- 2024
- Hai S. Le, Brendan Juba, Roni Stern:
Learning Safe Action Models with Partial Observability. AAAI 2024: 20159-20167 - Arijit Shaw, Brendan Juba, Kuldeep S. Meel:
An Approximate Skolem Function Counter. AAAI 2024: 8108-8116 - Daniel Hsu, Jizhou Huang, Brendan Juba:
Distribution-Specific Auditing for Subgroup Fairness. FORC 2024: 5:1-5:20 - Argaman Mordoch, Enrico Scala, Roni Stern, Brendan Juba:
Safe Learning of PDDL Domains with Conditional Effects. ICAPS 2024: 387-395 - Daniel Hsu, Jizhou Huang, Brendan Juba:
Polynomial time auditing of statistical subgroup fairness for Gaussian data. CoRR abs/2401.16439 (2024) - Argaman Mordoch, Enrico Scala, Roni Stern, Brendan Juba:
Safe Learning of PDDL Domains with Conditional Effects - Extended Version. CoRR abs/2403.15251 (2024) - Luise Ge, Brendan Juba, Yevgeniy Vorobeychik:
Learning Linear Utility Functions From Pairwise Comparison Queries. CoRR abs/2405.02612 (2024) - 2023
- Ionela G. Mocanu
, Vaishak Belle
, Brendan Juba:
Learnability with PAC Semantics for Multi-agent Beliefs. Theory Pract. Log. Program. 23(4): 730-747 (2023) - Andrew Estornell, Sanmay Das, Brendan Juba, Yevgeniy Vorobeychik:
Popularizing Fairness: Group Fairness and Individual Welfare. AAAI 2023: 7485-7493 - Argaman Mordoch, Brendan Juba, Roni Stern:
Learning Safe Numeric Action Models. AAAI 2023: 12079-12086 - Ionela G. Mocanu, Vaishak Belle, Brendan Juba:
Learnability with PAC Semantics for Multi-agent Beliefs. AAMAS 2023: 2604-2606 - Ionela G. Mocanu, Vaishak Belle, Brendan Juba:
Learnability with PAC Semantics for Multi-agent Beliefs. CoRR abs/2306.05490 (2023) - Argaman Mordoch, Shahaf S. Shperberg, Roni Stern, Brendan Juba:
Enhancing Numeric-SAM for Learning with Few Observations. CoRR abs/2312.10705 (2023) - Arijit Shaw, Brendan Juba, Kuldeep S. Meel:
An Approximate Skolem Function Counter. CoRR abs/2312.12026 (2023) - 2022
- Brendan Juba, Roni Stern:
Learning Probably Approximately Complete and Safe Action Models for Stochastic Worlds. AAAI 2022: 9795-9804 - Zihao Deng, Siddartha Devic, Brendan Juba:
Polynomial Time Reinforcement Learning in Factored State MDPs with Linear Value Functions. AISTATS 2022: 11280-11304 - Leda Liang, Brendan Juba:
Conditional Linear Regression for Heterogeneous Covariances. AISTATS 2022: 6182-6199 - Priyanka Golia, Brendan Juba, Kuldeep S. Meel:
A Scalable Shannon Entropy Estimator. CAV (1) 2022: 363-384 - Elena Grigorescu, Brendan Juba, Karl Wimmer, Ning Xie:
Hardness of Maximum Likelihood Learning of DPPs. COLT 2022: 3800-3819 - Brendan Juba, Roni Stern:
An Example of the SAM+ Algorithm for Learning Action Models for Stochastic Worlds. CoRR abs/2203.12499 (2022) - Elena Grigorescu, Brendan Juba, Karl Wimmer, Ning Xie:
Hardness of Maximum Likelihood Learning of DPPs. CoRR abs/2205.12377 (2022) - Priyanka Golia, Brendan Juba, Kuldeep S. Meel:
A Scalable Shannon Entropy Estimator. CoRR abs/2206.00921 (2022) - Elena Grigorescu, Brendan Juba, Karl Wimmer, Ning Xie:
Hardness of Maximum Likelihood Learning of DPPs. Electron. Colloquium Comput. Complex. TR22 (2022) - 2021
- Tomer Shahar, Shashank Shekhar, Dor Atzmon, Abdallah Saffidine, Brendan Juba, Roni Stern:
Safe Multi-Agent Pathfinding with Time Uncertainty. J. Artif. Intell. Res. 70: 923-954 (2021) - Mahdi Cheraghchi, Elena Grigorescu, Brendan Juba, Karl Wimmer, Ning Xie:
List Learning with Attribute Noise. AISTATS 2021: 2215-2223 - Atish Agarwala, Abhimanyu Das, Brendan Juba, Rina Panigrahy, Vatsal Sharan, Xin Wang, Qiuyi Zhang:
One Network Fits All? Modular versus Monolithic Task Formulations in Neural Networks. ICLR 2021 - Honghua Zhang, Brendan Juba, Guy Van den Broeck:
Probabilistic Generating Circuits. ICML 2021: 12447-12457 - Alexander Philipp Rader, Ionela G. Mocanu, Vaishak Belle, Brendan Juba:
Learning Implicitly with Noisy Data in Linear Arithmetic. IJCAI 2021: 1410-1417 - Brendan Juba, Hai S. Le, Roni Stern:
Safe Learning of Lifted Action Models. KR 2021: 379-389 - Honghua Zhang, Brendan Juba, Guy Van den Broeck:
Probabilistic Generating Circuits. CoRR abs/2102.09768 (2021)