Towards Model Agnostic Robustness

Media Summary: Sujay Sanghavi Associate Professor of Electrical and Computer Engineering University of Texas at Austin ABSTRACT: It is now ... MIFODS - LIDS Seminar. Cambridge, US Nov 18, 2019. Session language – English Target audience – Data Scientists I'll discuss an interpretation framework that allows use of the ...

Towards Model Agnostic Robustness - Detailed Analysis & Overview

Sujay Sanghavi Associate Professor of Electrical and Computer Engineering University of Texas at Austin ABSTRACT: It is now ... MIFODS - LIDS Seminar. Cambridge, US Nov 18, 2019. Session language – English Target audience – Data Scientists I'll discuss an interpretation framework that allows use of the ... Talks on Frontiers of Parameterized Complexity Keywords: PAC, Halfspaces, In this talk, Chen shares his research journey Brian Hu (KitWare) presents his work on explaining

In this video, we discuss about the paper entitled " Paper PDF: Check my merch: We present FLUKE ... Authors: Jeet Mohapatra, Tsui-Wei Weng, Pin-Yu Chen, Sijia Liu, Luca Daniel Description: Verifying This work has been accepted to the ACM Web Conference 2026 Demonstration Track. Abstract: AI

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Towards Model Agnostic Robustness

Sujay Sanghavi (UT Austin) -- Towards Model Agnostic Robustness

RSGNN: A Model-agnostic Approach for Enhancing the Robustness of Signed Graph Neural Networks

Nathalie Hauser: Model Agnostic Interpretation Beyond Shap and Lime

[CVPR 2026] BALM: A Model-Agnostic Framework for Balanced Multimodal Learning under IMR

Pasin Manurangsi. Adversarially Robust Proper Learning of Halfspaces with Agnostic Noise: Complexity

Pin-Yu Chen: AI Model Inspector: Towards Holistic Adversarial Robustness for Deep Learning

Explaining model robustness (METACOG-25)

ECS289G:Model-Agnostic Counterfactual Reasoning for Eliminating Popularity Bias in RecommenderSystem

BayLIME: Bayesian Local Interpretable Model-Agnostic Explanations (UAI'21)

FLUKE: A Linguistically-Driven and Task-Agnostic Framework for Robustness Evaluation

Towards Verifying Robustness of Neural Networks Against A Family of Semantic Perturbations

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Towards Model Agnostic Robustness

Towards Model Agnostic Robustness

Sujay Sanghavi Associate Professor of Electrical and Computer Engineering University of Texas at Austin ABSTRACT: It is now ...

Sujay Sanghavi (UT Austin) -- Towards Model Agnostic Robustness

Sujay Sanghavi (UT Austin) -- Towards Model Agnostic Robustness

MIFODS - LIDS Seminar. Cambridge, US Nov 18, 2019.

RSGNN: A Model-agnostic Approach for Enhancing the Robustness of Signed Graph Neural Networks

RSGNN: A Model-agnostic Approach for Enhancing the Robustness of Signed Graph Neural Networks

WWW conf. RSGNN: A

Nathalie Hauser: Model Agnostic Interpretation Beyond Shap and Lime

Nathalie Hauser: Model Agnostic Interpretation Beyond Shap and Lime

Session language – English Target audience – Data Scientists I'll discuss an interpretation framework that allows use of the ...

[CVPR 2026] BALM: A Model-Agnostic Framework for Balanced Multimodal Learning under IMR

[CVPR 2026] BALM: A Model-Agnostic Framework for Balanced Multimodal Learning under IMR

Title: BALM: A

Pasin Manurangsi. Adversarially Robust Proper Learning of Halfspaces with Agnostic Noise: Complexity

Pasin Manurangsi. Adversarially Robust Proper Learning of Halfspaces with Agnostic Noise: Complexity

Talks on Frontiers of Parameterized Complexity https://frontpc.blogspot.com Keywords: PAC, Halfspaces,

Pin-Yu Chen: AI Model Inspector: Towards Holistic Adversarial Robustness for Deep Learning

Pin-Yu Chen: AI Model Inspector: Towards Holistic Adversarial Robustness for Deep Learning

In this talk, Chen shares his research journey

Explaining model robustness (METACOG-25)

Explaining model robustness (METACOG-25)

Brian Hu (KitWare) presents his work on explaining

ECS289G:Model-Agnostic Counterfactual Reasoning for Eliminating Popularity Bias in RecommenderSystem

ECS289G:Model-Agnostic Counterfactual Reasoning for Eliminating Popularity Bias in RecommenderSystem

In this video, we discuss about the paper entitled "

BayLIME: Bayesian Local Interpretable Model-Agnostic Explanations (UAI'21)

BayLIME: Bayesian Local Interpretable Model-Agnostic Explanations (UAI'21)

BayLIME: Bayesian Local Interpretable

FLUKE: A Linguistically-Driven and Task-Agnostic Framework for Robustness Evaluation

FLUKE: A Linguistically-Driven and Task-Agnostic Framework for Robustness Evaluation

Paper PDF: http://arxiv.org/pdf/2504.17311v1 Check my merch: https://dragonprof-2.creator-spring.com We present FLUKE ...

Towards Verifying Robustness of Neural Networks Against A Family of Semantic Perturbations

Towards Verifying Robustness of Neural Networks Against A Family of Semantic Perturbations

Authors: Jeet Mohapatra, Tsui-Wei Weng, Pin-Yu Chen, Sijia Liu, Luca Daniel Description: Verifying

ARC: A Tool to Rate AI Models for Robustness Through a Causal Lens for Trustworthy Model Selection

ARC: A Tool to Rate AI Models for Robustness Through a Causal Lens for Trustworthy Model Selection

This work has been accepted to the ACM Web Conference 2026 Demonstration Track. Abstract: AI