Showing papers from Inria Show all papers
Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026
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Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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Paris Poster Session 3, Thu, Dec 10, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026
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Paris Poster Session 2, Wed, Dec 9, 5:00 PM–7:00 PM, Paris Poster Hall · Published 2026
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Paris Poster Session 5, Fri, Dec 11, 11:30 AM–1:30 PM, Paris Poster Hall · Published 2026
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Paris Poster Session 3, Thu, Dec 10, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026
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Jeffrey guidance extends diffusion control by updating marginals via Jeffrey's rule, reducing FID and enforcing fairness.
Paris Poster Session 4, Thu, Dec 10, 5:30 PM–7:30 PM, Paris Poster Hall · Published 2026
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Annealed Langevin dynamics replaces biased reverse-SDE sampling for compositional SBI scores with controllable bridging densities, yielding explicit hyperparameter rules; Linhart et al.'s formulation allows larger steps and fewer iterations than Geffner et al.'s in Gaussian settings and generalizes
Paris Poster Session 1, Wed, Dec 9, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026
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Reinforcement learning for code optimization fails due to noisy, sparse execution-time rewards, so a calibrated three-stage pipeline improves strict pass rates by up to 125% while preserving correctness.
Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026 · ▲ 13 on Hugging Face
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Optimal transport characterizes relaxed fair regression via smooth population-wide or exact subset parity penalties across aware and unaware settings, and proposed algorithms match or exceed state-of-the-art benchmarks.
Paris Poster Session 6, Fri, Dec 11, 2:30 PM–4:30 PM, Paris Poster Hall · Published 2026
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FedForest proposes a federated random forest using aggregated statistics to approximate centralized splits under heterogeneous data, enabling personalized client-indicator splits with near-centralized accuracy and low communication cost.
Paris Poster Session 5, Fri, Dec 11, 11:30 AM–1:30 PM, Paris Poster Hall · Published 2026
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Lumberjack improves differentially private random forests via heavy hitter pruning of deep trees, achieving state-of-the-art privacy-utility trade-offs.
Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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Under bounded parameters, releasing unlimited synthetic data preserves differential privacy amplification beyond prior asymptotic limits.
Paris Poster Session 2, Wed, Dec 9, 5:00 PM–7:00 PM, Paris Poster Hall · Published 2026
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Causal inference framing of membership inference attacks defines memorization as training inclusion effects, reveals interference and distribution-shift biases, and yields reliable estimators without retraining.
Paris Poster Session 1, Wed, Dec 9, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026
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A Cramér-von Mises fairness regularizer with O(B log B) complexity penalizes prediction-sensitive attribute dependence during training, achieving competitive fairness-utility trade-offs with lower overhead.
Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026
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