Hongseok Yang
where else to find them
no profile links on file
what they talk about
all 5 in the filters
where they keep going back
- POPL3 editions2023–2026
all 5 in the filters
on a stage with
all 12 in the filters
who talks about the same things
- Wonyeol Lee6 talks
- Sam Staton18 talks
- Xavier Rival7 talks
- Younesse Kaddar4 talks
- Cameron E. Freer4 talks
- Mathieu Huot11 talks
| title | conference | date |
|---|---|---|
| Optimising Density Computations in Probabilistic Programs via Automatic Loop Vectorisation | POPL 2026 | 2026-01-11 |
Automatic vectorisation translates imperative PPL loops into a tensor-ready target with a Pyro implementation that runs loop iterations via speculative… | ||
| Semantics of Integrating and Differentiating Singularities | PLDI 2025 | 2025-06-16 |
A formal semantics for programs that evaluate and differentiate singular integrals, giving a denotational model and an operational Monte Carlo estimator that… | ||
| Peter's early work on parametricity, and why it still matters to me | O'Hearn Fest (POPL 2024) | 2024-01-14 |
Argues that Peter H.'s categorical account of relational parametricity (using reflexive graphs and naturality) gives a formal semantics that validates… | ||
| Probabilistic programming interfaces for random graphs: Markov categories, graph... | POPL 2024 | 2024-01-14 |
Proves an equivalence: a well-behaved implementation of an abstract graph type (vertices, new, edge) corresponds exactly to a graphon. Uses Markov categories… | ||
| Introduction to the tensor-programs framework, a PL approach that helps analyse ... | LAFI 2023 | 2023-01-15 |
Introduces Greg Yang’s tensor‑programs framework: a simple straight-line tensor language that encodes forward and backward neural computations parameterized by… | ||
| Smoothness Analysis for Probabilistic Programs with Application to Optimised Var... | POPL 2023 | 2023-01-15 |
A static analysis discovers which fragments of a probabilistic program are differentiable and which are not, then applies the pathwise gradient estimator to… | ||
| Black-Box Variational Inference for Probabilistic Programs | Uncertainty in Computation 2016 | 2016-10-04 |
Claims that black‑box variational inference applies to universal probabilistic programs by converting the program into a parameterized approximating program… | ||
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