Harikrishna Narasimhan
40 papers · 2013–2025 · 9 conferences · across top CS/AI conferences
Achievements
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π§ Keyword Pioneer π£ Hot Topic Early Bird π Interdisciplinary Bridge πΊοΈ Taxonomy Completionist (10) π Conference Polyglot (9)
πΊοΈ
Taxonomy Completionist
(10)
π§
Keyword Pioneer
π£
Hot Topic Early Bird
π
Keyword Trendsetter Combo
(3)
π
Grand Slam
π¬
Deep Specialist
(19)
π
Keyword Champion
(3)
π₯
Unstoppable
(8)
π
Trend Setter
π
Conference Pioneer
ποΈ
Keyword Collector
(154)
β‘
Prolific Year
(5)
β
The Questioner
π
Century Club
(40)
Conferences
NIPS (13)
ICML (12)
ICLR (7)
IJCAI (2)
UAI (2)
AAAI (1)
AISTATS (1)
EMNLP (1)
JMLR (1)
Top co-authors
Keywords
confusion matrix
(5)
fairness constraint
(4)
constrained optimization
(4)
robust optimization
(3)
multiclass classification
(3)
cost-sensitive learning
(3)
class imbalance
(3)
statistical consistency
(3)
group fairness
(2)
class probability estimation
(2)
non-decomposable measures
(2)
roc curve
(2)
algorithmic fairness
(2)
true positive rate
(2)
deep learning
(2)
binary classification
(2)
stochastic gradient descent
(2)
black-box optimization
(2)
mechanism design
(2)
plug-in classifier
(2)
Papers
Bipartite Ranking From Multiple Labels: On Loss Versus Label Aggregation
ICML 2025
Faster Cascades via Speculative Decoding
ICLR 2025
Better autoregressive regression with LLMs via regression-aware fine-tuning
ICLR 2025
Plugin estimators for selective classification with out-of-distribution detection
ICLR 2024
Regression Aware Inference with LLMs
EMNLP 2024
Learning to Reject Meets Long-tail Learning
ICLR 2024
Consistent Multiclass Algorithms for Complex Metrics and Constraints
JMLR 2024
Language Model Cascades: Token-Level Uncertainty And Beyond
ICLR 2024
Robust distillation for worst-class performance: on the interplay between teacher and student objectives
UAI 2023
When Does Confidence-Based Cascade Deferral Suffice?
NIPS 2023
Distributionally Robust Post-hoc Classifiers under Prior Shifts
ICLR 2023
Churn Reduction via Distillation
ICLR 2022
Quadratic metric elicitation for fairness and beyond
UAI 2022
Post-hoc estimators for learning to defer to an expert
NIPS 2022
Training Over-parameterized Models with Non-decomposable Objectives
NIPS 2021
Optimizing Black-box Metrics with Iterative Example Weighting
ICML 2021
Implicit rate-constrained optimization of non-decomposable objectives
ICML 2021
Robust Optimization for Fairness with Noisy Protected Groups
NIPS 2020
Fair Performance Metric Elicitation
NIPS 2020
Consistent Plug-in Classifiers for Complex Objectives and Constraints
NIPS 2020
Pairwise Fairness for Ranking and Regression
AAAI 2020
Optimizing Black-box Metrics with Adaptive Surrogates
ICML 2020
Approximate Heavily-Constrained Learning with Lagrange Multiplier Models
NIPS 2020
Metric-Optimized Example Weights
ICML 2019
Optimizing Generalized Rate Metrics with Three Players
NIPS 2019
On Making Stochastic Classifiers Deterministic
NIPS 2019
Optimal Auctions through Deep Learning
ICML 2019
Deep Learning for Multi-Facility Location Mechanism Design
IJCAI 2018
Learning with Complex Loss Functions and Constraints
AISTATS 2018
Automated Mechanism Design without Money via Machine Learning
IJCAI 2016
Consistent Multiclass Algorithms for Complex Performance Measures
ICML 2015
Learnability of Influence in Networks
NIPS 2015
Surrogate Functions for Maximizing Precision at the Top
ICML 2015
Optimizing Non-decomposable Performance Measures: A Tale of Two Classes
ICML 2015
Online and Stochastic Gradient Methods for Non-decomposable Loss Functions
NIPS 2014
GEV-Canonical Regression for Accurate Binary Class Probability Estimation when One Class is Rare
ICML 2014
On the Statistical Consistency of Plug-in Classifiers for Non-decomposable Performance Measures
NIPS 2014
On the Relationship Between Binary Classification, Bipartite Ranking, and Binary Class Probability Estimation
NIPS 2013
On the Statistical Consistency of Algorithms for Binary Classification under Class Imbalance
ICML 2013
A Structural SVM Based Approach for Optimizing Partial AUC
ICML 2013