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Martin Pawelczyk
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2020 – today
- 2024
- [j1]Vadim Borisov
, Tobias Leemann
, Kathrin Seßler
, Johannes Haug
, Martin Pawelczyk
, Gjergji Kasneci
:
Deep Neural Networks and Tabular Data: A Survey. IEEE Trans. Neural Networks Learn. Syst. 35(6): 7499-7519 (2024) - [c13]Tobias Leemann, Martin Pawelczyk, Christian Thomas Eberle, Gjergji Kasneci
:
I Prefer Not to Say: Protecting User Consent in Models with Optional Personal Data. AAAI 2024: 21312-21321 - [i20]Tobias Leemann, Martin Pawelczyk, Bardh Prenkaj, Gjergji Kasneci
:
Towards Non-Adversarial Algorithmic Recourse. CoRR abs/2403.10330 (2024) - [i19]Martin Pawelczyk, Jimmy Z. Di, Yiwei Lu, Gautam Kamath, Ayush Sekhari, Seth Neel:
Machine Unlearning Fails to Remove Data Poisoning Attacks. CoRR abs/2406.17216 (2024) - [i18]Catherine Huang, Martin Pawelczyk, Himabindu Lakkaraju:
Explaining the Model, Protecting Your Data: Revealing and Mitigating the Data Privacy Risks of Post-Hoc Model Explanations via Membership Inference. CoRR abs/2407.17663 (2024) - 2023
- [c12]Martin Pawelczyk, Himabindu Lakkaraju, Seth Neel:
On the Privacy Risks of Algorithmic Recourse. AISTATS 2023: 9680-9696 - [c11]Vadim Borisov, Kathrin Seßler, Tobias Leemann, Martin Pawelczyk, Gjergji Kasneci:
Language Models are Realistic Tabular Data Generators. ICLR 2023 - [c10]Martin Pawelczyk, Teresa Datta, Johannes van den Heuvel, Gjergji Kasneci, Himabindu Lakkaraju:
Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recourse. ICLR 2023 - [c9]Martin Pawelczyk, Tobias Leemann, Asia Biega, Gjergji Kasneci:
On the Trade-Off between Actionable Explanations and the Right to be Forgotten. ICLR 2023 - [c8]Tobias Leemann, Martin Pawelczyk, Gjergji Kasneci:
Gaussian Membership Inference Privacy. NeurIPS 2023 - [i17]Tobias Leemann, Martin Pawelczyk, Gjergji Kasneci
:
Gaussian Membership Inference Privacy. CoRR abs/2306.07273 (2023) - [i16]Martin Pawelczyk, Seth Neel, Himabindu Lakkaraju:
In-Context Unlearning: Language Models as Few Shot Unlearners. CoRR abs/2310.07579 (2023) - 2022
- [c7]Martin Pawelczyk, Chirag Agarwal, Shalmali Joshi, Sohini Upadhyay, Himabindu Lakkaraju:
Exploring Counterfactual Explanations Through the Lens of Adversarial Examples: A Theoretical and Empirical Analysis. AISTATS 2022: 4574-4594 - [c6]Chirag Agarwal, Satyapriya Krishna, Eshika Saxena, Martin Pawelczyk, Nari Johnson, Isha Puri, Marinka Zitnik, Himabindu Lakkaraju:
OpenXAI: Towards a Transparent Evaluation of Model Explanations. NeurIPS 2022 - [i15]Martin Pawelczyk, Teresa Datta, Johannes van den Heuvel, Gjergji Kasneci
, Himabindu Lakkaraju:
Algorithmic Recourse in the Face of Noisy Human Responses. CoRR abs/2203.06768 (2022) - [i14]Chirag Agarwal, Nari Johnson, Martin Pawelczyk, Satyapriya Krishna, Eshika Saxena, Marinka Zitnik, Himabindu Lakkaraju:
Rethinking Stability for Attribution-based Explanations. CoRR abs/2203.06877 (2022) - [i13]Chirag Agarwal, Eshika Saxena, Satyapriya Krishna, Martin Pawelczyk, Nari Johnson, Isha Puri, Marinka Zitnik, Himabindu Lakkaraju:
OpenXAI: Towards a Transparent Evaluation of Model Explanations. CoRR abs/2206.11104 (2022) - [i12]Martin Pawelczyk, Tobias Leemann, Asia Biega, Gjergji Kasneci
:
On the Trade-Off between Actionable Explanations and the Right to be Forgotten. CoRR abs/2208.14137 (2022) - [i11]Vadim Borisov, Kathrin Seßler
, Tobias Leemann, Martin Pawelczyk, Gjergji Kasneci
:
Language Models are Realistic Tabular Data Generators. CoRR abs/2210.06280 (2022) - [i10]Tobias Leemann, Martin Pawelczyk, Christian Thomas Eberle, Gjergji Kasneci
:
I Prefer not to Say: Operationalizing Fair and User-guided Data Minimization. CoRR abs/2210.13954 (2022) - [i9]Martin Pawelczyk, Lea Tiyavorabun, Gjergji Kasneci
:
Decomposing Counterfactual Explanations for Consequential Decision Making. CoRR abs/2211.02151 (2022) - [i8]Martin Pawelczyk, Himabindu Lakkaraju, Seth Neel:
On the Privacy Risks of Algorithmic Recourse. CoRR abs/2211.05427 (2022) - 2021
- [c5]Hamed Jalali, Martin Pawelczyk, Gjergji Kasneci
:
Model Selection in Local Approximation Gaussian Processes: A Markov Random Fields Approach. IEEE BigData 2021: 768-778 - [c4]Martin Pawelczyk, Sascha Bielawski, Johannes van den Heuvel, Tobias Richter, Gjergji Kasneci:
CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms. NeurIPS Datasets and Benchmarks 2021 - [i7]Hamed Jalali, Martin Pawelczyk, Gjergji Kasneci:
Gaussian Experts Selection using Graphical Models. CoRR abs/2102.01496 (2021) - [i6]Martin Pawelczyk, Shalmali Joshi, Chirag Agarwal, Sohini Upadhyay, Himabindu Lakkaraju:
On the Connections between Counterfactual Explanations and Adversarial Examples. CoRR abs/2106.09992 (2021) - [i5]Martin Pawelczyk, Sascha Bielawski, Johannes van den Heuvel, Tobias Richter, Gjergji Kasneci:
CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms. CoRR abs/2108.00783 (2021) - [i4]Vadim Borisov, Tobias Leemann, Kathrin Seßler, Johannes Haug, Martin Pawelczyk, Gjergji Kasneci:
Deep Neural Networks and Tabular Data: A Survey. CoRR abs/2110.01889 (2021) - 2020
- [c3]Johannes Haug, Martin Pawelczyk, Klaus Broelemann, Gjergji Kasneci
:
Leveraging Model Inherent Variable Importance for Stable Online Feature Selection. KDD 2020: 1478-1502 - [c2]Martin Pawelczyk, Klaus Broelemann, Gjergji Kasneci:
On Counterfactual Explanations under Predictive Multiplicity. UAI 2020: 809-818 - [c1]Martin Pawelczyk, Klaus Broelemann, Gjergji Kasneci
:
Learning Model-Agnostic Counterfactual Explanations for Tabular Data. WWW 2020: 3126-3132 - [i3]Johannes Haug, Martin Pawelczyk, Klaus Broelemann, Gjergji Kasneci:
Leveraging Model Inherent Variable Importance for Stable Online Feature Selection. CoRR abs/2006.10398 (2020) - [i2]Martin Pawelczyk, Klaus Broelemann, Gjergji Kasneci:
On Counterfactual Explanations under Predictive Multiplicity. CoRR abs/2006.13132 (2020)
2010 – 2019
- 2019
- [i1]Martin Pawelczyk, Johannes Haug, Klaus Broelemann, Gjergji Kasneci:
Towards User Empowerment. CoRR abs/1910.09398 (2019)
Coauthor Index
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last updated on 2024-08-25 20:06 CEST by the dblp team
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