← Authors

Nika Haghtalab

31 papers · 2014–2025 · 7 conferences · across top CS/AI conferences

Achievements

🐣 Hot Topic Early Bird πŸŒ‰ Interdisciplinary Bridge 🧭 Keyword Pioneer 🌍 Conference Polyglot (7) πŸƒ Academic Marathon (11) 🐝 Cross-Pollinator (9) 🌈 Renaissance Researcher (7) πŸ—ΊοΈ Taxonomy Completionist (40) πŸ”¬ Deep Specialist (10) πŸ† Keyword Champion (3) πŸ—ƒοΈ Keyword Collector (111) πŸ’Ž Century Club (31) πŸ“ˆ Trend Setter ❓ The Questioner (3) πŸ”₯ Unstoppable (7) ⚑ Prolific Year (7)

Conferences

NIPS (14) COLT (5) ICML (4) AISTATS (3) IJCAI (3) AAAI (1) ALT (1)

Top co-authors

Eric Zhao (5) Michael I. Jordan (4) Kunhe Yang (4) Pranjal Awasthi (4) Avrim Blum (4) Abhishek Shetty (3) Meena Jagadeesan (3) Chara Podimata (3) Jacob Steinhardt (3) Jessica Dai (2)

Research topics

Differential Privacy (1)

Keywords

game theory (5) sample complexity (4) regret bound (4) vc dimension (3) pac learning (3) smoothed analysis (3) online learning (3) multi-agent system (3) game dynamics (2) adversarial learning (2) bounded noise (2) federated learning (2) multi-distribution learning (2) strategic classification (2) nash equilibrium (2) stackelberg game (2) active learning (2) information asymmetry (2) robust optimization (1) transductive learning (1)

Papers

Conference on Learning Theory 2025: Preface COLT 2025
Learning With Multi-Group Guarantees For Clusterable Subpopulations ICML 2025
Delegating Data Collection in Decentralized Machine Learning AISTATS 2024
Truthfulness of Calibration Measures NIPS 2024
Covert Malicious Finetuning: Challenges in Safeguarding LLM Adaptation ICML 2024
Is Knowledge Power? On the (Im)possibility of Learning from Strategic Interactions NIPS 2024
Can Probabilistic Feedback Drive User Impacts in Online Platforms? AISTATS 2024
Smoothed Analysis of Sequential Probability Assignment NIPS 2023
Open Problem: The Sample Complexity of Multi-Distribution Learning for VC Classes COLT 2023
Calibrated Stackelberg Games: Learning Optimal Commitments Against Calibrated Agents NIPS 2023
Improved Bayes Risk Can Yield Reduced Social Welfare Under Competition NIPS 2023
A Unifying Perspective on Multi-Calibration: Game Dynamics for Multi-Objective Learning NIPS 2023
Jailbroken: How Does LLM Safety Training Fail? NIPS 2023
Competition, Alignment, and Equilibria in Digital Marketplaces AAAI 2023
On-Demand Sampling: Learning Optimally from Multiple Distributions NIPS 2022
Algorithmic Learning Theory 2022: Preface ALT 2022
Oracle-Efficient Online Learning for Smoothed Adversaries NIPS 2022
One for One, or All for All: Equilibria and Optimality of Collaboration in Federated Learning ICML 2021
Smoothed Analysis of Online and Differentially Private Learning NIPS 2020
Maximizing Welfare with Incentive-Aware Evaluation Mechanisms IJCAI 2020
Structured Robust Submodular Maximization: Offline and Online Algorithms AISTATS 2019
Toward a Characterization of Loss Functions for Distribution Learning NIPS 2019
The Provable Virtue of Laziness in Motion Planning IJCAI 2019
Efficient PAC Learning from the Crowd COLT 2017
Online Learning with a Hint NIPS 2017
Collaborative PAC Learning NIPS 2017
Learning and 1-bit Compressed Sensing under Asymmetric Noise COLT 2016
Three Strategies to Success: Learning Adversary Models in Security Games IJCAI 2016
Efficient Learning of Linear Separators under Bounded Noise COLT 2015
Learning Optimal Commitment to Overcome Insecurity NIPS 2014
Clustering in the Presence of Background Noise ICML 2014