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.
Paper introduces a fast, robust, scalable method for detecting changes in data streams.
problem Detecting changes in data streams efficiently and reliably.
method Bayesian online changepoint detection with provable robustness and scalability.
result The proposed method is more than 10 times faster than previous approaches and provides provable robustness.
A new model for Gaussian process experts tackles scalability and uncertainty issues.
problem Scalability and excessive number of experts degrade predictive performance and increase uncertainty.
method Nested partitioning scheme infers the number of components, a generalised GP framework accommodates multiple response types, and a factorised exponential family structure handles multiple input types.
result Effectiveness demonstrated on synthetic data and an Alzheimer's challenge dataset.
This paper reviews recent advancements in amortized Variational Inference.
problem Scalability and efficiency issues in traditional Variational Inference.
method Systematic review of various Variational Inference techniques, focusing on amortized approaches.
result Amortized Variational Inference improves scalability and efficiency for generative modeling tasks.
The computational and storage complexity of kernel machines presents the primary barrier to their scaling to large, modern, datasets. A common way to tackle the scalability issue is to use the conjugate gradient algorithm, which relieves the constraints on both storage (the kernel matrix need not be stored) and computa…
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.
SIGL learns scalable graphons from graphs.
problem Estimating graphons from graphs of varying sizes.
method Combines INRs and GNNs for scalable graphon estimation.
result SIGL learns consistent graphons at arbitrary resolutions.
New method reduces SBL complexity from cubic to linear, improving scalability.
problem Sparse Bayesian Learning's high computational complexity for large feature spaces.
method DQN-SBL, a diagonal Quasi-Newton method for SBL.
result DQN-SBL achieves competitive generalization with sparse models, scaling well to large-scale problems.
New scalable Lipschitz bounds improve neural network robustness analysis.
problem Computing tight Lipschitz bounds for deep neural networks is challenging and computationally expensive.
method Derived new closed-form Lipschitz bounds using more general feasible points of LipSDP, avoiding SDP solvers.
result Improved scalability and precision of Lipschitz estimation for large neural networks.
FairGP uses graph partitioning to make Graph Transformers fair and scalable.
problem Fairness issues in Graph Transformers, especially against sensitive features.
method Graph partitioning to minimize the influence of higher-order nodes and optimize attention mechanisms.
result FairGP improves fairness in Graph Transformers while reducing computational complexity.
The paper analyzes and mitigates biases in scalable Gaussian Process methods.
problem Modeling biases in scalable Gaussian Process methods.
method Randomized truncation estimators to eliminate bias in exchange for increased variance.
result Randomized truncation estimators meaningfully outperform biased counterparts with minimal additional computation.
The paper tackles scalability issues in Graph Representation Learning.
problem Prohibitive time and memory complexities in Graph Representation Learning.
method Leveraging the K-Core Decomposition property of Graphs to reduce time and memory consumption.
result Proposed techniques significantly reduce computational resources without compromising embedding quality.
FastGAE scales graph AE and VAE to large graphs with millions of nodes.
problem Scalability issues in graph AE and VAE.
method Stochastic subgraph decoding scheme to speed up training.
result Outperforms existing approaches on various real-world graphs.
A scalable MARL algorithm using local rewards for cooperative multi-agent learning.
problem Scalability issues in cooperative multi-agent reinforcement learning due to large state and action spaces.
method LOMAQ algorithm incorporating local rewards in centralized training and decentralized execution.
result LOMAQ scales well compared to other methods, improving performance and convergence speed.
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.
Proposes a new learning framework for scalable image classification.
problem Scalability issues in CNN-based image classifiers for large number of classes.
method Combines hierarchical classification and auxiliary learning to address scalability issues.
result Reduces classification errors by up to 3.56% on CIFAR-10 dataset.
Study improves scalability of cell-free massive MIMO networks by optimizing UE-AP association.
problem Optimizing UE-AP association in cell-free massive MIMO networks.
method Deep learning algorithm using Bidirectional Long Short-Term Memory cells and hybrid probabilistic weight updating.
result Enhanced scalability without retraining, robust against pilot contamination.
While significant progress has been made separately on analytics systems for scalable stochastic gradient descent (SGD) and private SGD, none of the major scalable analytics frameworks have incorporated differentially private SGD. There are two inter-related issues for this disconnect between research and practice: (1)…
A scalable version of MADD improves big-data classification speed.
problem High computational complexity of MADD in big data.
method Selecting a representative set and using Random Fourier Features.
result Achieves similar performance to MADD but at a fraction of the computing time.
URSABench benchmarks Bayesian methods for deep learning models.
problem Scalability issues in Bayesian inference for deep learning.
method Open-source benchmark suite for assessing approximate Bayesian inference methods.
result Initial results show promise for addressing uncertainty and robustness in deep learning.
Intelligent Transportation Systems (ITSs) are envisioned to play a critical role in improving traffic flow and reducing congestion, which is a pervasive issue impacting urban areas around the globe. Rapidly advancing vehicular communication and edge cloud computation technologies provide key enablers for smart traffic …
STACI uses neural nets to estimate spatio-temporal fields with valid uncertainty quantification.
problem Scalable spatio-temporal deep learning models fail to capture underlying correlation structure.
method Variational Bayesian neural network approximation of non-stationary spatio-temporal Gaussian Process (GP) with conformal inference.
result STACI provides accurate prediction intervals for spatio-temporal processes, outperforming competing methods.
Optimal Transport (OT) naturally arises in many machine learning applications, yet the heavy computational burden limits its wide-spread uses. To address the scalability issue, we propose an implicit generative learning-based framework called SPOT (Scalable Push-forward of Optimal Transport). Specifically, we approxima…
Rank-1 BNNs improve efficiency and scalability of Bayesian neural nets.
problem Underfitting and lack of scalability in Bayesian neural networks.
method Propose a rank-1 parameterization of BNNs and use mixture approximate posteriors.
result Rank-1 BNNs achieve state-of-the-art performance across various datasets.
A scalable method for training prediction models in predict-then-optimize
problem Training prediction models in the predict-then-optimize paradigm
method Decision-focused learning pipeline
result Decision quality competitive with state-of-the-art methods while reducing training time
New tool improves scalability of data-driven invariant inference.
problem Scaling data-driven invariant inference to programs with many variables.
method Developed oasis tool to improve scalability.
result Outperforms state-of-the-art systems on benchmarks.
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.
Supervised cross-modal hashing has gained increasing research interest on large-scale retrieval task owning to its satisfactory performance and efficiency. However, it still has some challenging issues to be further studied: 1) most of them fail to well preserve the semantic correlations in hash codes because of the la…
The vast quantity of information brought by big data as well as the evolving computer hardware encourages success stories in the machine learning community. In the meanwhile, it poses challenges for the Gaussian process (GP) regression, a well-known non-parametric and interpretable Bayesian model, which suffers from cu…
While Bayesian neural networks (BNNs) have drawn increasing attention, their posterior inference remains challenging, due to the high-dimensional and over-parameterized nature. To address this issue, several highly flexible and scalable variational inference procedures based on the idea of particle optimization have be…
NodeSig efficiently computes binary node embeddings for scalable graph analysis.
problem Scalability issues in graph representation learning models.
method NodeSig uses random walk diffusion probabilities and stable random projections to compute binary node embeddings efficiently.
result NodeSig achieves a good balance between accuracy and efficiency on node classification and link prediction tasks.
L2G2G improves graph autoencoder accuracy without sacrificing scalability.
problem Scalability issues in graph autoencoders for large networks.
method Dynamic synchronization of latent node representations in each epoch.
result L2G2G achieves higher accuracy than standard Local2Global approach on large and dense networks.
A new method for measuring document similarity using hierarchical optimal transport.
problem Inability to measure semantic similarities and scalability issues in past document similarity measures.
method Model documents as distributions over topics, topics as distributions over words, solve optimal transport problem on topics.
result Hierarchical optimal transport provides better interpretability and scalability with comparable performance.
This paper optimizes SMPC for neural network inference, reducing memory and time.
problem Memory and time constraints in secure neural network inference.
method Implemented ABY2.0 protocol, optimized memory usage, and used a helper node.
result MNIST inference reduced from 8.03 GB RAM and 200s to 0.2 GB RAM and 32s.
BNAS improves neural architecture search with a scalable, fast, and efficient approach.
problem Efficiently searching for optimal neural architectures with high performance and low training time.
method Designing a broad scalable architecture (BCNN) with reinforcement learning and parameter sharing, and developing two variants.
result Significantly reduces training time and achieves state-of-the-art performance on CIFAR-10 and ImageNet.
Improves scalability and robustness of dynamic graph clustering.
problem Scalability and robustness issues in matrix factorization methods for dynamic graphs.
method Temporal separated matrix factorization, bi-clustering regularization, selective embedding updating.
result Demonstrated scalability, robustness, and effectiveness on synthetic and real-world benchmarks.
New graph representation learning network improves scalability and feature integration.
problem Scalability and feature integration in graph neural networks for large, dense graphs.
method Adaptive sampling of neighbours based on weighted multi-step transition probabilities.
result Comparable or better results on various graph benchmarks.
This thesis tackles non-convex Bayesian learning via scalable dynamic importance sampling algorithms.
problem Non-convex Bayesian learning problem in deep neural networks.
method Replica exchange Langevin Monte Carlo, control variates method, population-chain replica exchange, scalable dynamic importance sampling.
result Control variates method reduces variance and accelerates convergence in non-convex Bayesian learning.
Improved scalability and interpretability in training data attribution.
problem Identifying which training data drives specific behaviors, especially unintended ones.
method Leveraging interpretable structures within the model to attribute model behavior to semantic directions, not individual test examples.
result Simple probe-based attribution methods are first-order approximations of Concept Influence that achieve comparable performance while being over an order-of-magnitude faster.
Shai-am simplifies ML for finance, solving code structure and scalability issues.
problem Challenges in integrating ML for investment strategies, including code structure and scalability.
method Integrates a Python framework with modern open-source technologies to manage containerized pipelines and unified interfaces.
result Facilitates collaborative work in quantitative finance by enhancing reusability and readability.
A scalable method for efficient inference in Gaussian process regression networks.
problem Intractable inference in Gaussian process regression networks (GPRN).
method Tensorization of output space, tensor/matrix-normal variational posteriors, joint optimization, and exploiting Kronecker product structure.
result Captures posterior dependencies and improves inference quality for large number of outputs.
TSNPE improves SBI efficiency and scalability.
problem Efficient and scalable simulation-based inference for complex models.
method Sequential inference with truncated proposals.
result TSNPE performs on par with previous methods and scales to complex models.
This paper surveys large-scale machine learning methods for efficient data analysis.
problem Efficiently processing large-scale data with machine learning models.
method Divided into three categories: model simplification, optimization approximation, and computation parallelism.
result Blueprint for future developments in large-scale machine learning.
Develops coresets for scalable multivariate distribution estimation.
problem Handling large-scale data in non-parametric or semi-parametric regression and density estimation.
method Novel coreset construction for multivariate conditional transformation models (MCTMs).
result Substantial data reduction with high log-likelihood accuracy.
Bayesian optimization method tackles combinatorial spaces, scalable for large data.
problem Optimization over combinatorial categorical spaces in natural sciences.
method Combines variational optimization and continuous relaxations for gradient-based optimization.
result Method performs comparably to state-of-the-art methods while scaling well.
Improved predictive uncertainties in Gaussian Process regression.
problem Substantially underestimated uncertainties in GP predictive distributions.
method Two methods for scalable GP regression: variational inference for FITC and direct posterior predictive distribution.
result Significantly better calibrated uncertainties and higher log likelihoods.
Enhances Gaussian processes with spherical features for better scalability and flexibility.
problem Lack of representation learning in Gaussian processes compared to deep neural networks.
method Introduces spherical inter-domain features to improve GP approximation and scalability.
result The method alleviates limitations and improves scalability compared to alternative strategies.
New method improves sampling from noisy energy models.
problem Training and sampling challenges in Energy-Based Models.
method Pseudo-Gibbs sampling with moment matching.
result Effective sampling from clean model using noisy DSM-trained model.