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ORCIDBrendan Juba
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- affiliation: Washington University in St. Louis, USA
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2020 – today
- 2024
[c52]Arijit Shaw, Brendan Juba, Kuldeep S. Meel:
An Approximate Skolem Function Counter. AAAI 2024: 8108-8116- [c51]Hai S. Le, Brendan Juba, Roni Stern:
Learning Safe Action Models with Partial Observability. AAAI 2024: 20159-20167 - [c50]Daniel Hsu, Jizhou Huang, Brendan Juba:
Distribution-Specific Auditing for Subgroup Fairness. FORC 2024: 5:1-5:20 - [c49]Argaman Mordoch, Enrico Scala, Roni Stern, Brendan Juba:
Safe Learning of PDDL Domains with Conditional Effects. ICAPS 2024: 387-395 - [c48]Andrew Estornell, Tina Zhang, Sanmay Das, Chien-Ju Ho, Brendan Juba, Yevgeniy Vorobeychik:
The Impact of Features Used by Algorithms on Perceptions of Fairness. IJCAI 2024: 376-384 - [i38]Daniel Hsu, Jizhou Huang, Brendan Juba:
Polynomial time auditing of statistical subgroup fairness for Gaussian data. CoRR abs/2401.16439 (2024) - [i37]Argaman Mordoch, Enrico Scala, Roni Stern, Brendan Juba:
Safe Learning of PDDL Domains with Conditional Effects - Extended Version. CoRR abs/2403.15251 (2024) - [i36]Luise Ge, Brendan Juba, Yevgeniy Vorobeychik:
Learning Linear Utility Functions From Pairwise Comparison Queries. CoRR abs/2405.02612 (2024) - 2023
- [j8]Sumedh Pendurkar, Taoan Huang, Brendan Juba, Jiapeng Zhang, Sven Koenig, Guni Sharon:
The (Un)Scalability of Informed Heuristic Function Estimation in NP-Hard Search Problems. Trans. Mach. Learn. Res. 2023 (2023) - [j7]Ionela G. Mocanu
, Vaishak Belle, Brendan Juba:
Learnability with PAC Semantics for Multi-agent Beliefs. Theory Pract. Log. Program. 23(4): 730-747 (2023) - [c47]Andrew Estornell, Sanmay Das, Brendan Juba, Yevgeniy Vorobeychik:
Popularizing Fairness: Group Fairness and Individual Welfare. AAAI 2023: 7485-7493 - [c46]Argaman Mordoch, Brendan Juba, Roni Stern:
Learning Safe Numeric Action Models. AAAI 2023: 12079-12086 - [c45]Ionela G. Mocanu, Vaishak Belle, Brendan Juba:
Learnability with PAC Semantics for Multi-agent Beliefs. AAMAS 2023: 2604-2606 - [i35]Ionela G. Mocanu, Vaishak Belle, Brendan Juba:
Learnability with PAC Semantics for Multi-agent Beliefs. CoRR abs/2306.05490 (2023) - [i34]Argaman Mordoch, Shahaf S. Shperberg, Roni Stern, Brendan Juba:
Enhancing Numeric-SAM for Learning with Few Observations. CoRR abs/2312.10705 (2023) - [i33]Arijit Shaw, Brendan Juba, Kuldeep S. Meel:
An Approximate Skolem Function Counter. CoRR abs/2312.12026 (2023) - 2022
- [c44]Brendan Juba, Roni Stern:
Learning Probably Approximately Complete and Safe Action Models for Stochastic Worlds. AAAI 2022: 9795-9804 - [c43]Leda Liang, Brendan Juba:
Conditional Linear Regression for Heterogeneous Covariances. AISTATS 2022: 6182-6199 - [c42]Zihao Deng, Siddartha Devic, Brendan Juba:
Polynomial Time Reinforcement Learning in Factored State MDPs with Linear Value Functions. AISTATS 2022: 11280-11304 - [c41]Priyanka Golia, Brendan Juba, Kuldeep S. Meel:
A Scalable Shannon Entropy Estimator. CAV (1) 2022: 363-384 - [c40]Elena Grigorescu, Brendan Juba, Karl Wimmer, Ning Xie:
Hardness of Maximum Likelihood Learning of DPPs. COLT 2022: 3800-3819 - [i32]Brendan Juba, Roni Stern:
An Example of the SAM+ Algorithm for Learning Action Models for Stochastic Worlds. CoRR abs/2203.12499 (2022) - [i31]Elena Grigorescu, Brendan Juba, Karl Wimmer, Ning Xie:
Hardness of Maximum Likelihood Learning of DPPs. CoRR abs/2205.12377 (2022) - [i30]Priyanka Golia, Brendan Juba, Kuldeep S. Meel:
A Scalable Shannon Entropy Estimator. CoRR abs/2206.00921 (2022) - [i29]Elena Grigorescu, Brendan Juba, Karl Wimmer, Ning Xie:
Hardness of Maximum Likelihood Learning of DPPs. Electron. Colloquium Comput. Complex. TR22 (2022) - 2021
- [j6]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) - [c39]Mahdi Cheraghchi, Elena Grigorescu, Brendan Juba, Karl Wimmer, Ning Xie:
List Learning with Attribute Noise. AISTATS 2021: 2215-2223 - [c38]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 - [c37]Honghua Zhang, Brendan Juba, Guy Van den Broeck:
Probabilistic Generating Circuits. ICML 2021: 12447-12457 - [c36]Alexander Philipp Rader, Ionela G. Mocanu, Vaishak Belle, Brendan Juba:
Learning Implicitly with Noisy Data in Linear Arithmetic. IJCAI 2021: 1410-1417 - [c35]Brendan Juba, Hai S. Le, Roni Stern:
Safe Learning of Lifted Action Models. KR 2021: 379-389 - [i28]Honghua Zhang, Brendan Juba, Guy Van den Broeck:
Probabilistic Generating Circuits. CoRR abs/2102.09768 (2021) - [i27]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. CoRR abs/2103.15261 (2021) - [i26]Brendan Juba, Hai S. Le, Roni Stern:
Safe Learning of Lifted Action Models. CoRR abs/2107.04169 (2021) - [i25]Siddartha Devic, Zihao Deng, Brendan Juba:
Polynomial Time Reinforcement Learning in Correlated FMDPs with Linear Value Functions. CoRR abs/2107.05187 (2021) - [i24]Brendan Juba, Leda Liang:
Conditional Linear Regression for Heterogeneous Covariances. CoRR abs/2111.07834 (2021) - [i23]Zihao Deng, Zee Fryer, Brendan Juba, Rina Panigrahy, Xin Wang:
Provable Hierarchical Lifelong Learning with a Sketch-based Modular Architecture. CoRR abs/2112.10919 (2021) - 2020
- [c34]Brendan Juba, Hengxuan Li:
More Accurate Learning of k-DNF Reference Classes. AAAI 2020: 4385-4393 - [c33]Diego Calderon, Brendan Juba, Sirui Li, Zongyi Li, Lisa Ruan:
Conditional Linear Regression. AISTATS 2020: 2164-2173 - [c32]Ionela G. Mocanu, Vaishak Belle, Brendan Juba:
Polynomial-Time Implicit Learnability in SMT. ECAI 2020: 1152-1158 - [c31]Golnoosh Dehghanpoor, Michael D. Frachetti, Brendan Juba:
A Tensor Decomposition Method for Unsupervised Feature Learning on Satellite Imagery. IGARSS 2020: 1679-1682 - [i22]Mahdi Cheraghchi, Elena Grigorescu, Brendan Juba, Karl Wimmer, Ning Xie:
List Learning with Attribute Noise. CoRR abs/2006.06850 (2020) - [i21]Alexander Philipp Rader, Ionela G. Mocanu, Vaishak Belle, Brendan Juba:
Learning Implicitly with Noisy Data in Linear Arithmetic. CoRR abs/2010.12619 (2020)
2010 – 2019
- 2019
- [j5]Mark Braverman, Brendan Juba:
The Price of Uncertain Priors in Source Coding. IEEE Trans. Inf. Theory 65(2): 1165-1171 (2019) - [c30]Roni Stern, Brendan Juba:
Safe Partial Diagnosis from Normal Observations. AAAI 2019: 3084-3091 - [c29]Brendan Juba, Hai S. Le:
Precision-Recall versus Accuracy and the Role of Large Data Sets. AAAI 2019: 4039-4048 - [c28]Brendan Juba:
Polynomial-Time Probabilistic Reasoning with Partial Observations via Implicit Learning in Probability Logics. AAAI 2019: 7866-7875 - [c27]John Hainline, Brendan Juba, Hai S. Le, David P. Woodruff:
Conditional Sparse $L_p$-norm Regression With Optimal Probability. AISTATS 2019: 1042-1050 - [c26]Alexander Durgin, Brendan Juba:
Hardness of Improper One-Sided Learning of Conjunctions For All Uniformly Falsifiable CSPs. ALT 2019: 369-382 - [c25]Vaishak Belle, Brendan Juba:
Implicitly learning to reason in first-order logic. NeurIPS 2019: 3376-3386 - [i20]Vaishak Belle, Brendan Juba:
Implicitly Learning to Reason in First-Order Logic. CoRR abs/1906.10106 (2019) - [i19]Brendan Juba:
Query-driven PAC-Learning for Reasoning. CoRR abs/1906.10118 (2019) - 2018
- [j4]Mahdi Cheraghchi, Elena Grigorescu, Brendan Juba, Karl Wimmer, Ning Xie:
AC0∘MOD2 lower bounds for the Boolean Inner Product. J. Comput. Syst. Sci. 97: 45-59 (2018) - [c24]Brendan Juba, Zongyi Li, Evan Miller:
Learning Abduction Using Partial Observability. AAAI 2018: 1888-1896 - [c23]Diego Calderon, Brendan Juba, Zongyi Li, Lisa Ruan:
Conditional Linear Regression. AAAI 2018: 8055-8056 - [c22]Brendan Juba, Zongyi Li, Evan Miller:
Learning Abduction Under Partial Observability. AAAI 2018: 8097-8098 - [c21]Di Qi, Joshua Arfin, Mengxue Zhang, Tushar Mathew, Robert Pless, Brendan Juba:
Anomaly Explanation Using Metadata. WACV 2018: 1916-1924 - [i18]Diego Calderon, Brendan Juba, Sirui Li, Zongyi Li, Lisa Ruan:
Conditional Linear Regression. CoRR abs/1806.02326 (2018) - [i17]John Hainline, Brendan Juba, Hai S. Le, David P. Woodruff:
Conditional Sparse 𝓁p-norm Regression With Optimal Probability. CoRR abs/1806.10222 (2018) - [i16]Brendan Juba:
Polynomial-time probabilistic reasoning with partial observations via implicit learning in probability logics. CoRR abs/1806.11204 (2018) - [i15]Mark Braverman, Brendan Juba:
The Price of Uncertain Priors in Source Coding. CoRR abs/1811.08976 (2018) - [i14]Alexander Durgin, Brendan Juba:
Hardness of improper one-sided learning of conjunctions for all uniformly falsifiable CSPs. Electron. Colloquium Comput. Complex. TR18 (2018) - 2017
- [c20]Mengxue Zhang, Tushar Mathew, Brendan A. Juba:
An Improved Algorithm for Learning to Perform Exception-Tolerant Abduction. AAAI 2017: 1257-1265 - [c19]Zongyi Li, Brendan Juba, Evan Miller:
Learning Abduction under Partial Observability. AKBC@NIPS 2017 - [c18]Mithun Chakraborty, Kai Yee Phoebe Chua, Sanmay Das, Brendan Juba:
Coordinated Versus Decentralized Exploration In Multi-Agent Multi-Armed Bandits. IJCAI 2017: 164-170 - [c17]Roni Stern, Brendan Juba:
Efficient, Safe, and Probably Approximately Complete Learning of Action Models. IJCAI 2017: 4405-4411 - [c16]Brendan Juba:
Conditional Sparse Linear Regression. ITCS 2017: 45:1-45:14 - [i13]Roni Stern, Brendan Juba:
Efficient, Safe, and Probably Approximately Complete Learning of Action Models. CoRR abs/1705.08961 (2017) - [i12]Brendan Juba, Zongyi Li, Evan Miller:
Learning Abduction under Partial Observability. CoRR abs/1711.04438 (2017) - 2016
- [j3]Brendan Juba:
Integrated Common Sense Learning and Planning in POMDPs. J. Mach. Learn. Res. 17: 96:1-96:37 (2016) - [c15]Brendan Juba:
Learning Abductive Reasoning Using Random Examples. AAAI 2016: 999-1007 - [c14]Mahdi Cheraghchi, Elena Grigorescu, Brendan Juba, Karl Wimmer, Ning Xie:
AC^0 o MOD_2 Lower Bounds for the Boolean Inner Product. ICALP 2016: 35:1-35:14 - [i11]Brendan Juba:
Conditional Sparse Linear Regression. CoRR abs/1608.05152 (2016) - 2015
- [c13]Mark Braverman, Brendan Juba:
The price of uncertainty in communication. Allerton 2015: 924-927 - [c12]Brendan Juba:
Restricted Distribution Automatizability in PAC-Semantics. ITCS 2015: 93-102 - [c11]Brendan Juba, Christopher Musco, Fan Long, Stelios Sidiroglou-Douskos, Martin C. Rinard:
Principled Sampling for Anomaly Detection. NDSS 2015 - [i10]Mahdi Cheraghchi, Elena Grigorescu, Brendan Juba, Karl Wimmer, Ning Xie:
AC0 \circ MOD2 lower bounds for the Boolean Inner Product. Electron. Colloquium Comput. Complex. TR15 (2015) - 2013
- [c10]Brendan Juba:
Implicit Learning of Common Sense for Reasoning. IJCAI 2013: 939-946 - [c9]Brendan Juba, Ryan Williams:
Massive online teaching to bounded learners. ITCS 2013: 1-10 - [c8]Brendan Juba:
Compatibility among diversity Foundations, lessons, and directions of semantic communication. PerCom Workshops 2013: 458-463 - [i9]Brendan Juba:
PAC Quasi-automatizability of Resolution over Restricted Distributions. CoRR abs/1304.4633 (2013) - [i8]Brendan Juba:
On Non-automatizability in PAC-Semantics. Electron. Colloquium Comput. Complex. TR13 (2013) - 2012
- [j2]Oded Goldreich, Brendan Juba, Madhu Sudan:
A theory of goal-oriented communication. J. ACM 59(2): 8:1-8:65 (2012) - [j1]Brendan Juba:
On learning finite-state quantum sources. Quantum Inf. Comput. 12(1-2): 105-118 (2012) - [i7]Brendan Juba:
Learning implicitly in reasoning in PAC-Semantics. CoRR abs/1209.0056 (2012) - [i6]Brendan Juba, Ryan Williams:
Massive Online Teaching to Bounded Learners. Electron. Colloquium Comput. Complex. TR12 (2012) - 2011
- [b2]Brendan Juba:
Universal Semantic Communication. Springer 2011, ISBN 978-3-642-23296-1, pp. I-XIX, 1-397 - [c7]Brendan Juba, Santosh S. Vempala:
Semantic Communication for Simple Goals Is Equivalent to On-line Learning. ALT 2011: 277-291 - [c6]Brendan Juba, Madhu Sudan:
Efficient Semantic Communication via Compatible Beliefs. ICS 2011: 22-31 - [c5]Brendan Juba, Adam Tauman Kalai, Sanjeev Khanna, Madhu Sudan:
Compression without a common prior: an information-theoretic justification for ambiguity in language. ICS 2011: 79-86 - [c4]Oded Goldreich, Brendan Juba, Madhu Sudan:
A theory of goal-oriented communication. PODC 2011: 299-300 - [c3]Brendan Juba:
Reliable end-user communication under a changing packet network protocol. PODC 2011: 339-340 - 2010
- [b1]Brendan A. Juba:
Universal semantic communication. Massachusetts Institute of Technology, Cambridge, MA, USA, 2010 - [i5]Brendan Juba, Madhu Sudan:
Efficient Semantic Communication via Compatible Beliefs. Electron. Colloquium Comput. Complex. TR10 (2010)
2000 – 2009
- 2009
- [i4]Brendan Juba:
On Learning Finite-State Quantum Sources. CoRR abs/0910.3713 (2009) - [i3]Oded Goldreich, Brendan Juba, Madhu Sudan:
A Theory of Goal-Oriented Communication. Electron. Colloquium Comput. Complex. TR09 (2009) - 2008
- [c2]Brendan Juba, Madhu Sudan:
Universal semantic communication I. STOC 2008: 123-132 - [i2]Brendan Juba, Madhu Sudan:
Universal Semantic Communication II: A Theory of Goal-Oriented Communication. Electron. Colloquium Comput. Complex. TR08 (2008) - 2007
- [i1]Brendan Juba, Madhu Sudan:
Universal Semantic Communication I. Electron. Colloquium Comput. Complex. TR07 (2007) - 2006
- [c1]Brendan Juba:
Estimating relatedness via data compression. ICML 2006: 441-448
Coauthor Index
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