Santu Rana
35 papers · 2016–2026 · 11 conferences · across top CS/AI conferences
Achievements
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π§ Keyword Pioneer πΊοΈ Taxonomy Completionist (14) π Renaissance Researcher (5) π Interdisciplinary Bridge π Conference Polyglot (10)
π
Academic Marathon
(9)
πΊοΈ
Taxonomy Completionist
(14)
π§
Keyword Pioneer
π€
Dynamic Duo
(32)
π±
Topic Pioneer
π¬
Deep Specialist
(20)
π§¬
Topic Evolution
π
Keyword Champion
(6)
β‘
Prolific Year
(7)
ποΈ
Keyword Collector
(129)
π
Century Club
(34)
π
Trend Setter
π₯
Unstoppable
(10)
π
Conference Pioneer
Conferences
NIPS (9)
AAAI (5)
AISTATS (5)
ICML (5)
IJCAI (3)
ACML (2)
ECCV (2)
EACL (1)
ICCV (1)
NAACL (1)
WACV (1)
Top co-authors
Keywords
bayesian optimization
(18)
gaussian process
(12)
regret bound
(11)
acquisition function
(8)
hyperparameter tuning
(6)
bayesian optimisation
(4)
expected improvement
(3)
transfer learning
(3)
active learning
(3)
black-box optimization
(3)
kernel methods
(3)
black-box function
(2)
neural tangent kernel
(2)
hyperparameter optimization
(2)
multi-objective optimization
(2)
search space
(2)
high-dimensional optimization
(2)
high dimensional optimization
(2)
global optimization
(2)
reward function
(2)
Papers
The Unintended Trade-off of AI Alignment: Balancing Hallucination Mitigation and Safety in LLMs
EACL 2026
ALPACA AGAINST VICUNA: Using LLMs to Uncover Memorization of LLMs
NAACL 2025
Learn To Unlearn for Deep Neural Networks: Minimizing Unlearning Interference With Gradient Projection
WACV 2024
EMOTE: An Explainable Architecture for Modelling the Other through Empathy
IJCAI 2024
Gradient Descent in Neural Networks as Sequential Learning in Reproducing Kernel Banach Space
ICML 2023
Multi-weather Image Restoration via Domain Translation
ICCV 2023
Regret Bounds for Expected Improvement Algorithms in Gaussian Process Bandit Optimization
AISTATS 2022
Momentum Adversarial Distillation: Handling Large Distribution Shifts in Data-Free Knowledge Distillation
NIPS 2022
Video Restoration Framework and Its Meta-Adaptations to Data-Poor Conditions
ECCV 2022
Towards Effective and Robust Neural Trojan Defenses via Input Filtering
ECCV 2022
TRF: Learning Kernels with Tuned Random Features
AAAI 2022
Human-AI Collaborative Bayesian Optimisation
NIPS 2022
Expected Improvement for Contextual Bandits
NIPS 2022
A New Representation of Successor Features for Transfer across Dissimilar Environments
ICML 2021
Kernel Functional Optimisation
NIPS 2021
High Dimensional Level Set Estimation with Bayesian Neural Network
AAAI 2021
Bayesian Optimistic Optimisation with Exponentially Decaying Regret
ICML 2021
Trading Convergence Rate with Computational Budget in High Dimensional Bayesian Optimization
AAAI 2020
Accelerated Bayesian Optimisation through Weight-Prior Tuning
AISTATS 2020
Distributionally Robust Bayesian Quadrature Optimization
AISTATS 2020
Randomised Gaussian Process Upper Confidence Bound for Bayesian Optimisation
IJCAI 2020
DeepCoDA: personalized interpretability for compositional health data
ICML 2020
Bayesian Optimization for Categorical and Category-Specific Continuous Inputs
AAAI 2020
Sub-linear Regret Bounds for Bayesian Optimisation in Unknown Search Spaces
NIPS 2020
Bayesian Functional Optimisation with Shape Prior
AAAI 2019
Multi-objective Bayesian optimisation with preferences over objectives
NIPS 2019
Bayesian Optimization with Unknown Search Space
NIPS 2019
Exploiting Strategy-Space Diversity for Batch Bayesian Optimization
AISTATS 2018
Algorithmic Assurance: An Active Approach to Algorithmic Testing using Bayesian Optimisation
NIPS 2018
High Dimensional Bayesian Optimization using Dropout
IJCAI 2017
High Dimensional Bayesian Optimization with Elastic Gaussian Process
ICML 2017
Process-constrained batch Bayesian optimisation
NIPS 2017
Regret Bounds for Transfer Learning in Bayesian Optimisation
AISTATS 2017
Regret for Expected Improvement over the Best-Observed Value and Stopping Condition
ACML 2017
A Bayesian Nonparametric Approach for Multi-label Classification
ACML 2016