Dominik Wagner

19 papers A* 4A 3Journal 12
YearRankTypeTitle / Venue / Authors
2026 J jnl
PAC Finite-Time Safety Guarantees for Stochastic Systems with Unknown Disturbance Distributions.
CoRR
Taoran Wu, Dominik Wagner, C.-H. Luke Ong, Bai Xue
2025 J jnl
Comparative Analysis of Barrier-like Function Methods for Reach-Avoid Verification in Stochastic Discrete-Time Systems.
CoRR
Zhipeng Cao, Peixin Wang, Luke Ong, Dorde Zikelic, Dominik Wagner, Bai Xue
2025 A* conf
Formally Verified Cloud-Scale Authorization.
ICSE
Aleks Chakarov, Jaco Geldenhuys, Matthew Heck, Michael Hicks, Sam Huang, Georges-Axel Jaloyan, Anjali Joshi, K. Rustan M. Leino, Mikael Mayer, Sean McLaughlin, Akhilesh Mritunjai, Clément Pit-Claudel, Sorawee Porncharoenwase, Florian Rabe, Marianna Rapoport, Giles Reger, Cody Roux, Neha Rungta, Robin Salkeld, Matthias Schlaipfer, Daniel Schoepe, Johanna Schwartzentruber, Serdar Tasiran, Aaron Tomb, Emina Torlak, Jean-Baptiste Tristan, Lucas G. Wagner, Michael W. Whalen, Remy Willems, Tongtong Xiang, Tae Joon Byun, Joshua M. Cohen, Ruijie Fang, Junyoung Jang, Jakob Rath, Hira Taqdees Syeda, Dominik Wagner, Yongwei Yuan
2025 J jnl
PAC One-Step Safety Certification for Black-Box Discrete-Time Stochastic Systems.
CoRR
Taoran Wu, Dominik Wagner, Jingduo Pan, Luke Ong, Arvind Easwaran, Bai Xue
2025 J jnl
Refined Barrier Conditions for Finite-Time Safety and Reach-Avoid Guarantees in Stochastic Systems.
CoRR
Bai Xue, Luke Ong, Dominik Wagner, Peixin Wang
2025 J jnl
Reinforcement Learning with ω-Regular Objectives and Constraints.
CoRR
Dominik Wagner, Leon Witzman, Luke Ong
2025 J jnl
Safety-Biased Policy Optimisation: Towards Hard-Constrained Reinforcement Learning via Trust Regions.
CoRR
Ankit Kanwar, Dominik Wagner, Luke Ong
2024 A conf
Diagonalisation SGD: Fast & Convergent SGD for Non-Differentiable Models via Reparameterisation and Smoothing.
AISTATS
Dominik Wagner, Basim Khajwal, Luke Ong
2024 J jnl
Diagonalisation SGD: Fast & Convergent SGD for Non-Differentiable Models via Reparameterisation and Smoothing.
CoRR
Dominik Wagner, Basim Khajwal, C.-H. Luke Ong
2024 A* conf
Reinforcement Learning with LTL and ω-Regular Objectives via Optimality-Preserving Translation to Average Rewards.
NeurIPS
Xuan-Bach Le, Dominik Wagner, Leon Witzman, Alexander Rabinovich, Luke Ong
2024 J jnl
Reinforcement Learning with LTL and ω-Regular Objectives via Optimality-Preserving Translation to Average Rewards.
CoRR
Xuan-Bach Le, Dominik Wagner, Leon Witzman, Alexander Rabinovich, Luke Ong
2023 A conf
Fast and Correct Gradient-Based Optimisation for Probabilistic Programming via Smoothing.
ESOP
Basim Khajwal, C.-H. Luke Ong, Dominik Wagner
2023 J jnl
Fast and Correct Gradient-Based Optimisation for Probabilistic Programming via Smoothing.
CoRR
Basim Khajwal, C.-H. Luke Ong, Dominik Wagner
2021 A conf
Densities of Almost Surely Terminating Probabilistic Programs are Differentiable Almost Everywhere.
ESOP
Carol Mak, C.-H. Luke Ong, Hugo Paquet, Dominik Wagner
2021 A* conf
Initial Limit Datalog: a New Extensible Class of Decidable Constrained Horn Clauses.
LICS
Toby Cathcart Burn, Luke Ong, Steven J. Ramsay, Dominik Wagner
2021 J jnl
Initial Limit Datalog: a New Extensible Class of Decidable Constrained Horn Clauses.
CoRR
Toby Cathcart Burn, Luke Ong, Steven J. Ramsay, Dominik Wagner
2020 J jnl
Densities of almost-surely terminating probabilistic programs are differentiable almost everywhere.
CoRR
Carol Mak, C.-H. Luke Ong, Hugo Paquet, Dominik Wagner
2019 A* conf
HoCHC: A Refutationally Complete and Semantically Invariant System of Higher-order Logic Modulo Theories.
LICS
C.-H. Luke Ong, Dominik Wagner
2019 J jnl
HoCHC: a Refutationally-complete and Semantically-invariant System of Higher-order Logic Modulo Theories.
CoRR
C.-H. Luke Ong, Dominik Wagner