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--- world:std2025_abstract [2025/10/17 10:23]
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+++ world:std2025_abstract [2025/11/21 17:07] (current)
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 ===== List of abstracts for the workshop Sampling from the Target Distribution =====
-  * <color /yellow>[[https://std2025.sciencesconf.org/program?lang=fr|Back to the program of the workshop]]</color>
+  * <color /yellow>[[https://std2025.sciencesconf.org/program?lang=fr|Back to the workshop program]]</color>
+  * Please click below on a <color /cyan>speaker's name to view their abstract.</color>
 <hidden ==== Kamélia Daudel ====>
@@ Line 18: / Line 19: @@
 <note round  80%>
   * **Schedule**: 10H50-11H40
-  * **Title**:
+  * **Title**: Recursive learning of asymptotic variational objectives
 </note>
+  * **Abstract**: General state-space models (SSMs) are widely used in statistical machine learning and are among the most classical generative models for sequential time-series data. SSMs, comprising latent Markovian states, can be subjected to variational inference (VI), but standard VI methods like the importance-weighted autoencoder (IWAE) lack functionality for streaming data. To enable online VI in SSMs when the observations are received in real time, we propose maximising an IWAE-type variational lower bound on the asymptotic contrast function, rather than the standard IWAE ELBO, using stochastic approximation. Unlike the recursive maximum likelihood method, which directly maximises the asymptotic contrast, our approach, called online sequential IWAE (OSI-WAE), allows for online learning of both model parameters and a Markovian recognition model for inferring latent states. By approximating filter state posteriors and their derivatives using sequential Monte Carlo (SMC) methods, we create a particle-based framework for online VI in SSMs. This approach is more theoretically well-founded than recently proposed online variational SMC methods. We provide rigorous theoretical results on the learning objective and a numerical study demonstrating the method’s efficiency in learning model parameters and particle proposal kernels.
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 <note round  80%>
   * **Schedule**: 11H40-12H30
-  * **Title**:
+  * **Title**: Entropic Mirror Monte Carlo
 </note>
+  * **Abstract :** Importance sampling is a well-known Monte Carlo method used to estimate expectations under a target distribution by drawing weighted samples from a proposal distribution. However, for intricate target distributions, such as multi-modal distributions in high-dimensional spaces, the method becomes inefficient unless the proposal distribution is carefully designed.
+  * In this talk, we introduce an adaptive framework for constructing efficient proposal distributions related to the recent framework of mirror descent. Our algorithm enhances exploration of the target distribution by combining global sampling strategies with a delayed weighting mechanism. This delayed weighting is essential, as it enables immediate resampling in regions where the proposal distribution is poorly suited. We establish that the proposed scheme exhibits geometric convergence under mild assumptions.
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 <note round  80%>
   * **Schedule**: 14H50-15H40
-  * **Title**: Learning with Importance Weighted Variational Inference
+  * **Title**: Guiding Diffusion models at Inference
-  * **Joint work** with François Roueff
 </note>
+  * **Abstract**: Denoising diffusion models have driven significant progress in the field of Bayesian inverse problems. Recent approaches use pre-trained diffusion models as priors to solve a wide range of such problems, only leveraging inference-time compute and thereby eliminating the need to retrain task-specific models on the same dataset. To approximate the posterior of a Bayesian inverse problem, a diffusion model samples from a sequence of intermediate posterior distributions, each with an intractable likelihood function. This work proposes a novel mixture approximation of these intermediate distributions. Since direct gradient-based sampling of these mixtures is infeasible due to intractable terms, we propose a practical method based on Gibbs sampling. We validate our approach through extensive experiments on image inverse problems, utilizing both pixel- and latent-space diffusion priors, as well as on source separation with an audio diffusion model.
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 <note round  80%>
   * **Schedule**: 16H10-16H40
-  * **Title**: Learning with Importance Weighted Variational Inference
+  * **Title**: Limit behavior of the alpha-divergence and strong minimality of exponential families.
-  * **Joint work** with François Roueff
+  * **Joint work** with Randal Douc and François Roueff.
 </note>
+  * **Abstract**: Minimizing the alpha-divergence is a compelling way to approximate an unnormalized density with an exponential family distribution. To establish convergence properties for monotonic alpha-divergence minimization algorithms, it is helpful to understand how the objective behaves. In particular, we would like to know if its level sets are compact. This presentation investigates the behavior of the alpha-divergence as the parameter approaches the boundary of the parameter space, and as its norm goes to infinity. We connect this limit behavior to a key property of the approximating exponential family, which we call "strong minimality". This property is sufficient to guarantee the compactness of the level sets.
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 <note round  80%>
   * **Schedule**: 16H40-17H10
-  * **Title**: Learning with Importance Weighted Variational Inference
+  * **Title**:
-  * **Joint work** with François Roueff
+  * **Joint work**
 </note>
+* **Abstract**: Sequential Monte Carlo (SMC) methods are general iterative stochastic algorithms to generate samples from a sequence of probability distributions. They proceed by recursively moving samples through the bridge via three crucial steps: importance sampling, resampling, and rejuvenating. The last step usually consists in running independent Markov chains, and only keeping the final states for the next iterations.
+Waste-free SMC [Dau and Chopin, 2022] avoids discarding the intermediary samples.
+We establish a finite-sample complexity bound for waste-free SMC, our proof generalises the approach of Marion et al [2023]. This bound prescribes sufficient conditions on the size of the particle system for the sampler to return estimated expectations $\hat{\pi}_T(f)$ such that $|\hat{\pi}_T − \pi_T(f)| ≤ \varepsilon$ with probability at least $1 − \eta$, for any bounded test function $f$.
+We demonstrate that waste-free SMC enjoys lower complexity than SMC differing from a logarithmic factor $O(\log(T\eta^{-1} \varepsilon^{-2}))$.
+We show that our analysis allows practitioners to tune the particle budget according to the specific statistical objective in hand. In particular, when the goal is to control errors for expectation estimates under $\hat{\pi}_T$ of bounded functions, the sampler can be run with an intentionally reduced number of particles in earlier iterations while still preserving finite-sample guarantees on the estimates. In doing so, we highlight an important distinction between those two objectives: estimating normalisation constants is more challenging than estimating expectations of bounded functions.
 </hidden>

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