Scalable3-BO tackles scalability issues in Bayesian optimization for big data and high dimensions.
problem Bayesian optimization scalability issues in big data and high dimensions.
method Sparse Gaussian process, random embedding, asynchronous parallelization.
result Scalable3-BO framework optimizes high-dimensional problems with 1 million data points and 10,000 dimensions.
New framework scales graph AE and VAE by training on a subset of nodes.
problem Training scalability and speed issues in graph AE and VAE models.
method Utilizes graph degeneracy to train on a dense subset of nodes, with a propagation mechanism.
result Empirically competitive results on large graphs (millions of nodes and edges).
Develops a scalable framework for optimizing superposition-structured models.
problem Insufficient interpretability and generalization performance of simple structural models.
method Proximal Newton-type method with smoothed conic dual approach and LBFGS updating formula.
result Achieves super-linear convergence rate for optimizing superposition-structured models.
Unified framework connects SG-MCMC and SVGD for scalable Bayesian sampling.
problem Highly correlated samples in SG-MCMC limit scalability.
method Wasserstein gradient flows, particle-approximate techniques.
result Unified framework allows new scalable algorithms.
TensorHyper-VQC improves VQC scalability and robustness.
problem Scalability and noise sensitivity in VQC.
method Tensor-train-guided hypernetwork framework.
result TensorHyper-VQC achieves superior performance and robust noise tolerance.
The paper introduces a scalable unsupervised learning framework to improve deep neural networks.
problem Improving deep neural networks' performance and generalization in unsupervised settings.
method A scalable unsupervised regularization framework that constrains hypothesis space to non-trivial piecewise constant functions.
result The framework leads to a factually confident and smooth discriminative model, achieving state-of-the-art clustering results and generalization on both synthetic and real data.
A scalable framework preserves personalized higher-order network proximities.
problem Lack of expressive methods to preserve personalized higher-order network proximities.
method Incorporates random walk into a sound objective to preserve arbitrary higher-order proximities and introduces random walk with restart for personalized-weighted preservation.
result Consistently and substantially outperforms state-of-the-art methods on real-world networks.
Framework for applying GPs to real-world data with scalability guidelines.
problem Deployment of Gaussian Processes (GPs) is hindered by computational costs and lack of guidelines.
method Proposed a framework for identifying GP suitability and setting up robust models, formalizing decisions of experienced practitioners.
result More accurate results at test time for glacier elevation change case study.
Unified scalable GPCs for various likelihoods using additive noise.
problem Scalability issues and intractable inference in GPC for big data and non-Gaussian likelihoods.
method Additive noise to unify scalable GPCs for multiple likelihoods, using variational inference.
result Empirically superior results for binary/multi-class classification tasks with up to two million data points.
The paper tackles scalable simulation of discrete random variables.
problem Simulating discrete random variables with general and varying distributions in a scalable framework.
method Inspired by discrete choice models, the paper introduces parallelized randomness and a single associative operation for simulation.
result Characterization of algorithms for scalable simulation of discrete random variables.
New framework classifies generated images into finer categories.
problem Difficulty in detecting new types of generated images.
method Scalable deep metric learning for multi-class classification.
result Improved detection performance on new types of generated data.
New scalable algorithm improves linear regression accuracy and efficiency.
problem Improving linear regression accuracy and efficiency for large datasets.
method Developed new theory to model significance and multicollinearity as constraints, scaling with n in the 10,000s. result Significantly improves accuracy, reduces false detection rate, and speeds up computation.
Paper proposes scalable GP framework for cost-efficient wireless traffic prediction.
problem Wireless traffic prediction for C-RANs to improve spectrum and energy efficiency.
method Scalable Gaussian process framework with ADMM for parallel hyper-parameter optimization and cross-validation based optimal fusion strategy.
result Proposed scalable GP model outperforms state-of-the-art approaches in wireless traffic prediction.
We introduce a new structured kernel interpolation (SKI) framework, which generalises and unifies inducing point methods for scalable Gaussian processes (GPs). SKI methods produce kernel approximations for fast computations through kernel interpolation. The SKI framework clarifies how the quality of an inducing point a…
Unified framework for scalable black-box optimization.
problem Expensive black-box evaluations in scientific and engineering domains.
method Integrates active learning, multi-armed bandits, and distributed computing.
result Consistently outperforms state-of-the-art black-box optimizers.
Quantum Gaussian processes enable scalable quantum learning.
problem Lack of simple, interpretable, scalable learning frameworks for quantum data.
method Bayesian framework using Gaussian processes with quantum kernels.
result Provable and scalable quantum Gaussian processes for quantum learning.
FSBM improves matching efficiency with minimal supervision.
problem Scalability vs. minimal supervision in matching frameworks.
method FSBM uses a small portion of pre-aligned pairs as state feedback to guide non-coupled samples.
result FSBM accelerates training and enhances generalization.
A new framework for scalable uncertainty quantification in statistical models.
problem Computational bottleneck in uncertainty quantification for statistical machine learning.
method Predictive-matching Generative Parameter Sampler (GPS) framework.
result The GPS framework provides successful uncertainty quantification and additional flexibility.
Enhances HiP-MDP for scalable, robust transfer learning.
problem Scalability and robustness in transfer learning for complex tasks.
method Introduces HiP-MDP with latent embeddings, Bayesian Neural Network, and scalable inference.
result Improved scalability and robustness in transfer learning for high-dimensional tasks.
New framework uses conformal predictions for robust, scalable machine learning classification.
problem Developing robust and reliable machine learning models for classification.
method Introducing scalable classifiers linked to statistical order theory and probabilistic learning theory, defining a score function and conformal safety set.
result Demonstrated practical implications in cybersecurity for identifying DNS tunneling attacks.
A new framework evaluates large language models efficiently and accurately.
problem Evaluation of large language models is challenging due to stochasticity and heterogeneity of benchmarks.
method Interpretable and scalable framework based on Item Response Theory (IRT) and majorization-minimization principle.
result Our method achieves superior scalability and interpretability compared to existing approaches.
Proposes SHORE model for efficient MOR with sparsity and scalability.
problem Challenges of interpretability and scalability in MOR with high-dimensional outputs.
method Incorporates sparsity requirements and a two-stage optimization framework for efficient compression.
result Theoretical and empirical validation of the proposed framework's efficiency and accuracy.
Framework handles both exchangeable and non-exchangeable event sequences without tuning.
problem Handling both exchangeable and non-exchangeable event sequences efficiently.
method Parametric Hawkes-process-inspired conditional probability mass function with variational inference.
result Competitive computational and predictive performance against state-of-the-art methods.
Develops an ordinal-similarity framework for scalable and interpretable representation alignment.
problem Measuring representation similarity in large datasets.
method Triplet and Quadruplet Similarity Indices.
result Demonstrates inherent interpretability, robustness to outliers, and computational efficiency.
A scalable topic model for large document collections using MapReduce.
problem Scalability issues in topic modeling for large document collections.
method Correlated Topic Model with variational Expectation-Maximization in MapReduce framework.
result Comparable topic coherences with LDA in MapReduce framework.
Nyström approximation for scalable operator learning
problem Scalability of operator learning for large datasets
method Nyström subsampling with operator learning
result Minimax-optimal convergence rates for functional outputs
Develops scalable differentiable physics for complex object interactions.
problem Limited scalability of existing differentiable physics solvers.
method Adopting meshes for arbitrary geometry, localized collision handling, and accelerated implicit differentiation.
result Significantly reduces memory and computation requirements compared to particle-based methods.
A scalable Bayesian linear regression framework for spatial data.
problem Scalable methodologies for analyzing large spatial datasets.
method Conjugate Bayesian linear regression framework.
result Exact sampling from joint posterior distribution without iterative algorithms.
Framework boosts robustness of any learning algorithm against adversarial attacks.
problem Adversarial evasion attacks that modify data to evade detection.
method General retraining framework that improves robustness of arbitrary learning algorithms.
result Framework minimizes upper bound on optimal adversarial risk and boosts robustness to evasion attacks.
New scalable MARL framework for dynamic networked systems.
problem Scalability in multi-agent reinforcement learning with dynamic dependencies.
method Scalable Actor Critic framework for non-local and stochastic dependencies.
result Finite-time error bound showing convergence rate dependence on information spread speed.
The paper discusses scalable learning for wireless data-driven systems.
problem Expanding data volume and model complexity limit centralized learning solutions.
method Discusses scalable architecture and local learning strategies.
result Promising research directions in scalable data-driven wireless communications.
A new Gaussian process framework uses neural feature maps for scalable, accurate inference.
problem Efficient and accurate Gaussian process inference for diverse data types.
method Neural feature maps to construct expressive kernels, with theoretical guarantees and practical scalability.
result The approach outperforms existing methods in accuracy and efficiency across various data modalities.
A scalable DL benchmarking platform for evaluating and comparing models, frameworks, and hardware.
problem Lack of a uniform DL benchmarking platform for evaluating and comparing innovations.
method Identified 10 design features for a DL benchmarking platform, proposed MLModelScope, and implemented as an open-source project.
result Demonstrated how model, hardware, and framework selection affect accuracy and performance under different scenarios.
A scalable framework for gradient boosting using TensorFlow.
problem Training gradient boosted trees efficiently on large datasets.
method Distributed training architecture, automatic loss differentiation, layer-by-layer boosting, multi-class handling, regularization.
result Faster prediction and smaller ensembles compared to traditional methods.
A new framework SPOT efficiently solves large scale optimal transport problems.
problem Heavy computational burden in optimal transport limits its use.
method Implicit generative learning framework (SPOT) approximates optimal transport plan and solves it using stochastic gradient algorithms.
result SPOT efficiently solves optimal transport problems and can recover the density of the plan.
New batch learning framework improves scalability and accuracy of personalized ranking.
problem Inaccurate rank estimation in large-scale personalized ranking algorithms.
method Uses batch-based rank estimators and smooth rank-sensitive loss functions.
result Consistent accuracy improvements and time efficiency advantages over state-of-the-art methods.
Novel neural framework for scalable community detection and link prediction.
problem Efficiently learning graph topology and community structure in large graphs.
method Proposes a joint SBM loss function for community detection and link prediction, leveraging neural networks.
result Framework efficiently handles large graphs with a single forward pass, improving scalability and performance.
A-NeSI scales approximate inference for probabilistic neurosymbolic learning.
problem Combining neural networks with symbolic reasoning for scalable inference.
method A-NeSI: a new framework for PNL using neural networks for approximate inference.
result A-NeSI achieves scalable approximate inference without semantic changes.
SAFE automates feature engineering for industrial tasks efficiently and scalably.
problem Efficiency and scalability of automatic feature engineering methods for industrial tasks.
method SAFE (Scalable Automatic Feature Engineering) method, which provides excellent efficiency and scalability.
result SAFE method provides prominent efficiency and competitive effectiveness in industrial tasks.
Framework generates precise synthetic populations for scalable modeling.
problem Generating accurate synthetic populations without personal data.
method Constraint-programming framework encoding aggregated statistics and structural relations.
result Exact control of demographic profiles without requiring microdata.
Unified framework for scalable optimization of ranking-based objectives.
problem Scalability issues in optimizing ranking-based performance metrics.
method Unified framework using building block bounds for scalable optimization.
result Substantial improvement in performance over accuracy-objective baseline.
ESAC combines genetic methods with RL to improve scalability and efficiency.
problem Combining genetic scalability with RL's data efficiency and optimal control.
method Combines Evolution Strategies (ES) with Soft Actor-Critic (SAC) to enable skill transfer and reduce hyperparameter sensitivity.
result Demonstrates improved performance and sample efficiency in challenging tasks.
New method calibrates LLMs for safety-critical tasks with scalable Bayesian inference.
problem Overconfidence in LLMs after fine-tuning for specific tasks.
method Orthogonalized Low-Rank Adapters (PoLAR) with variational Bayesian inference.
result Scalable and well-calibrated uncertainty estimation for LLMs.
New framework uses background knowledge to speed up causal discovery.
problem Scalable causal discovery for large datasets.
method Utilizes background knowledge during causal discovery process.
result Background knowledge reduces computational requirements and improves structure quality.
A scalable framework optimizes multi-asset portfolios with constraints.
problem Optimizing multi-asset portfolios with inequality constraints.
method Integrates neural policies with Pontryagin's Maximum Principle, enforcing feasibility via log-barrier regularization.
result Recover KKT-optimal policies in high-dimensional problems without violating constraints.
GPflowOpt is a Python framework for Bayesian optimization using TensorFlow.
problem Efficiently optimizing functions with black-boxes and expensive evaluations.
method Bayesian optimization using Gaussian processes with TensorFlow for automatic differentiation, parallelization, and GPU computations.
result Scalable and easy-to-use framework with various acquisition functions and custom models.
Bayesian approach improves performance in Gaussian process models.
problem Scalable posterior estimation in Gaussian process models.
method Revisiting variational inference techniques with Bayesian treatment of inducing variables and hyper-parameters.
result State-of-the-art performance demonstrated across various regression and classification problems.
Bayesian deep learning methods improve uncertainty estimation in computer vision.
problem Estimating uncertainty in deep learning models for robust computer vision.
method Comprehensive evaluation framework for scalable epistemic uncertainty estimation methods.
result Ensembling provides more reliable uncertainty estimates than MC-dropout.