Estimating relatedness via data compression. - Brendan Juba (ICML, 2006)

Universal Semantic Communication I. - Brendan Juba; Madhu Sudan 001 (Electron. Colloquium Comput. Complex., 2007)

Universal Semantic Communication II: A Theory of Goal-Oriented Communication. - Brendan Juba; Madhu Sudan 001 (Electron. Colloquium Comput. Complex., 2008)

A Theory of Goal-Oriented Communication. - Oded Goldreich 001; Brendan Juba; Madhu Sudan 001 (Electron. Colloquium Comput. Complex., 2009)

Efficient Semantic Communication via Compatible Beliefs. - Brendan Juba; Madhu Sudan 001 (Electron. Colloquium Comput. Complex., 2010)

Universal semantic communication. - Brendan A. Juba (2010)

Universal Semantic Communication. - Brendan Juba (Springer, 2011)

Semantic Communication for Simple Goals Is Equivalent to On-line Learning. - Brendan Juba; Santosh S. Vempala (ALT, 2011)

Compression without a common prior: an information-theoretic justification for ambiguity in language. - Brendan Juba; Adam Tauman Kalai; Sanjeev Khanna; Madhu Sudan 001 (ICS, 2011)

Reliable end-user communication under a changing packet network protocol. - Brendan Juba (PODC, 2011)

Learning implicitly in reasoning in PAC-Semantics - Brendan Juba (CoRR, 2012)

Massive Online Teaching to Bounded Learners. - Brendan Juba; Ryan Williams 001 (Electron. Colloquium Comput. Complex., 2012)

On learning finite-state quantum sources. - Brendan Juba (Quantum Inf. Comput., 2012)

Implicit Learning of Common Sense for Reasoning. - Brendan Juba (IJCAI, 2013)

Compatibility among diversity Foundations, lessons, and directions of semantic communication. - Brendan Juba (PerCom Workshops, 2013)

PAC Quasi-automatizability of Resolution over Restricted Distributions - Brendan Juba (CoRR, 2013)

On Non-automatizability in PAC-Semantics. - Brendan Juba (Electron. Colloquium Comput. Complex., 2013)

The price of uncertainty in communication. - Mark Braverman; Brendan Juba (Allerton, 2015)

Restricted Distribution Automatizability in PAC-Semantics. - Brendan Juba (ITCS, 2015)

Principled Sampling for Anomaly Detection. - Brendan Juba; Christopher Musco; Fan Long; Stelios Sidiroglou-Douskos; Martin C. Rinard (NDSS, 2015)

AC0 circ MOD2 lower bounds for the Boolean Inner Product. - Mahdi Cheraghchi; Elena Grigorescu; Brendan Juba; Karl Wimmer; Ning Xie 002 (Electron. Colloquium Comput. Complex., 2015)

Learning Abductive Reasoning Using Random Examples. - Brendan Juba (AAAI, 2016)

AC^0 o MOD_2 Lower Bounds for the Boolean Inner Product. - Mahdi Cheraghchi; Elena Grigorescu; Brendan Juba; Karl Wimmer; Ning Xie 002 (ICALP, 2016)

Integrated Common Sense Learning and Planning in POMDPs. - Brendan Juba (J. Mach. Learn. Res., 2016)

An Improved Algorithm for Learning to Perform Exception-Tolerant Abduction. - Mengxue Zhang; Tushar Mathew; Brendan A. Juba (AAAI, 2017)

Learning Abduction under Partial Observability. - Zongyi Li; Brendan Juba; Evan Miller (AKBC@NIPS, 2017)

Coordinated Versus Decentralized Exploration In Multi-Agent Multi-Armed Bandits. - Mithun Chakraborty; Kai Yee Phoebe Chua; Sanmay Das; Brendan Juba (IJCAI, 2017)

Efficient, Safe, and Probably Approximately Complete Learning of Action Models. - Roni Stern; Brendan Juba (IJCAI, 2017)

Conditional Sparse Linear Regression. - Brendan Juba (ITCS, 2017)

Conditional Linear Regression. - Diego Calderon; Brendan Juba; Zongyi Li; Lisa Ruan (AAAI, 2018)

Learning Abduction Using Partial Observability. - Brendan Juba; Zongyi Li; Evan Miller (AAAI, 2018)

Anomaly Explanation Using Metadata. - Di Qi; Joshua Arfin; Mengxue Zhang; Tushar Mathew; Robert Pless; Brendan Juba (WACV, 2018)

Conditional Sparse 𝓁p-norm Regression With Optimal Probability. - John Hainline; Brendan Juba; Hai S. Le; David P. Woodruff (CoRR, 2018)

Hardness of improper one-sided learning of conjunctions for all uniformly falsifiable CSPs. - Alexander Durgin; Brendan Juba (Electron. Colloquium Comput. Complex., 2018)

AC0∘MOD2 lower bounds for the Boolean Inner Product. - Mahdi Cheraghchi; Elena Grigorescu; Brendan Juba; Karl Wimmer; Ning Xie 002 (J. Comput. Syst. Sci., 2018)

Polynomial-Time Probabilistic Reasoning with Partial Observations via Implicit Learning in Probability Logics. - Brendan Juba (AAAI, 2019)

Precision-Recall versus Accuracy and the Role of Large Data Sets. - Brendan Juba; Hai S. Le (AAAI, 2019)

Safe Partial Diagnosis from Normal Observations. - Roni Stern; Brendan Juba (AAAI, 2019)

Conditional Sparse $L_p$-norm Regression With Optimal Probability. - John Hainline; Brendan Juba; Hai S. Le; David P. Woodruff (AISTATS, 2019)

Implicitly learning to reason in first-order logic. - Vaishak Belle; Brendan Juba (NeurIPS, 2019)

Query-driven PAC-Learning for Reasoning. - Brendan Juba (CoRR, 2019)

The Price of Uncertain Priors in Source Coding. - Mark Braverman; Brendan Juba (IEEE Trans. Inf. Theory, 2019)

More Accurate Learning of k-DNF Reference Classes. - Brendan Juba; Hengxuan Li (AAAI, 2020)

Conditional Linear Regression. - Diego Calderon; Brendan Juba; Sirui Li; Zongyi Li; Lisa Ruan (AISTATS, 2020)

Polynomial-Time Implicit Learnability in SMT. - Ionela G. Mocanu; Vaishak Belle; Brendan Juba (ECAI, 2020)

A Tensor Decomposition Method for Unsupervised Feature Learning on Satellite Imagery. - Golnoosh Dehghanpoor; Michael D. Frachetti; Brendan Juba (IGARSS, 2020)

List Learning with Attribute Noise. - Mahdi Cheraghchi; Elena Grigorescu; Brendan Juba; Karl Wimmer; Ning Xie 002 (AISTATS, 2021)

One Network Fits All? Modular versus Monolithic Task Formulations in Neural Networks. - Atish Agarwala; Abhimanyu Das; Brendan Juba; Rina Panigrahy; Vatsal Sharan; Xin Wang 116; Qiuyi Zhang 001 (ICLR, 2021)

Probabilistic Generating Circuits. - Honghua Zhang; Brendan Juba; Guy Van den Broeck (ICML, 2021)

Learning Implicitly with Noisy Data in Linear Arithmetic. - Alexander Philipp Rader; Ionela G. Mocanu; Vaishak Belle; Brendan Juba (IJCAI, 2021)

Safe Learning of Lifted Action Models. - Brendan Juba; Hai S. Le; Roni Stern (KR, 2021)

Polynomial Time Reinforcement Learning in Correlated FMDPs with Linear Value Functions. - Siddartha Devic; Zihao Deng; Brendan Juba (CoRR, 2021)

Provable Hierarchical Lifelong Learning with a Sketch-based Modular Architecture. - Zihao Deng; Zee Fryer; Brendan Juba; Rina Panigrahy; Xin Wang 116 (CoRR, 2021)

Safe Multi-Agent Pathfinding with Time Uncertainty. - Tomer Shahar; Shashank Shekhar 002; Dor Atzmon; Abdallah Saffidine; Brendan Juba; Roni Stern (J. Artif. Intell. Res., 2021)

Learning Probably Approximately Complete and Safe Action Models for Stochastic Worlds. - Brendan Juba; Roni Stern (AAAI, 2022)

Polynomial Time Reinforcement Learning in Factored State MDPs with Linear Value Functions. - Zihao Deng; Siddartha Devic; Brendan Juba (AISTATS, 2022)

Conditional Linear Regression for Heterogeneous Covariances. - Leda Liang; Brendan Juba (AISTATS, 2022)

A Scalable Shannon Entropy Estimator. - Priyanka Golia; Brendan Juba; Kuldeep S. Meel (CAV (1), 2022)

An Example of the SAM+ Algorithm for Learning Action Models for Stochastic Worlds. - Brendan Juba; Roni Stern (CoRR, 2022)

Hardness of Maximum Likelihood Learning of DPPs. - Elena Grigorescu; Brendan Juba; Karl Wimmer; Ning Xie 002 (Electron. Colloquium Comput. Complex., 2022)

Popularizing Fairness: Group Fairness and Individual Welfare. - Andrew Estornell; Sanmay Das; Brendan Juba; Yevgeniy Vorobeychik (AAAI, 2023)

Learning Safe Numeric Action Models. - Argaman Mordoch; Brendan Juba; Roni Stern (AAAI, 2023)

Enhancing Numeric-SAM for Learning with Few Observations. - Argaman Mordoch; Shahaf S. Shperberg; Roni Stern; Brendan Juba (CoRR, 2023)

The (Un)Scalability of Informed Heuristic Function Estimation in NP-Hard Search Problems. - Sumedh Pendurkar; Taoan Huang; Brendan Juba; Jiapeng Zhang; Sven Koenig; Guni Sharon (Trans. Mach. Learn. Res., 2023)

Learnability with PAC Semantics for Multi-agent Beliefs. - Ionela G. Mocanu; Vaishak Belle; Brendan Juba (Theory Pract. Log. Program., 2023)

Learning Safe Action Models with Partial Observability. - Hai S. Le; Brendan Juba; Roni Stern (AAAI, 2024)

An Approximate Skolem Function Counter. - Arijit Shaw; Brendan Juba; Kuldeep S. Meel (AAAI, 2024)

Distribution-Specific Auditing for Subgroup Fairness. - Daniel Hsu 001; Jizhou Huang; Brendan Juba (FORC, 2024)

Safe Learning of PDDL Domains with Conditional Effects. - Argaman Mordoch; Enrico Scala; Roni Stern; Brendan Juba (ICAPS, 2024)

The Impact of Features Used by Algorithms on Perceptions of Fairness. - Andrew Estornell; Tina Zhang; Sanmay Das; Chien-Ju Ho; Brendan Juba; Yevgeniy Vorobeychik (IJCAI, 2024)

Polynomial time auditing of statistical subgroup fairness for Gaussian data. - Daniel Hsu 001; Jizhou Huang; Brendan Juba (CoRR, 2024)

Safe Learning of PDDL Domains with Conditional Effects - Extended Version. - Argaman Mordoch; Enrico Scala; Roni Stern; Brendan Juba (CoRR, 2024)

Learning Linear Utility Functions From Pairwise Comparison Queries. - Luise Ge; Brendan Juba; Yevgeniy Vorobeychik (CoRR, 2024)

Data for An Approximate Skolem Funcion Counter (AAAI-24 paper). - Arijit Shaw; Kuldeep S. Meel; Brendan Juba (Zenodo, 2024)

Distribution-Specific Agnostic Conditional Classification With Halfspaces. - Jizhou Huang; Brendan Juba (ICLR, 2025)

Polynomial-Time Relational Probabilistic Inference in Open Universes. - Luise Ge; Brendan Juba; Kris Nilsson (CoRR, 2025)

A scalable entropy estimator. - Priyanka Golia; Brendan Juba; Kuldeep S. Meel (Formal Methods Syst. Des., 2025)