FUN2MODEL - Publications
Sort by: date, type, title
43 publications:
-
[BPL+20]
Arno Blaas, Andrea Patane, Luca Laurenti, Luca Cardelli, Marta Kwiatkowska and Stephen Roberts.
Adversarial Robustness Guarantees for Classification with Gaussian Processes.
In 23rd International Conference on Artificial Intelligence and Statistics (AISTATS'20), PMLR.
August 2020.
[pdf]
[bib]
-
[PBL+22]
Andrea Patane, Arno Blaas, Luca Laurenti, Luca Cardelli, Stephen Roberts and Marta Kwiatkowska.
Adversarial Robustness Guarantees for Gaussian Processes.
Journal of Machine Learning Research, 23, pages 1-55.
2022.
[pdf]
[bib]
-
[Wic22]
Matthew Wicker.
Adversarial Robustness of Bayesian Neural Networks.
Ph.D. thesis, Department of Computer Science, University of Oxford.
2022.
[pdf]
[bib]
-
[CKL21]
Luca Cardelli, Marta Kwiatkowska and Luca Laurenti.
A Language for Modeling And Optimizing Experimental Biological Protocols.
Computation, MDPI.
October 2021.
[pdf]
[bib]
-
[LWL+20]
Emanuele La Malfa, Min Wu, Luca Laurenti, Benjie Wang, Anthony Hartshorn and Marta Kwiatkowska.
Assessing Robustness of Text Classification through Maximal Safe Radius Computation.
In Conference on Empirical Methods in Natural Language Processing (EMNLP'20), ACL.
November 2020.
[pdf]
[bib]
-
[KNPS21]
Marta Kwiatkowska, Gethin Norman, David Parker and Gabriel Santos.
Automated Verification of Concurrent Stochastic Systems.
Formal Methods in System Design, Springer.
January 2021.
[pdf]
[bib]
-
[San21]
Gabriel H.R. Santos.
Automatic Verification and Strategy Synthesis for Zero-sum and Equilibria Properties of Concurrent Stochastic Games.
Ph.D. thesis, Department of Computer Science, University of Oxford.
March 2021.
[pdf]
[bib]
-
[WLP+21]
Matthew Wicker, Luca Laurenti, Andrea Patane, Zhuotong Chen, Zheng Zhang and Marta Kwiatkowska.
Bayesian Inference with Certifiable Adversarial Robustness.
In International Conference on Artificial Intelligence and Statistics (AISTATS'21), PMLR.
April 2021.
[pdf]
[bib]
-
[KJI+21]
Hannah Rose Kirk, Yennie Jun, Haider Iqbal, Elias Benussi, Filippo Volpin, Frederic A. Dreyer, Aleksandar Shtedritski, Yuki M. Asano.
Bias Out-of-the-Box: An Empirical Analysis of Intersectional Occupational Biases in Popular Generative Language Models.
In Conference on Neural Information Processing Systems (NeurIPS'21). To appear.
December 2021.
[pdf]
[bib]
-
[WLP+21a]
Matthew Wicker, Luca Laurenti, Andrea Patane, Nicola Paoletti, Alessandro Abate and Marta Kwiatkowska.
Certification of Iterative Predictions in Bayesian Neural Networks.
In 37th Conference on Uncertainty in Artificial Intelligence (UAI'21).
May 2021.
[pdf]
[bib]
-
[KNPS22]
Marta Kwiatkowska, Gethin Norman, David Parker and Gabriel Santos.
Correlated Equilibria and Fairness in Concurrent Stochastic Games.
In Proc. 28th International Conference on Tools and Algorithms for the Construction and Analysis of Systems (TACAS'22). To appear.
April 2022.
[pdf]
[bib]
-
[YSD+22]
Rui Yan, Gabriel Santos, Xiaoming Duan, David Parker and Marta Kwiatkowska.
Finite-horizon Equilibria for Neuro-symbolic Concurrent Stochastic Games.
In Proc. 38th Conference on Uncertainty in Artificial Intelligence (UAI'22), AUAI Press. To appear.
August 2022.
https://arxiv.org/abs/2205.07546
-
[Bac22]
Edoardo Bacci.
Formal Verification of Deep Reinforcement Learning Agents.
Ph.D. thesis, School of Computer Science, University of Birmingham.
March 2022.
[pdf]
[bib]
-
[BPW+22]
Elias Benussi, Andrea Patane, Matthew Wicker, Luca Laurenti and Marta Kwiatkowska.
Individual Fairness Guarantees for Neural Networks.
In Proc. 31st International Joint Conference on Artificial Intelligence (IJCAI'22). To appear.
July 2022.
[pdf]
[bib]
-
[ZLS+20]
Amy Zhang, Clare Lyle, Shagun Sodhani, Angelos Filos, Marta Kwiatkowska, Joelle Pineau, Yarin Gal, Doina Precup.
Invariant Causal Prediction for Block MDPs.
In International Conference on Machine Learning (ICML'20), PMLR.
July 2020.
[pdf]
[bib]
-
[LRD+22]
Clare Lyle, Mark Rowland, Will Dabney, Marta Kwiatkowska and Yarin Gal.
Learning Dynamics and Generalization in Reinforcement Learning.
In Proc. 39th International Conference on Machine Learning (ICML'22). To appear.
2022.
-
[CBPF22]
Shenghui Chen, Kayla Boggess, David Parker and Lu Feng.
Multi-Objective Controller Synthesis with Uncertain Human Preferences.
In Proc. ACM/IEEE 13th International Conference on Cyber-Physical Systems (ICCPS'22), ACM. To appear.
May 2022.
[pdf]
[bib]
-
[KNPS20b]
Marta Kwiatkowska, Gethin Norman, David Parker, and Gabriel Santos.
Multi-player Equilibria Verification for Concurrent Stochastic Games.
In 17th International Conference on Quantitative Evaluation of SysTems (QEST'20), Springer.
August 2020.
[pdf]
[bib]
-
[BHK+20]
Carlos E. Budde, Arnd Hartmanns, Michaela Klauck, Jan KretínskyΜ, David Parker, Tim Quatmann, Andrea Turrini, and Zhen Zhang.
On Correctness, Precision, and Performance in Quantitative Verification: QComp 2020 Competition Report.
In 9th International Symposium on Leveraging Applications of Formal Methods, Verification and Validation (ISoLA'20), Springer.
October 2020.
[pdf]
[bib]
-
[LZM+21]
Emanuele La Malfa, Agnieszka Zbrzezny, Rhiannon Michelmore, Nicola Paoletti and Marta Kwiatkowska.
On Guaranteed Optimal Robust Explanations for NLP Models.
In 30th International Joint Conference on Artificial Intelligence (IJCAI'21).
August 2021.
[pdf]
[bib]
-
[Pat21]
Andrea Patane.
On the Adversarial Robustness of Gaussian Processes.
Ph.D. thesis, Department of Computer Science, University of Oxford.
February 2021.
[pdf]
[bib]
-
[LVK+20]
Clare Lyle, Mark van der Wilk, Marta Kwiatkowska, Yarin Gal and Benjamin Bloem-Reddy.
On the Benefits of Invariance in Neural Networks.
Technical report , arXiv. Presented as poster at the NeurIPS 2019 Machine Learning with Guarantees Workshop.
May 2020.
[pdf]
[bib]
-
[GKK+21]
Pascale Gourdeau, Varun Kanade, Marta Kwiatkowska and James Worrell.
On the Hardness of Robust Classification.
Journal of Machine Learning Research (JMLR), 22(273), pages 1-29.
October 2021.
[pdf]
[bib]
-
[LK22]
Emanuele La Malfa and Marta Kwiatkowska.
πβπ πΎπππ ππ πππππ: on the Notion of Robustness for Natural Language Processing.
In Proc. 36th AAAI Conference on Artificial Intelligence (AAAI'22). To appear.
March 2022.
[pdf]
[bib]
-
[KNPS20]
Marta Kwiatkowska, Gethin Norman, David Parker and Gabriel Santos.
PRISM-games 3.0: Stochastic Game Verification with Concurrency, Equilibria and Time.
In 32nd International Conference on Computer Aided Verification (CAV'20), Springer.
July 2020.
[pdf]
[bib]
-
[BP20]
Edoardo Bacci and David Parker.
Probabilistic Guarantees for Safe Deep Reinforcement Learning.
In 18th International Conference on Formal Modelling and Analysis of Timed Systems (FORMATS'20), Springer.
September 2020.
[pdf]
[bib]
-
[KNP22]
Marta Kwiatkowska, Gethin Norman and David Parker.
Probabilistic Model Checking and Autonomy.
Annual Review of Control, Robotics, and Autonomous Systems, 5, Annual Reviews, 5, pages 385-410.
May 2022.
[pdf]
[bib]
-
[WLPK20]
Matthew Wicker, Luca Laurenti, Andrea Patane and Marta Kwiatkowska.
Probabilistic Safety for Bayesian Neural Networks.
In 36th Conference on Uncertainty in Artificial Intelligence (UAI'20), PMLR.
August 2020.
[pdf]
[bib]
-
[WLK21]
Benjie Wang, Clare Lyle and Marta Kwiatkowska.
Provable Guarantees on the Robustness of Decision Rules to Causal Interventions.
In 30th International Joint Conference on Artificial Intelligence (IJCAI'21).
August 2021.
[pdf]
[bib]
-
[AGH+21]
Alessandro Abate, Julian Gutierrez, Lewis Hammond, Paul Harrenstein, Marta Kwiatkowska, Muhammad Najib, Giuseppe Perelli, Thomas Steeples and Michael Wooldridge.
Rational Verification: A Progress Report.
Applied Intelligence, Springer.
August 2021.
[pdf]
[bib]
-
[Wu20]
Min Wu.
Robustness Evaluation of Deep Neural Networks with Provable Guarantees.
Ph.D. thesis, Department of Computer Science, University of Oxford.
May 2020.
[pdf]
[bib]
-
[WWK22]
Hjalmar Wijk, Benjie Wang and Marta Kwiatkowska.
Robustness Guarantees for Credal Bayesian Networks via Constraint Relaxation over Probabilistic Circuits.
In Proc. 31st International Joint Conference on Artificial Intelligence (IJCAI'22). To appear.
July 2022.
[pdf]
[bib]
-
[WK20]
Min Wu and Marta Kwiatkowska.
Robustness Guarantees for Deep Neural Networks on Videos.
In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR'20), IEEE.
June 2020.
[pdf]
[bib]
-
[Kwi20]
Marta Kwiatkowska.
Safety and Robustness for Deep Learning with Provable Guarantees.
In 35th IEEE/ACM International Conference on Automated Software Engineering, (ASE’20), ACM.
September 2020.
[pdf]
[bib]
-
[PLP+20]
Kyriakos Polymenakos, Luca Laurenti, Andrea Patane, Jan-Peter Calliess, Luca Cardelli, Marta Kwiatkowska, Alessandro Abate and Stephen Roberts.
Safety Guarantees for Iterative Predictions with Gaussian Processes.
In 59th Conference on Decision and Control (CDC'20), IEEE.
December 2020.
[pdf]
[bib]
-
[Kwi19]
Marta Kwiatkowska.
Safety Verification for Deep Neural Networks with Provable Guarantees (Invited Paper).
In 30th International Conference on Concurrency Theory (CONCUR'19), Dagstuhl Publishing.
August 2019.
[pdf]
[bib]
-
[GKKW22]
Pascale Gourdeau, Varun Kanade, Marta Kwiatkowska and James Worrell.
Sample Complexity Bounds for Robustly Learning Decision Lists against Evasion Attacks.
In Proc. 31st International Joint Conference on Artificial Intelligence (IJCAI'22). To appear.
July 2022.
[pdf]
[bib]
-
[BAJ+22]
Thom S. Badings, Alessandro Abate, Nils Jansen, David Parker, Hasan A. Poonawala and Marielle Stoelinga.
Sampling-Based Robust Control of Autonomous Systems with Non-Gaussian Noise.
In Proc. 36th AAAI Conference on Artificial Intelligence (AAAI'22). To appear.
March 2022.
[pdf]
[bib]
-
[YSN+22]
Rui Yan, Gabriel Santos, Gethin Norman, David Parker and Marta Kwiatkowska.
Strategy Synthesis for Zero-sum Neuro-symbolic Concurrent Stochastic Games.
Technical report arXiv:2202.06255, arXiv.
2022.
https://arxiv.org/abs/2202.06255
-
[WWK22b]
Benjie Wang, Matthew Wicker and Marta Kwiatkowska.
Tractable Uncertainty for Structure Learning.
In Proc. 39th International Conference on Machine Learning (ICML'22). To appear.
2022.
https://arxiv.org/abs/2204.14170
-
[LKF21]
Tobias Lorenz, Marta Kwiatkowska and Mario Fritz.
Uncertify: Attacks Against Neural Network Certification.
Technical report , ArXiv. Under submission.
August 2021.
[pdf]
[bib]
-
[BP22]
Edoardo Bacci and David Parker.
Verified Probabilistic Policies for Deep Reinforcement Learning.
In Proc. 14th International Symposium NASA Formal Methods (NFM'22), volume 13260 of LNCS, pages 193-212, Springer.
May 2022.
[pdf]
[bib]
-
[BGP21]
Edoardo Bacci, Mirco Giacobbe and David Parker.
Verifying Reinforcement Learning up to Infinity.
In 30th International Joint Conference on Artificial Intelligence (IJCAI'21).
August 2021.
[pdf]
[bib]
Sort by: date, type, title
