Yuxuan Jiang
where else to find them
no profile links on file
what they talk about
all 5 in the filters
where they keep going back
- OOPSLA2 editions2021–2023
all 4 in the filters
on a stage with
all 16 in the filters
who talks about the same things
- Supun Abeysinghe3 talks
- Yuyan Bao8 talks
- Oliver Bračevac9 talks
- Songlin Jia6 talks
- Guannan Wei21 talks
- Tiark Rompf24 talks
| title | conference | date |
|---|---|---|
| Beyond Loss and Accuracy: Closing the Observability Gaps in AI Training with... | SREcon26 Americas | 2026-03-24 |
TrainCheck is an open-source Python module that instruments PyTorch training via monkey-patching, records API traces and parameter hashes, and checks inferred… | ||
| Training with Confidence: Catching Silent Errors in Deep Learning Training with... | OSDI '25 | 2025-07-07 |
TRAINCHECK automatically infers low‑level training invariants from reference traces and uses selective dynamic instrumentation (monkey‑patching and proxies) to… | ||
| One-Size-Fits-None: Understanding and Enhancing Slow-Fault Tolerance in Modern... | NSDI '25 | 2025-04-28 |
A systematic testing pipeline injects diverse slow faults into six modern distributed systems and sweeps ~100k fault combinations including 8000 configuration… | ||
| Graph IRs for Impure Higher-Order Languages: Making Aggressive Optimizations Af... | OOPSLA 2023 | 2023-10-23 |
A typed graph IR combines reachability types with an effect system to compute precise, flow-sensitive effect dependencies without CFA by doing type-based… | ||
| Reachability Types: Tracking Aliasing and Separation in Higher-Order Functional Programs | OOPSLA 2021 | 2021-10-17 |
New type system lambda assigns reachability qualifiers to expressions to track exact alias sets, enforces separation for higher-order functions with function… | ||
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