← Authors

Sunil Gupta

53 papers · 2013–2026 · 11 conferences · across top CS/AI conferences

Achievements

πŸƒ Academic Marathon (12) 🐝 Cross-Pollinator (14) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird πŸŒ‰ Interdisciplinary Bridge 🌍 Conference Polyglot (11) 🌈 Renaissance Researcher (5) πŸ—ΊοΈ Taxonomy Completionist (20) 🌱 Topic Pioneer πŸ”¬ Deep Specialist (22) πŸ† Keyword Champion (2) 🀝 Dynamic Duo (43) πŸ† Grand Slam πŸ—ƒοΈ Keyword Collector (193) ⚑ Prolific Year (5) πŸš€ Conference Pioneer πŸ“ˆ Trend Setter πŸ’Ž Century Club (52) πŸ”₯ Unstoppable (10)

Conferences

AAAI (9) NIPS (9) AISTATS (6) ICML (6) IJCAI (6) ACML (5) WACV (5) ICLR (3) ECCV (2) ICCV (1) UAI (1)

Top co-authors

Svetha Venkatesh (43) Santu Rana (30) Alistair Shilton (9) Hung Le (8) Vu Nguyen (6) Hung Tran-The (6) Thanh Nguyen-Tang (5) Cheng Li (5) Dang Nguyen (5) Kien Do (5)

Research topics

Computer Vision (1)

Keywords

bayesian optimization (18) gaussian process (14) regret bound (13) acquisition function (8) hyperparameter tuning (6) transfer learning (5) domain generalization (3) active learning (3) kernel methods (3) expected improvement (3) black-box optimization (3) bayesian optimisation (3) reinforcement learning (2) multi-objective optimization (2) offline reinforcement learning (2) neural tangent kernel (2) bayesian nonparametric (2) high-dimensional optimization (2) high dimensional optimization (2) continual learning (2)

Papers

Probabilities Are All You Need: A Probability-Only Approach to Uncertainty Estimation in Large Language Models AAAI 2026
Fair Domain Generalization with Heterogeneous Sensitive Attributes Across Domains WACV 2025
EvoCL: Continual Learning over Evolving Domains WACV 2025
Navigating Social Dilemmas with LLM-based Agents via Consideration of Future Consequences IJCAI 2025
Black-box Optimization with Unknown Constraints via Overparameterized Deep Neural Networks UAI 2025
Beyond the Known: Decision Making with Counterfactual Reasoning Decision Transformer IJCAI 2025
Causal Discovery via Bayesian Optimization ICLR 2025
Stable Hadamard Memory: Revitalizing Memory-Augmented Agents for Reinforcement Learning ICLR 2025
High Dimensional Bayesian Optimization using Lasso Variable Selection AISTATS 2025
EMOTE: An Explainable Architecture for Modelling the Other through Empathy IJCAI 2024
Learn To Unlearn for Deep Neural Networks: Minimizing Unlearning Interference With Gradient Projection WACV 2024
Diversifying Training Pool Predictability for Zero-shot Coordination: A Theory of Mind Approach IJCAI 2024
Robust Transfer Learning for Active Level Set Estimation with Locally Adaptive Gaussian Process Prior ACML 2024
Root Cause Explanation of Outliers under Noisy Mechanisms AAAI 2024
Active Set Ordering NIPS 2024
Domain Generalization with Interpolation Robustness ACML 2023
On Instance-Dependent Bounds for Offline Reinforcement Learning with Linear Function Approximation AAAI 2023
Active Level Set Estimation for Continuous Search Space with Theoretical Guarantee ACML 2023
Multi-weather Image Restoration via Domain Translation ICCV 2023
Gradient Descent in Neural Networks as Sequential Learning in Reproducing Kernel Banach Space ICML 2023
Continual Learning With Dependency Preserving Hypernetworks WACV 2023
Guiding Visual Question Answering With Attention Priors WACV 2023
Video Restoration Framework and Its Meta-Adaptations to Data-Poor Conditions ECCV 2022
Offline Neural Contextual Bandits: Pessimism, Optimization and Generalization ICLR 2022
Learning to Constrain Policy Optimization with Virtual Trust Region NIPS 2022
Expected Improvement for Contextual Bandits NIPS 2022
Regret Bounds for Expected Improvement Algorithms in Gaussian Process Bandit Optimization AISTATS 2022
TRF: Learning Kernels with Tuned Random Features AAAI 2022
Black-Box Few-Shot Knowledge Distillation ECCV 2022
A New Representation of Successor Features for Transfer across Dissimilar Environments ICML 2021
High Dimensional Level Set Estimation with Bayesian Neural Network AAAI 2021
Distributional Reinforcement Learning via Moment Matching AAAI 2021
Bayesian Optimistic Optimisation with Exponentially Decaying Regret ICML 2021
Kernel Functional Optimisation NIPS 2021
Randomised Gaussian Process Upper Confidence Bound for Bayesian Optimisation IJCAI 2020
Trading Convergence Rate with Computational Budget in High Dimensional Bayesian Optimization AAAI 2020
DeepCoDA: personalized interpretability for compositional health data ICML 2020
Sub-linear Regret Bounds for Bayesian Optimisation in Unknown Search Spaces NIPS 2020
Accelerated Bayesian Optimisation through Weight-Prior Tuning AISTATS 2020
Bayesian Optimization for Categorical and Category-Specific Continuous Inputs AAAI 2020
Distributionally Robust Bayesian Quadrature Optimization AISTATS 2020
Bayesian Functional Optimisation with Shape Prior AAAI 2019
Bayesian Optimization with Unknown Search Space NIPS 2019
Multi-objective Bayesian optimisation with preferences over objectives NIPS 2019
Algorithmic Assurance: An Active Approach to Algorithmic Testing using Bayesian Optimisation NIPS 2018
Exploiting Strategy-Space Diversity for Batch Bayesian Optimization AISTATS 2018
High Dimensional Bayesian Optimization using Dropout IJCAI 2017
Regret for Expected Improvement over the Best-Observed Value and Stopping Condition ACML 2017
Regret Bounds for Transfer Learning in Bayesian Optimisation AISTATS 2017
High Dimensional Bayesian Optimization with Elastic Gaussian Process ICML 2017
Process-constrained batch Bayesian optimisation NIPS 2017
A Bayesian Nonparametric Approach for Multi-label Classification ACML 2016
Factorial Multi-Task Learning : A Bayesian Nonparametric Approach ICML 2013