Proactive search in conversations (PSC) aims to reduce user effort in formulating explicit queries by proactively retrieving useful relevant information given conversational context. Previous work in PSC either directly uses this context as input to off-the-shelf ad-hoc retrievers or further fine-tunes them on PSC data. However, ad-hoc retrievers are pre-trained on short and concise queries, while the PSC input is longer and noisier. This input mismatch between ad-hoc search and PSC limits retrieval quality. While fine-tuning on PSC data helps, its benefits remain constrained by this input gap. In this work, we propose Conv2Query, a novel conversation-to-query framework that adapts ad-hoc retrievers to PSC by bridging the input gap between ad-hoc search and PSC. Conv2Query maps conversational context into ad-hoc queries, which can either be used as input for off-the-shelf ad-hoc retrievers or for further fine-tuning on PSC data. Extensive experiments on two PSC datasets show that Conv2Query significantly improves ad-hoc retrievers' performance, both when used directly and after fine-tuning on PSC.

Recent advances in weak supervision (WS) techniques allow to mitigate the enormous cost and effort of human data annotation for supervised machine learning by automating it using simple rule-based labelling functions (LFs). However, LFs need to be carefully designed, often requiring expert domain knowledge and extensive validation for existing WS methods to be effective. To tackle this, we propose the Weak Supervision Variational Auto-Encoder (WS-VAE), a novel framework that combines unsupervised representation learning and weak labelling to reduce the dependence of WS on expert and manual engineering of LFs. Our technique learns from inputs and weak labels jointly to capture the input signals distribution with a latent space. The unsupervised representation component of the WS-VAE regularises the inference of weak labels, while a specifically designed decoder allows the model to learn the relevance of LFs for each input. These unique features lead to considerably improved robustness to the quality of LFs, compared to existing methods. An extensive empirical evaluation on a standard WS benchmark shows that our WS-VAE is competitive to state-of-the-art methods and substantially more robust to LF engineering.

Machine learning methods for computational imaging require uncertainty estimation to be reliable in real settings. While Bayesian models offer a computationally tractable way of recovering uncertainty, they need large data volumes to be trained, which in imaging applications implicates prohibitively expensive collections with specific imaging instruments. This paper introduces a novel framework to train variational inference for inverse problems exploiting in combination few experimentally collected data, domain expertise and existing image data sets. In such a way, Bayesian machine learning models can solve imaging inverse problems with minimal data collection efforts. Extensive simulated experiments show the advantages of the proposed framework. The approach is then applied to two real experimental optics settings: holographic image reconstruction and imaging through highly scattering media. In both settings, state of the art reconstructions are achieved with little collection of training data.

We consider the problem of wireless communication across medium boundaries, specifically across the water-air interface. In particular, we are interested in enabling a submerged underwater sensor to directly communicate with an airborne node. Today's communication technologies cannot enable such a communication link. This is because no single type of wireless signal can operate well across different media and most wireless signals reflect back at media boundaries.