ML Anthology

Swaroop, Siddharth

23 publications

ICLRW 2025 AI Companions Are Not the Solution to Loneliness: Design Choices and Their Drawbacks Jonas B Raedler, Siddharth Swaroop, Weiwei Pan
ICLR 2025 Connecting Federated ADMM to Bayes Siddharth Swaroop, Mohammad Emtiyaz Khan, Finale Doshi-Velez
ICML 2025 Position: Rethinking LLM Bias Probing Using Lessons from the Social Sciences Kirsten Morehouse, Siddharth Swaroop, Weiwei Pan
ICLRW 2025 Rethinking LLM Bias Probing Using Lessons from the Social Sciences Kirsten Morehouse, Siddharth Swaroop, Weiwei Pan
ICMLW 2024 AMBER: An Entropy Maximizing Environment Design Algorithm for Inverse Reinforcement Learning Paul Nitschke, Lars Lien Ankile, Eura Nofshin, Siddharth Swaroop, Finale Doshi-Velez, Weiwei Pan
NeurIPSW 2024 An Autonomy-Based Classification: Liability in the Age of AI Agents Julia Smakman, Lisa Soder, Connor Dunlop, Weiwei Pan, Siddharth Swaroop
NeurIPSW 2024 Levels of Autonomy: Liability in the Age of AI Agents Lisa Soder, Julia Smakman, Connor Dunlop, Weiwei Pan, Siddharth Swaroop, Noam Kolt
NeurIPSW 2024 Position: AI Agents & Liability – Mapping Insights from ML and HCI Research to Policy Connor Dunlop, Weiwei Pan, Julia Smakman, Lisa Soder, Siddharth Swaroop
JMLR 2024 Rethinking Discount Regularization: New Interpretations, Unintended Consequences, and Solutions for Regularization in Reinforcement Learning Sarah Rathnam, Sonali Parbhoo, Siddharth Swaroop, Weiwei Pan, Susan A. Murphy, Finale Doshi-Velez
NeurIPSW 2024 Understanding Model Bias Requires Systematic Probing Across Tasks Soline Boussard, Susannah Cheng Su, Helen Zhao, Siddharth Swaroop, Weiwei Pan
TMLR 2023 Differentially Private Partitioned Variational Inference Mikko A. Heikkilä, Matthew Ashman, Siddharth Swaroop, Richard E Turner, Antti Honkela
ICMLW 2023 Discovering User Types: Characterization of User Traits by Task-Specific Behaviors in Reinforcement Learning Lars Lien Ankile, Brian Ham, Kevin Mao, Eura Shin, Siddharth Swaroop, Finale Doshi-Velez, Weiwei Pan
ICMLW 2023 Discovering User Types: Mapping User Traits by Task-Specific Behaviors in Reinforcement Learning Lars Lien Ankile, Brian Ham, Kevin Mao, Eura Shin, Siddharth Swaroop, Finale Doshi-Velez, Weiwei Pan
TMLR 2023 Improving Continual Learning by Accurate Gradient Reconstructions of the past Erik Daxberger, Siddharth Swaroop, Kazuki Osawa, Rio Yokota, Richard E Turner, José Miguel Hernández-Lobato, Mohammad Emtiyaz Khan
ICMLW 2023 Soft Prompting Might Be a Bug, Not a Feature Luke Bailey, Gustaf Ahdritz, Anat Kleiman, Siddharth Swaroop, Finale Doshi-Velez, Weiwei Pan
NeurIPS 2021 Collapsed Variational Bounds for Bayesian Neural Networks Marcin Tomczak, Siddharth Swaroop, Andrew Foong, Richard Turner
ICLR 2021 Generalized Variational Continual Learning Noel Loo, Siddharth Swaroop, Richard E Turner
NeurIPS 2021 Knowledge-Adaptation Priors Mohammad Emtiyaz Khan, Siddharth Swaroop
ICMLW 2020 Combining Variational Continual Learning with FiLM Layers Noel Loo, Siddharth Swaroop, Richard E Turner
NeurIPS 2020 Continual Deep Learning by Functional Regularisation of Memorable past Pingbo Pan, Siddharth Swaroop, Alexander Immer, Runa Eschenhagen, Richard Turner, Mohammad Emtiyaz Khan
ICMLW 2020 Continual Deep Learning by Functional Regularisation of Memorable past Pingbo Pan, Siddharth Swaroop, Alexander Immer, Runa Eschenhagen, Richard Turner, Mohammad Emtiyaz Khan
NeurIPS 2020 Efficient Low Rank Gaussian Variational Inference for Neural Networks Marcin Tomczak, Siddharth Swaroop, Richard Turner
NeurIPS 2019 Practical Deep Learning with Bayesian Principles Kazuki Osawa, Siddharth Swaroop, Mohammad Emtiyaz Khan, Anirudh Jain, Runa Eschenhagen, Richard E Turner, Rio Yokota