Siddharth Swaroop
Lecturer in Artificial Intelligence
Researches continual learning, Bayesian deep learning and human-AI collaboration.
Siddharth Swaroop's lab at UCL works on probabilistic & causal inference and human-AI collaboration & trust. 19 papers since 2023, 5 at top venues, mostly IUI and AAMAS.
At a glance
- papers, 2023–2026
- 5/ year
- at T1 venues
- 26%
- PhD students
- —
- MS students
- —
- alumni listed
- —
Papers by yearfrom the lab homepage, 2023–2026
- 0 papers2026 · T1 0 · collecting
- 3 papers2025 · T1 2
- 9 papers2024 · T1 2
- 7 papers2023 · T1 1
T1 venuesOtherVenues: IUI, AAMAS, CHI, ICLR, JMLR
Research focuspaper titles that use each topic's words
- Human-AI collaboration & trust3 papers
- Probabilistic & causal inference2 papers
- continual learning1 paper
- continual1 paper
- bayesian deep0 papers
Paper list
From the lab homepage.
20253 papers
- ICLR (T1)Connecting Federated ADMM to Bayes
- CHI (T1)Contrastive Explanations That Anticipate Human Misconceptions Can Improve Human Decision-Making Skills
- IUIPersonalising AI assistance based on overreliance rate in AI-assisted decision making
20249 papers
- IUIAccuracy-Time Tradeoffs in AI-Assisted Decision Making under Time Pressure
- Workshop on Socially Responsible Language Modelling Research @ NeurIPS , Workshop on Safe & Trustworthy Agents @ NeurIPSAI Agents & Liability – Mapping Insights from ML and HCI Research to Policy
- Models of Human Feedback for AI Alignment @ ICMLAMBER: An Entropy Maximizing Environment Design Algorithm for Inverse Reinforcement Learning
- Workshop on Socially Responsible Language Modelling Research @ NeurIPS , Workshop on Safe & Trustworthy Agents @ NeurIPSLevels of Autonomy: Liability in the age of AI Agents
- Book chapter in ‘‘Towards Human Brain Inspired Lifelong Learning’’Lifelong Learning for Deep Neural Networks with Bayesian Principles
- AAMAS (T1)Reinforcement Learning Interventions on Boundedly Rational Human Agents in Frictionful Tasks
- JMLR (T1)Rethinking Discount Regularization: New Interpretations, Unintended Consequences, and Solutions for Regularization in Reinforcement Learning
- Workshop on Socially Responsible Language Modelling Research @ NeurIPSUnderstanding Model Bias Requires Systematic Probing Across Tasks
- AMIA JournalWhere do doctors disagree? Characterizing Decision Points for Safe Reinforcement Learning in Choosing Vasopressor Treatment
20237 papers
- AI&HCI workshop at @ ICMLAdaptive interventions for both accuracy and time in AI-assisted human decision making
- TMLR (T1)Differentially private partitioned variational inference
- AI&HCI workshop at @ ICML (Honorable mention for best paper award), Interactive Learning with Implicit Human Feedback Workshop @ ICMLDiscovering User Types: Mapping User Traits by Task-Specific Behaviors in Reinforcement Learning
- TMLR & CoLLAsImproving Continual Learning by Accurate Gradient Reconstructions of the Past
- Duality Principles for Modern Machine Learning Workshop @ ICMLMemory Maps to Understand Models
- AAAI WorkshopModeling Mobile Health Users as Reinforcement Learning Agents
- Challenges of Deploying Generative AI Workshop @ ICMLSoft prompting might be a bug, not a feature
Where alumni went
Alumni are not listed on the lab homepage.
Apply to UCLFall 2027
Not announced yet
Computer Science MPhil/PhD
- Round
- Main
- Opens
- —
- GRE
- See program page
- English
- See program page
- Fee
- See program page
Not yet confirmed for this intake.
Similar labsnearest by research summary
- SGNTU
Timothy van Bremen
- Probabilistic & causal inference
- GBImperial
Deniz Gündüz
- Signal processing & information theory
- USMichigan
Lei Ying
- Reinforcement learning
- GBUCL
Ricardo Silva
- Learning theory & optimization
- CNSJTU
Lei Xu
- Learning theory & optimization
- USCMU
Eric Xing
- Generative & foundation models
Your notes
Save this lab to keep notes.
Data
Some numbers could not be verified:
- The member list could not be found on the lab site.
- The alumni list could not be found on the lab site.
- Collected
- Verified
- Papers
- The lab homepage
- Members
- Not collected
- Alumni
- Not collected
Tiers are venue classifications, not lab rankings.