Nika Haghtalab
31 papers · 2014–2025 · 7 conferences · across top CS/AI conferences
Achievements
Jump to papers ↓+11 more ↓ Show less ↑
π Academic Marathon (11) π Interdisciplinary Bridge π§ Keyword Pioneer π Conference Polyglot (7) π Cross-Pollinator (9)
π
Cross-Pollinator
(9)
π
Renaissance Researcher
(7)
πΊοΈ
Taxonomy Completionist
(40)
π¬
Deep Specialist
(10)
π
Keyword Champion
(3)
ποΈ
Keyword Collector
(111)
π
Century Club
(31)
π
Trend Setter
β
The Questioner
(3)
π₯
Unstoppable
(7)
β‘
Prolific Year
(7)
Conferences
NIPS (14)
COLT (5)
ICML (4)
AISTATS (3)
IJCAI (3)
AAAI (1)
ALT (1)
Top co-authors
Research topics
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