C.-H. Luke Ong
where else to find them
no profile links on file
what they talk about
where they keep going back
- LAFI4 editions2022–2025
on a stage with
all 15 in the filters
who talks about the same things
- Peixin Wang5 talks
- Hongfei Fu6 talks
- Guanyan Li3 talks
- Joseph Tassarotti20 talks
- Fabian Zaiser11 talks
- Mingshuai Chen4 talks
| title | conference | date |
|---|---|---|
| NP-NUTS: A Nonparametric No-U-Turn Sampler | LAFI 2025 | 2025-01-19 |
A new algorithm, NP‑NUTS, automatically adapts HMC trajectory length for models whose parameter dimension varies by combining NUTS’s doubling/tree construction… | ||
| Guaranteed Bounds on Posterior Distributions of Discrete Probabilistic Programs with Loops | POPL 2025 | 2025-01-19 |
Automated tool Diabolo implements two provable methods: residual mass semantics (fast unrolling upper/lower bounds) and geometric bound semantics (contraction… | ||
| Static Posterior Inference of Bayesian Probabilistic Programming via Polynomial Solving | PLDI 2024 | 2024-06-24 |
Derives guaranteed upper and lower bounds for normalized posteriors by reducing inference to polynomial solving over templates guided by a new fixed‑point… | ||
| A slice sampler for the Indian Buffet Process: expressivity in nonparametric pro... | LAFI 2024 | 2024-01-14 |
Introduces a slice-sampling construction that encodes the Indian Buffet Process inside a pure probabilistic program without truncation or hidden state, by… | ||
| Guaranteed Bounds for Discrete Probabilistic Programs with Loops via Generating ... | LAFI 2024 | 2024-01-14 |
Technique finds guaranteed upper and lower bounds on the posterior of discrete probabilistic programs with unbounded loops by representing distributions as… | ||
| Static Posterior Inference of Bayesian Probabilistic Programming via Polynomial ... | LAFI 2024 | 2024-01-14 |
Automated method derives guaranteed upper and lower bounds on a probabilistic program’s normalized posterior by reducing NPD bounding to bounds on the… | ||
| Exact Inference for Discrete Probabilistic Programs via Generating Functions | LAFI 2023 | 2023-01-15 |
Exact inference method represents program state distributions with probability generating functions and transforms those closed-form PGFs across sampling… | ||
| On the Reparameterisation Gradient for Non-Differentiable but Continuous Models | LAFI 2023 | 2023-01-15 |
Claim: the reparameterisation gradient is unbiased for continuous but possibly non-differentiable probabilistic programs. Proof uses a categorical denotational… | ||
| Pitfalls of Full Bayesian Inference in Universal Probabilistic Programming | LAFI 2023 | 2023-01-15 |
Argues that full Bayes on universal PPLs treats programs as a Bayesian Model Average over their straight-line programs (SLPs), which can make posterior SLP… | ||
| A Language and Smoothed Semantics for Convergent Stochastic Gradient Descent | LAFI 2022 | 2022-01-16 |
Provides a smoothed denotational semantics (in Frölicher spaces) plus a mild type system forbidding parameter-dependent guards that ensures any typable… | ||
| Nonparametric Involutive Markov Chain Monte Carlo | LAFI 2022 | 2022-01-16 |
Framework generalizes involutive MCMC to universal PPLs and implements a concrete fix for trans‑dimensional moves: it appends samples until proposal has… | ||
| Rigorous Approximation of Posterior Inference for Probabilistic Programs | LAFI 2022 | 2022-01-16 |
Claims rigorous, nonstochastic posterior bounds for a universal probabilistic language that allows recursion, continuous distributions and conditioning. Method… | ||
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