# Nika Haghtalab

According to our database

Collaborative distances:

^{1}, Nika Haghtalab authored at least 31 papers between 2014 and 2020.Collaborative distances:

## Timeline

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## Bibliography

2020

*k*-center Clustering under Perturbation Resilience.

ACM Trans. Algorithms, 2020

Ignorance Is Almost Bliss: Near-Optimal Stochastic Matching with Few Queries.

Oper. Res., 2020

Smoothed Analysis of Online and Differentially Private Learning.

CoRR, 2020

The disparate equilibria of algorithmic decision making when individuals invest rationally.

Proceedings of the FAT* '20: Conference on Fairness, 2020

2019

Computing Stackelberg Equilibria of Large General-Sum Games.

Proceedings of the Algorithmic Game Theory - 12th International Symposium, 2019

Toward a Characterization of Loss Functions for Distribution Learning.

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Structured Robust Submodular Maximization: Offline and Online Algorithms.

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

Algorithmic Greenlining: An Approach to Increase Diversity.

Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 2019

2018

The Provable Virtue of Laziness in Motion Planning.

Proceedings of the Twenty-Eighth International Conference on Automated Planning and Scheduling, 2018

Weighted Voting Via No-Regret Learning.

Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

Algorithms for Generalized Topic Modeling.

Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017

Monitoring stealthy diffusion.

Knowl. Inf. Syst., 2017

Robust Submodular Maximization: Offline and Online Algorithms.

CoRR, 2017

Opting Into Optimal Matchings.

Proceedings of the Twenty-Eighth Annual ACM-SIAM Symposium on Discrete Algorithms, 2017

Online Learning with a Hint.

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Collaborative PAC Learning.

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Oracle-Efficient Online Learning and Auction Design.

Proceedings of the 58th IEEE Annual Symposium on Foundations of Computer Science, 2017

Efficient PAC Learning from the Crowd.

Proceedings of the 30th Conference on Learning Theory, 2017

2016

Oracle-Efficient Learning and Auction Design.

CoRR, 2016

Generalized Topic Modeling.

CoRR, 2016

Three Strategies to Success: Learning Adversary Models in Security Games.

Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, 2016

k-Center Clustering Under Perturbation Resilience.

Proceedings of the 43rd International Colloquium on Automata, Languages, and Programming, 2016

Learning and 1-bit Compressed Sensing under Asymmetric Noise.

Proceedings of the 29th Conference on Learning Theory, 2016

2015

Symmetric and Asymmetric $k$-center Clustering under Stability.

CoRR, 2015

Commitment Without Regrets: Online Learning in Stackelberg Security Games.

Proceedings of the Sixteenth ACM Conference on Economics and Computation, 2015

Variational Dropout and the Local Reparameterization Trick.

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Efficient Learning of Linear Separators under Bounded Noise.

Proceedings of The 28th Conference on Learning Theory, 2015

2014

Ignorance is Almost Bliss: Near-Optimal Stochastic Matching With Few Queries.

CoRR, 2014

Learning Optimal Commitment to Overcome Insecurity.

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Clustering in the Presence of Background Noise.

Proceedings of the 31th International Conference on Machine Learning, 2014

Lazy Defenders Are Almost Optimal against Diligent Attackers.

Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, 2014