Ong, Luke

10 publications

ICLRW 2025 Reinforcement Learning with LTL and $\omega$-Regular Objectives via Optimality-Preserving Translation to Average Rewards Xuan-Bach Le, Dominik Wagner, Leon Witzman, Alexander Rabinovich, Luke Ong

ICLR 2025 Towards Interpreting Visual Information Processing in Vision-Language Models Clement Neo, Luke Ong, Philip Torr, Mor Geva, David Krueger, Fazl Barez

ICLRW 2025 Verifying Omega-Regular Properties of Neural Network-Controlled Systems via Proof Certificates Peixin Wang, Jianhao Bai, Dapeng Zhi, Min Zhang, Luke Ong

AISTATS 2024 Beyond Bayesian Model Averaging over Paths in Probabilistic Programs with Stochastic Support Tim Reichelt, Luke Ong, Tom Rainforth

AISTATS 2024 Diagonalisation SGD: Fast & Convergent SGD for Non-Differentiable Models via Reparameterisation and Smoothing Dominik Wagner, Basim Khajwal, Luke Ong

NeurIPS 2024 Reinforcement Learning with LTL and $\omega$-Regular Objectives via Optimality-Preserving Translation to Average Rewards Xuan-Bach Le, Dominik Wagner, Leon Witzman, Alexander Rabinovich, Luke Ong

UAI 2022 Expectation Programming: Adapting Probabilistic Programming Systems to Estimate Expectations Efficiently Tim Reichelt, Adam Goliński, Luke Ong, Tom Rainforth

ICML 2022 Nonparametric Involutive Markov Chain Monte Carlo Carol Mak, Fabian Zaiser, Luke Ong

NeurIPS 2022 Rethinking Variational Inference for Probabilistic Programs with Stochastic Support Tim Reichelt, Luke Ong, Thomas Rainforth

ICML 2021 Nonparametric Hamiltonian Monte Carlo Carol Mak, Fabian Zaiser, Luke Ong