Haghtalab, Nika

37 publications

COLT 2025 Conference on Learning Theory 2025: Preface Nika Haghtalab, Ankur Moitra

NeurIPS 2025 Distortion of AI Alignment: Does Preference Optimization Optimize for Preferences? Paul Gölz, Nika Haghtalab, Kunhe Yang

NeurIPS 2025 From Style to Facts: Mapping the Boundaries of Knowledge Injection with Finetuning Eric Zhao, Pranjal Awasthi, Nika Haghtalab

ICML 2025 Learning with Multi-Group Guarantees for Clusterable Subpopulations Jessica Dai, Nika Haghtalab, Eric Zhao

NeurIPS 2025 Sample-Adaptivity Tradeoff in On-Demand Sampling Nika Haghtalab, Omar Montasser, Mingda Qiao

AISTATS 2024 Can Probabilistic Feedback Drive User Impacts in Online Platforms? Jessica Dai, Bailey Flanigan, Nika Haghtalab, Meena Jagadeesan, Chara Podimata

ICML 2024 Covert Malicious Finetuning: Challenges in Safeguarding LLM Adaptation Danny Halawi, Alexander Wei, Eric Wallace, Tony Tong Wang, Nika Haghtalab, Jacob Steinhardt

AISTATS 2024 Delegating Data Collection in Decentralized Machine Learning Nivasini Ananthakrishnan, Stephen Bates, Michael Jordan, Nika Haghtalab

NeurIPS 2024 Is Knowledge Power? on the (Im)possibility of Learning from Strategic Interactions Nivasini Ananthakrishnan, Nika Haghtalab, Chara Podimata, Kunhe Yang

NeurIPS 2024 Truthfulness of Calibration Measures Nika Haghtalab, Mingda Qiao, Kunhe Yang, Eric Zhao

NeurIPS 2023 A Unifying Perspective on Multi-Calibration: Game Dynamics for Multi-Objective Learning Nika Haghtalab, Michael I. Jordan, Eric Zhao

NeurIPS 2023 Calibrated Stackelberg Games: Learning Optimal Commitments Against Calibrated Agents Nika Haghtalab, Chara Podimata, Kunhe Yang

AAAI 2023 Competition, Alignment, and Equilibria in Digital Marketplaces Meena Jagadeesan, Michael I. Jordan, Nika Haghtalab

NeurIPS 2023 Improved Bayes Risk Can Yield Reduced Social Welfare Under Competition Meena Jagadeesan, Michael I. Jordan, Jacob Steinhardt, Nika Haghtalab

NeurIPS 2023 Jailbroken: How Does LLM Safety Training Fail? Alexander Wei, Nika Haghtalab, Jacob Steinhardt

COLT 2023 Open Problem: The Sample Complexity of Multi-Distribution Learning for VC Classes Pranjal Awasthi, Nika Haghtalab, Eric Zhao

NeurIPS 2023 Smoothed Analysis of Sequential Probability Assignment Alankrita Bhatt, Nika Haghtalab, Abhishek Shetty

ALT 2022 Algorithmic Learning Theory 2022: Preface Sanjoy Dasgupta, Nika Haghtalab

NeurIPS 2022 On-Demand Sampling: Learning Optimally from Multiple Distributions Nika Haghtalab, Michael I. Jordan, Eric Zhao

NeurIPS 2022 Oracle-Efficient Online Learning for Smoothed Adversaries Nika Haghtalab, Yanjun Han, Abhishek Shetty, Kunhe Yang

ICML 2021 One for One, or All for All: Equilibria and Optimality of Collaboration in Federated Learning Avrim Blum, Nika Haghtalab, Richard Lanas Phillips, Han Shao

IJCAI 2020 Maximizing Welfare with Incentive-Aware Evaluation Mechanisms Nika Haghtalab, Nicole Immorlica, Brendan Lucier, Jack Z. Wang

NeurIPS 2020 Smoothed Analysis of Online and Differentially Private Learning Nika Haghtalab, Tim Roughgarden, Abhishek Shetty

AISTATS 2019 Structured Robust Submodular Maximization: Offline and Online Algorithms Nima Anari, Nika Haghtalab, Seffi Naor, Sebastian Pokutta, Mohit Singh, Alfredo Torrico

IJCAI 2019 The Provable Virtue of Laziness in Motion Planning Nika Haghtalab, Simon Mackenzie, Ariel D. Procaccia, Oren Salzman, Siddhartha S. Srinivasa

NeurIPS 2019 Toward a Characterization of Loss Functions for Distribution Learning Nika Haghtalab, Cameron Musco, Bo Waggoner

AAAI 2018 Algorithms for Generalized Topic Modeling Avrim Blum, Nika Haghtalab

AAAI 2018 Weighted Voting via No-Regret Learning Nika Haghtalab, Ritesh Noothigattu, Ariel D. Procaccia

NeurIPS 2017 Collaborative PAC Learning Avrim Blum, Nika Haghtalab, Ariel D Procaccia, Mingda Qiao

COLT 2017 Efficient PAC Learning from the Crowd Pranjal Awasthi, Avrim Blum, Nika Haghtalab, Yishay Mansour

NeurIPS 2017 Online Learning with a Hint Ofer Dekel, Arthur Flajolet, Nika Haghtalab, Patrick Jaillet

COLT 2016 Learning and 1-Bit Compressed Sensing Under Asymmetric Noise Pranjal Awasthi, Maria-Florina Balcan, Nika Haghtalab, Hongyang Zhang

IJCAI 2016 Three Strategies to Success: Learning Adversary Models in Security Games Nika Haghtalab, Fei Fang, Thanh Hong Nguyen, Arunesh Sinha, Ariel D. Procaccia, Milind Tambe

COLT 2015 Efficient Learning of Linear Separators Under Bounded Noise Pranjal Awasthi, Maria-Florina Balcan, Nika Haghtalab, Ruth Urner

ICML 2014 Clustering in the Presence of Background Noise Shai Ben-David, Nika Haghtalab

AAAI 2014 Lazy Defenders Are Almost Optimal Against Diligent Attackers Avrim Blum, Nika Haghtalab, Ariel D. Procaccia

NeurIPS 2014 Learning Optimal Commitment to Overcome Insecurity Avrim Blum, Nika Haghtalab, Ariel D Procaccia