Vision-Language Latent Diffusion Models (LDMs) (Rombach et al., 2022) provide powerful generative priors for inverse problems. However, existing LDM-based inverse solvers typically require a large number of neural function evaluations (NFEs) and backpropagation through large pretrained components, leading to substantial computational costs and, in some cases, degraded reconstruction quality. We propose a unified Euclidean-Wasserstein-2 gradient-flow framework that jointly performs posterior sampling and prompt optimization in the latent space through a single flow that aligns the prior and posterior with the observed data. Combined with few-step latent text-to-image models, this formulation enables low-NFE inference without backpropagation through autoencoders. Experiments across several canonical imaging inverse problems show that our method achieves state-of-the-art performance with significantly reduced computational cost.

By minimising off-target activation, Bayesian target optimisation could enable (e.g.)421 more precise synaptic connectivity mapping, improving our understanding of neural circuitry. This422 advancement has potential implications for understanding brain disorders like epilepsy, where423 abnormal synaptic connections are central to seizure generation and propagation. Deepening our424 understanding of these diseases can lead to enhanced targeted interventions and more effective425 therapeutic strategies, benefiting individuals with neurological disorders.426 First, we develop431 the approach for single optogenetic targets, as this is most closely related to existing GP-based432 receptive field inference techniques. We use a GP-Bernoulli approach to model the response ynt of436 neuron n on trial t to a single-target stimulus xt,437 ynt Bernoulli( (gn(xt))), (9) where the stimulus xt =( c1t,c2t,It) 2 R3 represents the two-dimensional coordinates and laser438 power of the t-th hologram.

Now consider a perturbation of the prior distribution over transition functions δ: T R 0 such that R Tp δ(Tp)P(Tp|h0)dTp = 1. Proof: Proposition 2 directly extends Proposition 1 in [8] to BAMDPs. Therefore, the perturbed distribution over histories is also a valid probability distribution. Provided that cbo is chosen appropriately (details in the appendix), as the number of perturbations expanded approaches, a perturbation within any > 0 of the optimal perturbation will be expanded by the Bayesian optimisation procedure with probability 1 δ. Proof: Consider an adversary decision node, v, associated with augmented state (s,ha,y) in the BACVaR-SG. We begin by proving that Q((s,ha,y),ξ) is continuous with respect to ξ. Define a function d: S R, such that ξ + d produces a valid adversary perturbation.

In this work, we address risk-averse Bayes-adaptive reinforcement learning. We pose the problem of optimising the conditional value at risk (CVaR) of the total return in Bayes-adaptive Markov decision processes (MDPs). We show that a policy optimising CVaR in this setting is risk-averse to both the epistemic uncertainty due to the prior distribution over MDPs, and the aleatoric uncertainty due to the inherent stochasticity of MDPs. We reformulate the problem as a two-player stochastic game and propose an approximate algorithm based on Monte Carlo tree search and Bayesian optimisation. Our experiments demonstrate that our approach significantly outperforms baseline approaches for this problem.

This paper proposes an adaptive neural-compilation framework to address the problem of learning efficient programs. Traditional code optimisation strategies used in compilers are based on applying pre-specified set of transformations that make the code faster to execute without changing its semantics. In contrast, our work involves adapting programs to make them more efficient while considering correctness only on a target input distribution. Our approach is inspired by the recent works on differentiable representations of programs. We show that it is possible to compile programs written in a low-level language to a differentiable representation. We also show how programs in this representation can be optimised to make them efficient on a target input distribution. Experimental results demonstrate that our approach enables learning specifically-tuned algorithms for given data distributions with a high success rate.

Gaussian processes (GPs) offer appealing properties but are costly to train at scale. Sparse variational GP (SVGP) approximations reduce cost yet still rely on Cholesky decompositions of kernel matrices, ill-suited to low-precision, massively parallel hardware. While one can construct valid variational bounds that rely only on matrix multiplications (matmuls) via an auxiliary matrix parameter, optimising them with off-the-shelf first-order methods is challenging. We make the inverse-free approach practical by proposing a better-conditioned bound and deriving a matmul-only natural-gradient update for the auxiliary parameter, markedly improving stability and convergence. We further provide simple heuristics, such as step-size schedules and stopping criteria, that make the overall optimisation routine fit seamlessly into existing workflows. Across regression and classification benchmarks, we demonstrate that our method 1) serves as a drop-in replacement in SVGP-based models (e.g., deep GPs), 2) recovers similar performance to traditional methods, and 3) can be faster than baselines when well tuned.