default search action
Nika Haghtalab
Person information
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2024
- [j8]Nika Haghtalab
, Tim Roughgarden
Smoothed Analysis with Adaptive Adversaries. J. ACM 71(3): 19 (2024) - [j7]Wenshuo Guo, Nika Haghtalab, Kirthevasan Kandasamy, Ellen Vitercik:
Leveraging Reviews: Learning to Price with Buyer and Seller Uncertainty. SIGecom Exch. 22(1): 74-82 (2024) - [c46]Nivasini Ananthakrishnan, Stephen Bates, Michael I. Jordan, Nika Haghtalab:
Delegating Data Collection in Decentralized Machine Learning. AISTATS 2024: 478-486 - [c45]Jessica Dai, Bailey Flanigan, Meena Jagadeesan, Nika Haghtalab, Chara Podimata:
Can Probabilistic Feedback Drive User Impacts in Online Platforms? AISTATS 2024: 2512-2520 - [c44]Danny Halawi, Alexander Wei, Eric Wallace, Tony Tong Wang, Nika Haghtalab, Jacob Steinhardt:
Covert Malicious Finetuning: Challenges in Safeguarding LLM Adaptation. ICML 2024 - [c43]Constantinos Daskalakis, Noah Golowich, Nika Haghtalab, Abhishek Shetty:
Smooth Nash Equilibria: Algorithms and Complexity. ITCS 2024: 37:1-37:22 - [c42]Nika Haghtalab, Nicole Immorlica, Brendan Lucier, Markus Mobius, Divyarthi Mohan:
Communicating with Anecdotes (Extended Abstract). ITCS 2024: 57:1-57:2 - [i40]Jessica Dai, Bailey Flanigan, Nika Haghtalab, Meena Jagadeesan, Chara Podimata:
Can Probabilistic Feedback Drive User Impacts in Online Platforms? CoRR abs/2401.05304 (2024) - [i39]Nika Haghtalab, Mingda Qiao, Kunhe Yang:
Platforms for Efficient and Incentive-Aware Collaboration. CoRR abs/2402.15169 (2024) - [i38]Danny Halawi, Alexander Wei, Eric Wallace, Tony T. Wang, Nika Haghtalab, Jacob Steinhardt:
Covert Malicious Finetuning: Challenges in Safeguarding LLM Adaptation. CoRR abs/2406.20053 (2024) - [i37]Nika Haghtalab, Mingda Qiao, Kunhe Yang, Eric Zhao:
Truthfulness of Calibration Measures. CoRR abs/2407.13979 (2024) - [i36]Nivasini Ananthakrishnan, Nika Haghtalab, Chara Podimata, Kunhe Yang:
Is Knowledge Power? On the (Im)possibility of Learning from Strategic Interaction. CoRR abs/2408.08272 (2024) - [i35]Emilio Calvano, Nika Haghtalab, Ellen Vitercik, Eric Zhao:
Algorithmic Content Selection and the Impact of User Disengagement. CoRR abs/2410.13108 (2024) - [i34]Jessica Dai, Nika Haghtalab, Eric Zhao:
Learning With Multi-Group Guarantees For Clusterable Subpopulations. CoRR abs/2410.14588 (2024) - 2023
- [c41]Meena Jagadeesan, Michael I. Jordan, Nika Haghtalab:
Competition, Alignment, and Equilibria in Digital Marketplaces. AAAI 2023: 5689-5696 - [c40]Pranjal Awasthi, Nika Haghtalab, Eric Zhao:
Open Problem: The Sample Complexity of Multi-Distribution Learning for VC Classes. COLT 2023: 5943-5949 - [c39]Alexander Wei, Nika Haghtalab, Jacob Steinhardt:
Jailbroken: How Does LLM Safety Training Fail? NeurIPS 2023 - [c38]Alankrita Bhatt, Nika Haghtalab, Abhishek Shetty:
Smoothed Analysis of Sequential Probability Assignment. NeurIPS 2023 - [c37]Nika Haghtalab, Michael I. Jordan, Eric Zhao:
A Unifying Perspective on Multi-Calibration: Game Dynamics for Multi-Objective Learning. NeurIPS 2023 - [c36]Nika Haghtalab, Chara Podimata, Kunhe Yang:
Calibrated Stackelberg Games: Learning Optimal Commitments Against Calibrated Agents. NeurIPS 2023 - [c35]Meena Jagadeesan, Michael I. Jordan, Jacob Steinhardt, Nika Haghtalab:
Improved Bayes Risk Can Yield Reduced Social Welfare Under Competition. NeurIPS 2023 - [c34]Naveen Durvasula
Smoothed Analysis of Online Non-parametric Auctions. EC 2023: 540-560 - [c33]Wenshuo Guo
Leveraging Reviews: Learning to Price with Buyer and Seller Uncertainty. EC 2023: 816 - [c32]Mahsa Derakhshan, Naveen Durvasula, Nika Haghtalab:
Stochastic Minimum Vertex Cover in General Graphs: A 3/2-Approximation. STOC 2023: 242-253 - [i33]Mahsa Derakhshan, Naveen Durvasula, Nika Haghtalab:
Stochastic Minimum Vertex Cover in General Graphs: a 3/2-Approximation. CoRR abs/2302.02567 (2023) - [i32]Wenshuo Guo, Nika Haghtalab, Kirthevasan Kandasamy, Ellen Vitercik:
Leveraging Reviews: Learning to Price with Buyer and Seller Uncertainty. CoRR abs/2302.09700 (2023) - [i31]Nika Haghtalab, Michael I. Jordan, Eric Zhao:
A Unifying Perspective on Multi-Calibration: Unleashing Game Dynamics for Multi-Objective Learning. CoRR abs/2302.10863 (2023) - [i30]Alankrita Bhatt, Nika Haghtalab, Abhishek Shetty:
Smoothed Analysis of Sequential Probability Assignment. CoRR abs/2303.04845 (2023) - [i29]Nika Haghtalab, Chara Podimata, Kunhe Yang:
Calibrated Stackelberg Games: Learning Optimal Commitments Against Calibrated Agents. CoRR abs/2306.02704 (2023) - [i28]Meena Jagadeesan, Michael I. Jordan, Jacob Steinhardt, Nika Haghtalab:
Improved Bayes Risk Can Yield Reduced Social Welfare Under Competition. CoRR abs/2306.14670 (2023) - [i27]Alexander Wei, Nika Haghtalab, Jacob Steinhardt:
Jailbroken: How Does LLM Safety Training Fail? CoRR abs/2307.02483 (2023) - [i26]Pranjal Awasthi, Nika Haghtalab, Eric Zhao:
The Sample Complexity of Multi-Distribution Learning for VC Classes. CoRR abs/2307.12135 (2023) - [i25]Nivasini Ananthakrishnan, Stephen Bates, Michael I. Jordan, Nika Haghtalab:
Delegating Data Collection in Decentralized Machine Learning. CoRR abs/2309.01837 (2023) - [i24]Constantinos Daskalakis, Noah Golowich, Nika Haghtalab, Abhishek Shetty:
Smooth Nash Equilibria: Algorithms and Complexity. CoRR abs/2309.12226 (2023) - 2022
- [c31]Nika Haghtalab, Yanjun Han, Abhishek Shetty, Kunhe Yang:
Oracle-Efficient Online Learning for Smoothed Adversaries. NeurIPS 2022 - [c30]Nika Haghtalab, Michael I. Jordan, Eric Zhao:
On-Demand Sampling: Learning Optimally from Multiple Distributions. NeurIPS 2022 - [c29]Nika Haghtalab, Thodoris Lykouris
Learning in Stackelberg Games with Non-myopic Agents. EC 2022: 917-918 - [e1]Sanjoy Dasgupta, Nika Haghtalab:
International Conference on Algorithmic Learning Theory, 29 March - 1 April 2022, Paris, France. Proceedings of Machine Learning Research 167, PMLR 2022 [contents] - [i23]Nika Haghtalab, Yanjun Han, Abhishek Shetty, Kunhe Yang:
Oracle-Efficient Online Learning for Beyond Worst-Case Adversaries. CoRR abs/2202.08549 (2022) - [i22]Nika Haghtalab, Nicole Immorlica, Brendan Lucier, Markus Mobius, Divyarthi Mohan:
Communicating with Anecdotes. CoRR abs/2205.13461 (2022) - [i21]Nika Haghtalab, Thodoris Lykouris, Sloan Nietert, Alex Wei:
Learning in Stackelberg Games with Non-myopic Agents. CoRR abs/2208.09407 (2022) - [i20]Meena Jagadeesan, Michael I. Jordan, Nika Haghtalab:
Competition, Alignment, and Equilibria in Digital Marketplaces. CoRR abs/2208.14423 (2022) - [i19]Nika Haghtalab, Michael I. Jordan, Eric Zhao:
On-Demand Sampling: Learning Optimally from Multiple Distributions. CoRR abs/2210.12529 (2022) - 2021
- [j6]Alfredo Torrico
Structured Robust Submodular Maximization: Offline and Online Algorithms. INFORMS J. Comput. 33(4): 1590-1607 (2021) - [j5]Nika Haghtalab, Matthew O. Jackson
Belief polarization in a complex world: A learning theory perspective. Proc. Natl. Acad. Sci. USA 118(19): e2010144118 (2021) - [c28]Nika Haghtalab, Tim Roughgarden, Abhishek Shetty:
Smoothed Analysis with Adaptive Adversaries. FOCS 2021: 942-953 - [c27]Avrim Blum, Nika Haghtalab, Richard Lanas Phillips, Han Shao:
One for One, or All for All: Equilibria and Optimality of Collaboration in Federated Learning. ICML 2021: 1005-1014 - [i18]Nika Haghtalab, Tim Roughgarden, Abhishek Shetty:
Smoothed Analysis with Adaptive Adversaries. CoRR abs/2102.08446 (2021) - [i17]Avrim Blum, Nika Haghtalab, Richard Lanas Phillips, Han Shao:
One for One, or All for All: Equilibria and Optimality of Collaboration in Federated Learning. CoRR abs/2103.03228 (2021) - 2020
- [j4]Avrim Blum
Ignorance Is Almost Bliss: Near-Optimal Stochastic Matching with Few Queries. Oper. Res. 68(1): 16-34 (2020) - [j3]Miroslav Dudík, Nika Haghtalab, Haipeng Luo, Robert E. Schapire, Vasilis Syrgkanis, Jennifer Wortman Vaughan:
Oracle-efficient Online Learning and Auction Design. J. ACM 67(5): 26:1-26:57 (2020) - [j2]Maria-Florina Balcan, Nika Haghtalab, Colin White:
k-center Clustering under Perturbation Resilience. ACM Trans. Algorithms 16(2): 22:1-22:39 (2020) - [c26]Lydia T. Liu, Ashia Wilson, Nika Haghtalab, Adam Tauman Kalai, Christian Borgs
The disparate equilibria of algorithmic decision making when individuals invest rationally. FAT* 2020: 381-391 - [c25]Nika Haghtalab, Nicole Immorlica, Brendan Lucier, Jack Z. Wang:
Maximizing Welfare with Incentive-Aware Evaluation Mechanisms. IJCAI 2020: 160-166 - [c24]Nika Haghtalab, Tim Roughgarden, Abhishek Shetty:
Smoothed Analysis of Online and Differentially Private Learning. NeurIPS 2020 - [p1]Maria-Florina Balcan, Nika Haghtalab:
Noise in Classification. Beyond the Worst-Case Analysis of Algorithms 2020: 361-381 - [i16]Nika Haghtalab, Tim Roughgarden, Abhishek Shetty:
Smoothed Analysis of Online and Differentially Private Learning. CoRR abs/2006.10129 (2020) - [i15]Maria-Florina Balcan, Nika Haghtalab:
Noise in Classification. CoRR abs/2010.05080 (2020) - [i14]Nika Haghtalab, Nicole Immorlica, Brendan Lucier, Jack Z. Wang:
Maximizing Welfare with Incentive-Aware Evaluation Mechanisms. CoRR abs/2011.01956 (2020)
2010 – 2019
- 2019
- [c23]Christian Borgs
Algorithmic Greenlining: An Approach to Increase Diversity. AIES 2019: 69-76 - [c22]Nima Anari, Nika Haghtalab, Seffi Naor, Sebastian Pokutta, Mohit Singh, Alfredo Torrico:
Structured Robust Submodular Maximization: Offline and Online Algorithms. AISTATS 2019: 3128-3137 - [c21]Nika Haghtalab, Simon Mackenzie, Ariel D. Procaccia, Oren Salzman, Siddhartha S. Srinivasa:
The Provable Virtue of Laziness in Motion Planning. IJCAI 2019: 6161-6165 - [c20]Nika Haghtalab, Cameron Musco, Bo Waggoner:
Toward a Characterization of Loss Functions for Distribution Learning. NeurIPS 2019: 7235-7244 - [c19]Avrim Blum, Nika Haghtalab, MohammadTaghi Hajiaghayi, Saeed Seddighin:
Computing Stackelberg Equilibria of Large General-Sum Games. SAGT 2019: 168-182 - [i13]Nika Haghtalab, Cameron Musco, Bo Waggoner:
Toward a Characterization of Loss Functions for Distribution Learning. CoRR abs/1906.02652 (2019) - [i12]Avrim Blum, Nika Haghtalab, MohammadTaghi Hajiaghayi, Saeed Seddighin:
Computing Stackelberg Equilibria of Large General-Sum Games. CoRR abs/1909.03319 (2019) - [i11]Lydia T. Liu, Ashia Wilson, Nika Haghtalab, Adam Tauman Kalai, Christian Borgs, Jennifer T. Chayes:
The Disparate Equilibria of Algorithmic Decision Making when Individuals Invest Rationally. CoRR abs/1910.04123 (2019) - 2018
- [c18]Nika Haghtalab, Ritesh Noothigattu, Ariel D. Procaccia:
Weighted Voting Via No-Regret Learning. AAAI 2018: 1055-1062 - [c17]Avrim Blum, Nika Haghtalab:
Algorithms for Generalized Topic Modeling. AAAI 2018: 2730-2737 - [c16]Nika Haghtalab, Simon Mackenzie, Ariel D. Procaccia, Oren Salzman, Siddhartha S. Srinivasa:
The Provable Virtue of Laziness in Motion Planning. ICAPS 2018: 106-113 - 2017
- [j1]Nika Haghtalab
Monitoring stealthy diffusion. Knowl. Inf. Syst. 52(3): 657-685 (2017) - [c15]Pranjal Awasthi, Avrim Blum, Nika Haghtalab, Yishay Mansour:
Efficient PAC Learning from the Crowd. COLT 2017: 127-150 - [c14]Miroslav Dudík, Nika Haghtalab, Haipeng Luo, Robert E. Schapire, Vasilis Syrgkanis, Jennifer Wortman Vaughan:
Oracle-Efficient Online Learning and Auction Design. FOCS 2017: 528-539 - [c13]Avrim Blum, Nika Haghtalab, Ariel D. Procaccia, Mingda Qiao:
Collaborative PAC Learning. NIPS 2017: 2392-2401 - [c12]Ofer Dekel, Arthur Flajolet, Nika Haghtalab, Patrick Jaillet:
Online Learning with a Hint. NIPS 2017: 5299-5308 - [c11]Avrim Blum, Ioannis Caragiannis, Nika Haghtalab, Ariel D. Procaccia, Eviatar B. Procaccia, Rohit Vaish:
Opting Into Optimal Matchings. SODA 2017: 2351-2363 - [i10]Nika Haghtalab, Ritesh Noothigattu, Ariel D. Procaccia:
Weighted Voting Via No-Regret Learning. CoRR abs/1703.04756 (2017) - [i9]Pranjal Awasthi, Avrim Blum, Nika Haghtalab, Yishay Mansour:
Efficient PAC Learning from the Crowd. CoRR abs/1703.07432 (2017) - [i8]Nika Haghtalab, Simon Mackenzie, Ariel D. Procaccia, Oren Salzman, Siddhartha S. Srinivasa:
The Provable Virtue of Laziness in Motion Planning. CoRR abs/1710.04101 (2017) - [i7]Nima Anari, Nika Haghtalab, Joseph Naor, Sebastian Pokutta, Mohit Singh, Alfredo Torrico:
Robust Submodular Maximization: Offline and Online Algorithms. CoRR abs/1710.04740 (2017) - 2016
- [c10]Pranjal Awasthi, Maria-Florina Balcan, Nika Haghtalab, Hongyang Zhang:
Learning and 1-bit Compressed Sensing under Asymmetric Noise. COLT 2016: 152-192 - [c9]Maria-Florina Balcan, Nika Haghtalab, Colin White:
k-Center Clustering Under Perturbation Resilience. ICALP 2016: 68:1-68:14 - [c8]Nika Haghtalab, Fei Fang, Thanh Hong Nguyen, Arunesh Sinha, Ariel D. Procaccia, Milind Tambe:
Three Strategies to Success: Learning Adversary Models in Security Games. IJCAI 2016: 308-314 - [i6]Avrim Blum, Ioannis Caragiannis, Nika Haghtalab, Ariel D. Procaccia, Eviatar B. Procaccia, Rohit Vaish:
Opting Into Optimal Matchings. CoRR abs/1609.04051 (2016) - [i5]Avrim Blum, Nika Haghtalab:
Generalized Topic Modeling. CoRR abs/1611.01259 (2016) - [i4]Miroslav Dudík, Nika Haghtalab, Haipeng Luo, Robert E. Schapire, Vasilis Syrgkanis, Jennifer Wortman Vaughan:
Oracle-Efficient Learning and Auction Design. CoRR abs/1611.01688 (2016) - 2015
- [c7]Pranjal Awasthi, Maria-Florina Balcan, Nika Haghtalab, Ruth Urner:
Efficient Learning of Linear Separators under Bounded Noise. COLT 2015: 167-190 - [c6]Nika Haghtalab, Aron Laszka
Monitoring Stealthy Diffusion. ICDM 2015: 151-160 - [c5]Maria-Florina Balcan, Avrim Blum, Nika Haghtalab, Ariel D. Procaccia:
Commitment Without Regrets: Online Learning in Stackelberg Security Games. EC 2015: 61-78 - [c4]Avrim Blum, John P. Dickerson, Nika Haghtalab, Ariel D. Procaccia, Tuomas Sandholm, Ankit Sharma:
Ignorance is Almost Bliss: Near-Optimal Stochastic Matching With Few Queries. EC 2015: 325-342 - [i3]Pranjal Awasthi, Maria-Florina Balcan, Nika Haghtalab, Ruth Urner:
Efficient Learning of Linear Separators under Bounded Noise. CoRR abs/1503.03594 (2015) - [i2]Maria-Florina Balcan, Nika Haghtalab, Colin White:
Symmetric and Asymmetric $k$-center Clustering under Stability. CoRR abs/1505.03924 (2015) - 2014
- [c3]Avrim Blum, Nika Haghtalab, Ariel D. Procaccia:
Lazy Defenders Are Almost Optimal against Diligent Attackers. AAAI 2014: 573-579 - [c2]Shai Ben-David, Nika Haghtalab:
Clustering in the Presence of Background Noise. ICML 2014: 280-288 - [c1]Avrim Blum, Nika Haghtalab, Ariel D. Procaccia:
Learning Optimal Commitment to Overcome Insecurity. NIPS 2014: 1826-1834 - [i1]Avrim Blum, Nika Haghtalab, Ariel D. Procaccia, Ankit Sharma:
Ignorance is Almost Bliss: Near-Optimal Stochastic Matching With Few Queries. CoRR abs/1407.4094 (2014)
Coauthor Index
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from ,
, and
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and
to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from .
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-12-05 20:43 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by: