Nika Haghtalab
32 papers · 2014–2026 · 7 venues · across top CS/AI venues
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πΊοΈ Taxonomy Completionist (41) π Academic Marathon Β· Half π Conference Polyglot Β· Silver π Interdisciplinary Bridge Β· Silver π£ Hot Topic Early Bird
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Deep Specialist
(12)
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Keyword Trendsetter Combo
(5)
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Keyword Pioneer
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Keyword Collector
(130)
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Prolific Year Β· Productive
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Trend Setter
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The Questioner
(3)
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Unstoppable Β· Bronze
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Renaissance Researcher
(7)
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Century Club Β· Bronze
Top co-authors
Research topics
Machine Learning
(27)
Optimization & Theory
(18)
Learning Types
(14)
Artificial Intelligence
(13)
Mathematics & Optimization
(11)
Core AI
(11)
Optimization
(11)
Core Methods
(5)
Application Areas
(4)
Data Science & Analytics
(3)
Applications
(3)
Learning Paradigms Β· Artificial Intelligence
(3)
Natural Language Processing
(2)
Learning Paradigms Β· Machine Learning
(2)
Resources & Methods
(2)
Keywords
game theory
(5)
regret bound
(4)
sample complexity
(4)
online learning
(4)
multi-agent system
(3)
pac learning
(3)
vc dimension
(3)
smoothed analysis
(3)
bounded noise
(2)
adversarial learning
(2)
information asymmetry
(2)
multi-distribution learning
(2)
game dynamics
(2)
federated learning
(2)
strategic classification
(2)
nash equilibrium
(2)
active learning
(2)
online algorithm
(2)
stackelberg game
(2)
transductive learning
(1)
Papers
Blackwell Approachability and Gradient Equilibrium are Equivalent
COLT 2026
Conference on Learning Theory 2025: Preface
COLT 2025
Learning With Multi-Group Guarantees For Clusterable Subpopulations
ICML 2025
Can Probabilistic Feedback Drive User Impacts in Online Platforms?
AISTATS 2024
Covert Malicious Finetuning: Challenges in Safeguarding LLM Adaptation
ICML 2024
Delegating Data Collection in Decentralized Machine Learning
AISTATS 2024
Is Knowledge Power? On the (Im)possibility of Learning from Strategic Interactions
NeurIPS 2024
Truthfulness of Calibration Measures
NeurIPS 2024
A Unifying Perspective on Multi-Calibration: Game Dynamics for Multi-Objective Learning
NeurIPS 2023
Calibrated Stackelberg Games: Learning Optimal Commitments Against Calibrated Agents
NeurIPS 2023
Competition, Alignment, and Equilibria in Digital Marketplaces
AAAI 2023
Improved Bayes Risk Can Yield Reduced Social Welfare Under Competition
NeurIPS 2023
Jailbroken: How Does LLM Safety Training Fail?
NeurIPS 2023
Open Problem: The Sample Complexity of Multi-Distribution Learning for VC Classes
COLT 2023
Smoothed Analysis of Sequential Probability Assignment
NeurIPS 2023
Algorithmic Learning Theory 2022: Preface
ALT 2022
On-Demand Sampling: Learning Optimally from Multiple Distributions
NeurIPS 2022
Oracle-Efficient Online Learning for Smoothed Adversaries
NeurIPS 2022
One for One, or All for All: Equilibria and Optimality of Collaboration in Federated Learning
ICML 2021
Maximizing Welfare with Incentive-Aware Evaluation Mechanisms
IJCAI 2020
Smoothed Analysis of Online and Differentially Private Learning
NeurIPS 2020
Structured Robust Submodular Maximization: Offline and Online Algorithms
AISTATS 2019
The Provable Virtue of Laziness in Motion Planning
IJCAI 2019
Toward a Characterization of Loss Functions for Distribution Learning
NeurIPS 2019
Collaborative PAC Learning
NeurIPS 2017
Efficient PAC Learning from the Crowd
COLT 2017
Online Learning with a Hint
NeurIPS 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
Clustering in the Presence of Background Noise
ICML 2014
Learning Optimal Commitment to Overcome Insecurity
NeurIPS 2014