGPOST: Interpretable Machine Learning for Image Classification with LIME by my friend @ArteagaCrist #InterpretableML

The how & why beyond the what - #interpretableML for #systemsimmunology. Our new paper, "Essential Regression - A generalizable framework for inferring causal latent factors from multi-omic datasets" now online in @Patterns_CP #MachineLearning #immunology

Relying on XGBoost/RF feature importances to interpret your model? Read this first You are probably reporting misleading conclusions #XAI #interpretableML

Our #WACV23 paper on the topic of evaluation for model interpretation methods is available online Congrats to @behzadikhormuji for the good work. #xai #interpretableml @IDLabResearch @UAntwerpMathCS #uantwerp

Excellent overview of #InterpretableML and #XAI capabilities in #Knime. H/T @lrudnitskaya and @paolotamag

A big objection to #deeplearning & #neuralnetworks has been interpretability, esp. for #audit. Today an enlightening article on #machinelearning interpretability techniques #datascience #data #XAI #lowcode #opensource #AI #artificialintelligence