Paper establishes predictive performance and computational complexity of speed priors.
problem Estimating predictive performance and computational complexity of speed priors.
method Proposed a variant of speed prior and analyzed its computational and predictive properties.
result Our speed prior is computable in exponential time but not in polynomial time.
Grouped Gaussian Processes improve solar power and wind speed forecasting.
problem Forecasting distributed solar power and wind speed at multiple sites.
method Coupled Gaussian process priors over groups of node and weight functions.
result Our approach maintains or improves point-prediction accuracy and provides better quantification of predictive uncertainties.
New method improves Robbins-Monro algorithm convergence with prior information.
problem Improving convergence speed of Robbins-Monro algorithm.
method Integrates prior information into Robbins-Monro iteration without regression model.
result Prior-information Robbins-Monro sequence converges faster than standard.
RL for image captions improved with a language prior.
problem Learning biases and large sample space issues in RL image captioning.
method Added a language prior to constrain the action space.
result RL with the language prior module performs better in readability and speed.
Non-autoregressive method speeds up protein folding prediction 23 times.
problem Generating protein sequences with higher order interactions.
method Discrete diffusion conditioned on 3D structure using ProteinMPNN.
result 23 times speed up in inference without performance loss.
DeepRV accelerates spatiotemporal inference using neural priors.
problem Intractable scaling of Gaussian Processes for large datasets.
method Neural-network surrogate replacing GP prior sampling with O(N2) complexity. result DeepRV achieves highest fidelity to exact GPs while significantly speeding up inference.
Paper improves speech separation by using deep neural networks for more accurate density priors.
problem Improving the accuracy of source priors for independent vector analysis in speech separation.
method Estimating the derivative of speech density using deep neural networks to optimize performance indices.
result Neural network density priors outperform previous ones in convergence speed and SIR.
PD-PINNs accelerate PINN training by incorporating task-specific dictionaries.
problem Training PINNs is slow and lacks theoretical error bounds.
method Integrates task-dependent dictionaries into PINNs to enhance convergence.
result PD-PINNs achieve faster convergence and bounded prediction errors.
New algorithm speeds up Bayesian sparse regression for large datasets.
problem Slow posterior computation in large n & large p Bayesian sparse regression.
method Prior-preconditioned conjugate gradient method for accelerated Gibbs sampling.
result Order of magnitude speed-up in posterior inference for large-scale observational studies.
New method speeds up nuclear-norm constrained learning over multiple machines.
problem Synchronization slowdown and high communication costs in large-scale learning.
method Asynchronous Stochastic Frank-Wolfe (SFW-asyn) method.
result SFW-asyn achieves the same convergence rate as vanilla SFW but with speed-ups almost linear to the number of machines.
A quick gamma approximation speeds up Bayesian inference.
problem Inconvenient gamma shape parameter conjugate priors in Bayesian models.
method Introduced an easy algorithm to approximate gamma shape parameter full conditional by another gamma distribution.
result The approximation is accurate and fast, even for small sample sizes.
The study examines how prior and likelihood choices affect Bayesian matrix factorisation on small datasets.
problem Improving predictive performance of Bayesian matrix factorisation on small datasets.
method Review and comparison of 16 Bayesian matrix factorisation models across four groups: Gaussian-likelihood with real-valued priors, nonnegative priors, semi-nonnegative models, and Poisson-likelihood approaches.
result Poisson models give poor predictions, and nonnegative models are more constrained than real-valued ones.
Bayesian optimization uses priors to speed up robot learning.
problem Identifying the best prior when multiple exist for a new task.
method Introduces MLEI, a new acquisition function combining prior likelihood and expected improvement.
result MLEI effectively identifies and exploits priors in new situations.
This work learns latent representations to speed up exploration in complex environments.
problem Challenging exploration in high-dimensional state and action spaces with sparse rewards.
method Representation learning using prior experience to learn effective latent representations.
result Learned latent representations reduce the dimensionality of the search space for effective exploration.
Quantum algorithms for multi-armed bandits are explored with limited reward access.
problem Exploring quantum speed-ups in multi-armed bandit problems with limited reward information.
method Introduced new bandit models and showed query complexity equivalence with classical algorithms.
result No quadratic speed-up is possible for multi-armed bandits with limited reward access.
Algorithm learns shared demand structure across dynamic pricing experiments.
problem Learning shared demand parameters across multiple dynamic pricing experiments.
method Meta dynamic pricing algorithm that learns prior online while solving Thompson sampling experiments.
result Algorithm achieves sublinear meta regret in experiment-rich environments.
Bayesian framework inserts prior knowledge in RL for faster task solving.
problem Faster transfer learning across reinforcement learning tasks.
method Bayesian posterior distribution combining task-specific and prior knowledge.
result Significant speed ups achieved in maze solving.
Bayesian compression improves deep learning efficiency.
problem Efficiency and compression in deep learning networks.
method Adopting a Bayesian approach with hierarchical priors and posterior uncertainties.
result Achieved state-of-the-art compression rates with competitive performance.
FLAP adapts policies quickly to new tasks using shared linear representations.
problem Adapting policies to new tasks efficiently and effectively.
method FLAP uses a shared linear representation and a separate adapter network for quick adaptation.
result FLAP achieves up to 8X faster adaptation and significantly better performance on out-of-distribution tasks.
Efficiently solves inverse PDE problems with Gaussian processes.
problem Solving inverse problems in linear PDEs with noisy data.
method Gaussian process regression with algebraic priors.
result High accuracy and computational efficiency achieved.
A GAN-based projector speeds up image recovery in linear inverse problems.
problem Efficiently solving linear inverse problems with convergence guarantees.
method A GAN-based projector trained with projected gradient descent (PGD).
result Guaranteed O(δ) reconstruction error in O(log(1/δ)) steps. Adaptive sampling improves graph diffusion models by maintaining uniform information speed.
problem Standard diffusion models overlook non-homogeneous dynamics on complex manifolds.
method Information-geometric framework using Fisher-Rao metric and Drift Variation Score (DVS).
result DVS solver ensures uniform rate of distributional change, improving structural fidelity and efficiency.
The goal of imitation learning is for an apprentice to learn how to behave in a stochastic environment by observing a mentor demonstrating the correct behavior. Accurate prior knowledge about the correct behavior can reduce the need for demonstrations from the mentor. We present a novel approach to encoding prior knowl…
Transfer Neural AutoML speeds up deep learning architecture design.
problem High computational cost in Neural AutoML.
method Transfer learning to speed up architecture design.
result Reduces convergence time by over an order of magnitude.
Deep learning method uses asymptotic expansion to solve high-dimensional BSDEs faster.
problem Solving high-dimensional BSDEs efficiently.
method Asymptotic expansion as prior knowledge in deep learning for BSDEs.
result Significantly reduces loss function and accelerates convergence.
Bayesian compressive sensing speeds up object detection in video sequences.
problem Efficiently detecting objects in large video datasets.
method Bayesian compressive sensing methods for object detection.
result Bayesian methods achieve similar or better accuracy than greedy algorithms but faster.
TiDE uses MLP for fast, simple long-term time-series forecasting.
problem Long-term time-series forecasting challenges.
method Time-series Dense Encoder (TiDE) based on MLP.
result TiDE matches or outperforms Transformer models while being 5-10x faster.
Scalable PnP-ADMM for large-scale imaging problems.
problem Heavy computational and memory requirements of current PnP algorithms.
method Incremental variant of PnP-ADMM with theoretical convergence guarantees.
result Fast convergence and scalability compared to existing PnP algorithms.
New EPM models improve model shrinkage in edge partition models.
problem Overfitting and inappropriate model shrinkage in EPMs.
method Proposed two novel EPM models: CEPM and DEPM, incorporating constrained and Dirichlet priors respectively.
result IDEPM model shows state-of-the-art performance in generalization and prediction.
Small-GAN speeds up GAN training by using coresets.
problem Slowness and high memory usage in GAN training with large batches.
method Draw a large batch of samples from the prior, compress using Coreset-selection, and use cached Inception activations for random projection.
result Significantly reduces training time and memory usage for modern GAN variants.
Improved image reconstruction from sparse measurements using generative models.
problem Signal recovery from limited compressed measurements.
method Generative model with constrained latent variables for stable signal reconstruction.
result Improved reconstruction accuracy and preservation of realistic features.
This work speeds up unsupervised sentence learning using paragraph coherence.
problem Training fast unsupervised sentence encoders.
method Discourse-based objective function for neural network training.
result Models trained with this method are faster and perform well.
Physics-informed kernel learning integrates physical priors into machine learning models.
problem Tackles the integration of physical laws into machine learning models for improved accuracy and efficiency.
method Uses Fourier methods to approximate the kernel and minimizes a physics-informed risk function.
result Demonstrates PIKL outperforms physics-informed neural networks and traditional PDE solvers in various scenarios.
Bayesian l0-regularized least squares for high-dimensional predictors.
problem Optimizing a non-convex objective function over model space.
method Spike-and-slab priors with single Best Replacement (SBR) for scalability.
result SBR can find the spike-and-slab estimator, bridging Bayesian regularization and proximal updating.
LGES speeds up causal discovery while maintaining accuracy.
problem Causal discovery from observational data with computational and accuracy limitations.
method LGES modifies GES by avoiding certain edge insertions, using prior knowledge, and leveraging interventional data.
result LGES outperforms GES in speed, accuracy, and robustness to misspecified knowledge.
Bayesian neural networks speed up numerical integration.
problem Scalability of Bayesian quadrature methods.
method Bayesian Stein networks using neural networks and Laplace approximation.
result Orders of magnitude speed-up on benchmark functions and real-world problems.
We present an approximation scheme for support vector machine models that use an RBF kernel. A second-order Maclaurin series approximation is used for exponentials of inner products between support vectors and test instances. The approximation is applicable to all kernel methods featuring sums of kernel evaluations and…
We show that a simple modification of the 1-nearest neighbor classifier yields a strongly Bayes consistent learner. Prior to this work, the only strongly Bayes consistent proximity-based method was the k-nearest neighbor classifier, for k growing appropriately with sample size. We will argue that a margin-regularized 1…
New BNN architectures reduce computational cost for uncertainty quantification.
problem High computational cost in Bayesian neural networks.
method Partial trace-class Bayesian neural networks (PaTraC BNNs).
result Comparable uncertainty quantification with fewer parameters.
Quantum algorithm speeds up Gibbs partition function estimation.
problem Estimating partition functions in sublinear time.
method Sublinear-time quantum algorithm using quantum phase and amplitude estimation.
result First sublinear-time speed-up for partition function estimation.
COTA improves customer support speed and accuracy with machine learning.
problem Improving speed and reliability of customer support.
method Combining feature engineering and deep learning for automated ticket classification and answer selection.
result COTA v2 outperforms COTA v1 in reducing issue resolution time by 10%.
Sparse transformer architecture improves accuracy and speed in generative modeling and inverse problems.
problem Improving accuracy and speed in generative modeling and inverse problems.
method Proposes a sparse transformer architecture using regularized Wasserstein proximal operator with L1 prior. result Sparse transformer achieves higher accuracy and faster convergence than classical methods.
A new non-convex method improves robust PCA with features.
problem Robust Principal Component Analysis with prior feature information.
method A novel non-convex optimization approach for decomposition.
result Exact recovery guarantees with low computational complexity.
This work improves dictionary learning speed without sacrificing accuracy.
problem Prohibitive computational cost of standard dictionary learning methods.
method Approximate dictionary learning using unrolling and gradient descent.
result Unrolling outperforms standard methods in support estimation and early iterations.
We developed efficient methods to compute gradients for Neural SDEs, improving training speed and accuracy.
problem Training Neural SDEs requires accurate and efficient computation of gradients, which is challenging due to the complexity of SDEs.
method We introduced a reversible Heun method for solving backwards-in-time SDEs and a Brownian Interval for sampling and reconstructing Brownian motion.
result Our methods significantly improve training speed and accuracy for Neural SDEs, outperforming state-of-the-art techniques.
A new parallel clustering method improves speed and accuracy for single cell transcriptomic data.
problem Challenges in clustering single cell transcriptomic data, including poor quality, lack of prior knowledge, and slow computation.
method Parallel Split Merge Sampling on Dirichlet Process Mixture Model (Para-DPMM).
result The Para-DPMM model outperforms existing methods in clustering quality and computational speed.
Spike-and-slab priors are improved for high-dimensional Bayesian regression.
problem Prohibitive computational costs for existing samplers in high-dimensional settings.
method Proposes Scalable Spike-and-Slab (S3) for high-dimensional Bayesian regression. result Improves computational cost to max{n2pt,np} per iteration, demonstrating significant speed-ups and quality gains. TGD improves conditional sampling by concentrating computation on promising trajectories.
problem Efficiently training-free conditional sampling with diffusion priors.
method Tempered Guided Diffusion (TGD) using annealed sequential Monte Carlo.
result TGD yields a consistent particle approximation to the posterior as the number of particles grows.