Kernel method has been developed as one of the standard approaches for nonlinear learning, which however, does not scale to large data set due to its quadratic complexity in the number of samples. A number of kernel approximation methods have thus been proposed in the recent years, among which the random features metho…
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23 results for “RB”
RB-Modulation: Training-Free Personalization of Diffusion Models using Stochastic Optimal Controlcs.LG
RB-Modulation trains free diffusion models without external adapters.
problem Training-free personalization of diffusion models with style and content control.
method Stochastic optimal control with a style descriptor and cross-attention aggregation.
result Precise content and style extraction and control without external adapters.
New method quantifies redundant information using information bottleneck.
problem Quantifying redundant information among multiple sources.
method Formulated as an information bottleneck problem, termed redundancy bottleneck.
result Extracts information that best predicts the target without revealing source identity.
We study the residual bootstrap (RB) method in the context of high-dimensional linear regression. Specifically, we analyze the distributional approximation of linear contrasts , where is a ridge-regression estimator. When regression coefficients are estimated via least squares, classical…
Efficient bandit exploration for various distributions without distribution-specific tuning.
problem Optimizing exploration in multi-armed bandit models for different distributions.
method Sub-sampling Duelling Algorithms (SDA) with Random Block sampling for efficient exploration.
result Achieves asymptotically optimal regret for Bernoulli, Gaussian, and Poisson distributions.
Spectral clustering is one of the most effective clustering approaches that capture hidden cluster structures in the data. However, it does not scale well to large-scale problems due to its quadratic complexity in constructing similarity graphs and computing subsequent eigendecomposition. Although a number of methods h…
Lyapunov-based analysis shows polynomial sample complexity for WCMDPs and RBs.
problem Learning in WCMDPs and RBs under a generative model.
method Lyapunov-based analysis framework.
result Near-optimal policies can be learned with polynomial complexity.
Policy optimization on high-dimensional continuous control tasks exhibits its difficulty caused by the large variance of the policy gradient estimators. We present the action subspace dependent gradient (ASDG) estimator which incorporates the Rao-Blackwell theorem (RB) and Control Variates (CV) into a unified framework…
Generative Adversarial Networks (GAN) have become one of the most successful frameworks for unsupervised generative modeling. As GANs are difficult to train much research has focused on this. However, very little of this research has directly exploited game-theoretic techniques. We introduce Generative Adversarial Netw…
We describe a bicategory of reduced orbifolds in the framework of classical differential geometry (i.e. without any explicit reference to notions of Lie groupoids or differentiable stacks, but only using orbifold atlases, local lifts and changes of charts). In order to construct such a …
Adaptive Batching for Gaussian Process Surrogates with Application in Noisy Level Set Estimationstat.ML
Adaptive batching improves Gaussian process surrogates for noisy level set estimation.
problem Learning the level set of noisy simulator responses.
method Developed four novel adaptive batching schemes for Gaussian process metamodels.
result Adaptive batching brings significant computational speed-ups with minimal loss of modeling fidelity.
An active learning approach reduces AoI violation in vehicular networks.
problem Dynamic nature of vehicular networks makes resource allocation challenging.
method Gaussian process regression (GPR) for online decentralized active learning.
result Significant improvement in AoI violation probability with at least 50% reduction.
Optimizes convergence time of federated learning over wireless networks.
problem Limited resource blocks in wireless networks affect federated learning convergence time and performance.
method Formulates an optimization problem to minimize convergence time while optimizing performance, proposes a probabilistic user selection scheme and uses ANNs for estimation.
result Improves convergence time and performance of federated learning over wireless networks.
Study clarifies almost Ricci-Bourguignon solitons and their properties.
problem Understanding the properties of almost Ricci-Bourguignon solitons.
method Revisit and compare with known results of Barros and Ribeiro.
result Identify conditions for compact almost RB-solitons to be trivial or have special properties.
In this paper, the problem of training federated learning (FL) algorithms over a realistic wireless network is studied. In particular, in the considered model, wireless users execute an FL algorithm while training their local FL models using their own data and transmitting the trained local FL models to a base station …
Physics-informed neural networks improve surrogate modeling of turbulent Rayleigh-Bénard convection.
problem Modeling turbulent Rayleigh-Bénard convection with high accuracy and efficiency.
method Physics-informed neural networks (PINNs) with novel padding and regularization techniques.
result Significantly improved predictive accuracy of surrogate models at high Rayleigh numbers Ra = 2 × 10^9.
The paper proposes a conjecture for a symmetric version of Ehrhard's inequality.
problem Formulating a conjecture for the optimal Ehrhard-type inequality for convex symmetric sets.
method Formulating a conjecture and explaining its optimality in terms of Gaussian concavity power.
result Proving certain inequalities for symmetric convex sets, with round k-cylinders as the only equality cases.
We study the problem of "isotropically rounding" a polytope , that is, computing a linear transformation which makes the uniform distribution on the polytope have roughly identity covariance matrix. We assume is defined by linear inequalities, with guarantee that , w…
Save for some special cases, current training methods for Generative Adversarial Networks (GANs) are at best guaranteed to converge to a `local Nash equilibrium` (LNE). Such LNEs, however, can be arbitrarily far from an actual Nash equilibrium (NE), which implies that there are no guarantees on the quality of the found…
New model CRS combines transparency and high performance for classification.
problem Need models with transparent structure and high classification performance.
method Concept Rule Sets (CRS) with Multilayer Logical Perceptron (MLLP) and Random Binarization (RB).
result CRS outperforms state-of-the-art approaches and has low complexity.
Bayesian SAE model with spectral clustering and uncertainty quantification.
problem Small Area Estimation (SAE) with uncertainty quantification.
method Spectral clustering with external covariates, posterior projections, and CPMSE.
result Closed form expressions for posterior mean estimators and CPMSE.
ReQuestNet simplifies 5G channel estimation with a unified model.
problem Complex channel estimation in 5G systems with varying conditions.
method Unified neural architecture that handles dynamic resource blocks and transmit layers.
result Significantly outperforms legacy methods, achieving up to 10dB gain at high SNRs.
This paper defines ribbons and ribbon complexes in CW spaces and analyzes their topological properties.
problem Characterizing and analyzing topological structures in CW spaces.
method Introducing planar ribbons, ribbon complexes, and ribbon nerves in Alexandroff-Hopf-Whitehead CW spaces, and studying their topological properties.
result Characterization of ribbons and ribbon nerves by Betti numbers and homotopy types.