Rahul Kidambi
16 papers · 2015–2025 · 8 venues · across top CS/AI venues
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Achievements
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(5)
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Research topics
Machine Learning
(14)
Optimization & Theory
(11)
Mathematics & Optimization
(7)
Core Methods
(7)
Optimization
(7)
Reinforcement Learning
(4)
Methods
(4)
Learning Types
(3)
Artificial Intelligence
(2)
Application Areas
(2)
Deep Learning
(1)
Natural Language Processing
(1)
Robotics
(1)
Applications
(1)
Capabilities
(1)
Keywords
stochastic gradient descent
(5)
offline reinforcement learning
(2)
least squares regression
(2)
imitation learning
(2)
online learning
(2)
adversarial robustness
(1)
policy evaluation
(1)
stochastic optimization
(1)
behavior cloning
(1)
policy learning
(1)
ensemble learning
(1)
convex optimization
(1)
game theory
(1)
model-based learning
(1)
minimax optimality
(1)
multi-label classification
(1)
algorithmic fairness
(1)
empirical risk minimization
(1)
representation learning
(1)
submodular optimization
(1)
Papers
Fundamental Limits of Perfect Concept Erasure
AISTATS 2025
A Minimaximalist Approach to Reinforcement Learning from Human Feedback
ICML 2024
Conditional Language Policy: A General Framework For Steerable Multi-Objective Finetuning
EMNLP 2024
Enhancing Group Fairness in Online Settings Using Oblique Decision Forests
ICLR 2024
Making Paper Reviewing Robust to Bid Manipulation Attacks
ICML 2021
Mitigating Covariate Shift in Imitation Learning via Offline Data With Partial Coverage
NeurIPS 2021
MobILE: Model-Based Imitation Learning From Observation Alone
NeurIPS 2021
Top-k eXtreme Contextual Bandits with Arm Hierarchy
ICML 2021
Leverage Score Sampling for Faster Accelerated Regression and ERM
ALT 2020
MOReL: Model-Based Offline Reinforcement Learning
NeurIPS 2020
Open Problem: Do Good Algorithms Necessarily Query Bad Points?
COLT 2019
The Step Decay Schedule: A Near Optimal, Geometrically Decaying Learning Rate Procedure For Least Squares
NeurIPS 2019
Accelerating Stochastic Gradient Descent for Least Squares Regression
COLT 2018
On the insufficiency of existing momentum schemes for Stochastic Optimization
ICLR 2018
Parallelizing Stochastic Gradient Descent for Least Squares Regression: Mini-batching, Averaging, and Model Misspecification
JMLR 2018
Submodular Hamming Metrics
NeurIPS 2015