Rajat Sen
19 papers · 2016–2025 · 4 venues · across top CS/AI venues
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Achievements
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π£ Hot Topic Early Bird π Conference Polyglot Β· Bronze π Academic Marathon Β· Sprint π Interdisciplinary Bridge Β· Bronze πΊοΈ Taxonomy Completionist (32)
π
Keyword Champion
(2)
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Triple Crown
ποΈ
Keyword Collector
(104)
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Century Club Β· Bronze
π₯
Unstoppable Β· Silver
Venues
Top co-authors
Research topics
Machine Learning
(18)
Optimization & Theory
(13)
Learning Types Β· Machine Learning
(11)
Mathematics & Optimization
(10)
Core Methods
(9)
Optimization
(9)
Artificial Intelligence
(3)
Core AI
(3)
Data Science & Analytics
(2)
Deep Learning
(2)
Application Areas
(2)
Architectures
(2)
Methods
(2)
Natural Language Processing
(1)
Learning Types Β· Deep Learning
(1)
Keywords
regret bound
(6)
online learning
(3)
contextual bandit
(3)
multi-armed bandit
(3)
stochastic optimization
(2)
black-box optimization
(2)
importance sampling
(2)
regret minimization
(2)
multi-fidelity optimization
(2)
forecasting
(2)
linear regression
(2)
causal inference
(2)
hierarchical partitioning
(2)
expectation maximization
(1)
sample complexity
(1)
robust statistics
(1)
multi-label classification
(1)
parameter estimation
(1)
matrix factorization
(1)
adversarial robustness
(1)
Papers
In-Context Fine-Tuning for Time-Series Foundation Models
ICML 2025
A decoder-only foundation model for time-series forecasting
ICML 2024
Linear Regression using Heterogeneous Data Batches
NeurIPS 2024
Transformers can optimally learn regression mixture models
ICLR 2024
Efficient List-Decodable Regression using Batches
ICML 2023
On Learning Mixture of Linear Regressions in the Non-Realizable Setting
ICML 2022
On the benefits of maximum likelihood estimation for Regression and Forecasting
ICLR 2022
Trimmed Maximum Likelihood Estimation for Robust Generalized Linear Model
NeurIPS 2022
Top-k eXtreme Contextual Bandits with Arm Hierarchy
ICML 2021
Mix and Match: An Optimistic Tree-Search Approach for Learning Models from Mixture Distributions
NeurIPS 2020
Blocking Bandits
NeurIPS 2019
Noisy Blackbox Optimization using Multi-fidelity Queries: A Tree Search Approach
AISTATS 2019
Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting
NeurIPS 2019
Contextual Bandits with Stochastic Experts
AISTATS 2018
Multi-Fidelity Black-Box Optimization with Hierarchical Partitions
ICML 2018
Contextual Bandits with Latent Confounders: An NMF Approach
AISTATS 2017
Identifying Best Interventions through Online Importance Sampling
ICML 2017
Model-Powered Conditional Independence Test
NeurIPS 2017
Regret of Queueing Bandits
NeurIPS 2016