New algorithm selects features more efficiently than existing methods.
problem Feature selection and attribute reduction in machine learning.
method Hybridizes B-SFLA with FRDD for efficient feature selection.
result Significantly outperforms other metaheuristic methods in feature selection and classification accuracy.
In this article, a compact finite difference method is proposed for pricing European and American options under jump-diffusion models. Partial integro-differential equation and linear complementary problem governing European and American options respectively are discretized using Crank-Nicolson Leap-Frog scheme. In pro…
Machine learning detects frog calls in audio recordings with high accuracy.
problem Estimating frog presence in audio recordings from tropical environments.
method Gaussian mixture model classification of frog calls with 20 cepstral features.
result The algorithm achieved an average weighted error rate of 0.9% for nine species classification.
SGD learns neural networks with a complexity measure called leap.
problem Time complexity of SGD learning on neural networks.
method Introduced a complexity measure called leap, proved conjecture for Gaussian data, and showed saddle-to-saddle dynamics.
result Proved a conjecture about the time complexity of learning functions with low-dimensional support.
LEAP predicts graph edges and weights from path aggregations.
problem Predicting edges and weights in graphs.
method Trainable framework based on path aggregations.
result LEAP outperforms state-of-the-art methods in link and rating prediction.
LEAPS samples discrete distributions via CTMCs and locally equivariant networks.
problem Sampling from discrete distributions with known normalization.
method Continuous-time Markov chain, locally equivariant functions, attention layers, convolutional networks.
result LEAPS minimizes the variance of importance weights, improving sampling efficiency.
Develops shuffling gradient-based methods for nonconvex-concave minimax optimization.
problem Nonconvex-concave minimax optimization problems.
method Two shuffling gradient-based algorithms for nonconvex-linear and nonconvex-strongly concave settings.
result Achieves state-of-the-art oracle complexity in nonconvex optimization and best-known complexity bounds for nonconvex-strongly concave setting.
SMG combines shuffling and momentum for non-convex optimization.
problem Non-convex finite-sum optimization problems.
method Shuffling Gradient-based method with momentum.
result Established state-of-the-art convergence rates for SMG.
A new algorithm improves convergence rates for convex optimization problems.
problem Convex optimization problems with finite-sum structure.
method Nesterov Accelerated Shuffling Gradient (NASG) integrating Nesterov's acceleration with different shuffling schemes.
result Improved convergence rate of O(1/T) for unified shuffling schemes.
A new Gaussian mechanism for differential privacy in the shuffle model is introduced.
problem Improving differential privacy in distributed learning environments.
method Characterization and upper-bounding of Rényi differential privacy (RDP) for the shuffle Gaussian mechanism.
result The shuffle Gaussian mechanism provides improved privacy guarantees compared to existing methods.
Improves privacy amplification by shuffling for differential privacy.
problem Enhancing privacy guarantees in systems with anonymous data contributions.
method Theoretical and numerical analysis of Rényi differential privacy parameters and privacy amplification by shuffling.
result First asymptotically optimal analysis of Rényi differential privacy parameters for shuffled outputs.
When using stochastic gradient descent to solve large-scale machine learning problems, a common practice of data processing is to shuffle the training data, partition the data across multiple machines if needed, and then perform several epochs of training on the re-shuffled (either locally or globally) data. The above …
This work improves privacy in the shuffle model for summing real numbers.
problem Improving privacy in the shuffle model for data aggregation.
method Developed optimal single message protocol and new lower and upper bounds for privacy amplification.
result Improved accuracy and communication in summing real numbers protocol.
New shuffling methods improve convergence without Lipschitz smoothness.
problem Lack of convergence guarantees for shuffling methods under non-Lipschitz conditions.
method Revisit shuffling methods, prove convergence under general bounded variance condition.
result Matched current best-known convergence rates without Lipschitz smoothness.
Improved solver maintains positivity and accuracy across all time steps.
problem Linear second-order schemes for Fokker-Planck equation cannot preserve positivity.
method Flux-Corrected Diagonal Frog (FCDF) framework using nonlinear extension and iterative limiter.
result FCDF schemes are unconditionally positive across all time steps and maintain second-order accuracy.
LEAP nets model power grid disruptions for rapid response.
problem Modeling and predicting power grid disruptions.
method Transfer learning neural network embedding approach.
result LEAP nets can rapidly assess human operators' actions in emergencies.
Simpler, faster algorithm for uniformity testing in the shuffle model.
problem Testing uniformity of data in the shuffle model with privacy constraints.
method Simplified analysis and use of privacy amplification via shuffling.
result An algorithm with the same guarantees but simpler and more streamlined.
A new method resolves permutation issues in shuffled linear regression for large-scale applications.
problem Estimating latent features through linear transformation with unknown permutations.
method Spectral matching method to align spectral components of measurement and feature covariances.
result Achieves accurate estimates in shuffled LS and LASSO settings with sufficient samples.
BUDS balances privacy and utility by shuffling data, achieving strong privacy with minimal loss.
problem Balancing privacy and utility in crowd-sourced statistical databases.
method One-hot encoding, iterative shuffling, loss estimation, risk minimization.
result Achieves ε=0.02 for privacy, maintaining a privacy bound of ε=ln[t/((n1−1)S)]. New convergence rates for shuffling gradient methods without strong convexity.
problem Theoretical gap between shuffling gradient methods' empirical success and established convergence rates.
method Proved last-iterate convergence rates for shuffling gradient methods using function value gap.
result First last-iterate convergence rates for shuffling gradient methods without strong convexity.
Optimal SGD rates achieved with shuffling, covering non-convex and convex cases.
problem Optimizing finite-sum optimization problems with shuffling strategies.
method RandomShuffle and SingleShuffle algorithms for SGD, analyzing convergence rates.
result Minimax optimal convergence rates established, generalizing to non-convex costs.
SGD with random shuffling achieves lower optimization error than repeated shuffling.
problem Optimization of smooth and strongly-convex finite-sum problems.
method Lower bounds on SGD with random shuffling and repeated shuffling.
result Lower bounds on SGD with random shuffling and repeated shuffling reveal performance gaps.
Two-layer networks learn faster with batch reuse, overcoming information and leap exponents.
problem Limitations of gradient flow and single-pass GD in learning multi-index target functions.
method Multi-pass gradient descent that reuses batches, analyzed using Dynamical Mean-Field Theory.
result Two-time-step overlap with target subspace for non-staircase functions, overcoming information and leap exponents.
Deep learning identifies frog species and detects new ones.
problem Identifying and detecting new species in morphologically similar groups.
method Machine learning, specifically deep neural networks, applied to frog hind limb skin texture.
result Deep neural networks can classify images into known species and new classes.
Improved privacy-preserving summation protocol with fewer messages.
problem Achieving efficient differential privacy in multi-party summation.
method Combining secure shuffling with Laplace mechanism in the shuffle model.
result Protocol with O(1/ε) error and O(log(n/δ)) messages per party. Unified analysis for shuffling-type gradient methods in optimization.
problem Optimization of finite-sum problems using shuffling strategies.
method Unified convergence analysis for various shuffling methods.
result Improved convergence rates for nonconvex problems and matching rates for convex problems.
Diagonal Frog: High-order positivity-preserving FD schemes for anisotropic Fokker-Planck equations
problem Positivity-preserving discretizations for anisotropic Fokker-Planck equations
method Diagonal Frog discretization
result Second-order accuracy and mass conservation
Leap framework transfers knowledge across learning processes for improved performance.
problem Transfer learning struggles with new tasks not linked to previous tasks.
method Leap framework associates each task with a manifold and minimizes the expected path length during training.
result Leap outperforms competing methods in meta-learning and transfer learning.
New protocols improve privacy in counting and selection problems with multiple messages.
problem Improving privacy in counting and selection problems with multiple messages.
method Analyzed frequency estimation and selection problems in the shuffled model with multiple messages per user.
result Protocols with multiple messages achieve exponential improvements in error compared to single-message protocols.
New convergence bounds for shuffling-based SGD methods in distributed learning.
problem Analyzing the performance of shuffling-based variants of SGD in distributed learning.
method Study of minibatch and local Random Reshuffling methods, proving convergence bounds and lower bounds.
result Shuffling-based variants converge faster than with-replacement sampling methods, and the bounds are tight.
LEAPS uses semantic models to improve reinforcement learning in diverse environments.
problem Generalizing and adapting to unseen environments in reinforcement learning.
method Hybrid model-based and model-free approach with a multi-target sub-policy and a Bayesian semantic model.
result LEAPS outperforms baselines in visual navigation tasks using diverse indoor scenes.
Proposes methods to recover labels from shuffled networks using graph averages.
problem Recovering labels from a shuffled network using graph averages.
method Cluster networks into classes, then match the new graph to cluster-averages, minimizing the graph matching objective function.
result Higher fidelity matching performance when clustering networks into different classes.
New method to rank metrics on non-shuffled traffic.
problem Position bias in ranking metrics due to item display order.
method Leverage stochasticity of recommendation policy to mitigate position bias.
result Improved ranking metrics without shuffling recommendations.
Improved shuffling technique amplifies privacy guarantees for anonymous data contributions.
problem Enhancing privacy in systems where data is contributed anonymously.
method Developed a new approach to random shuffling that amplifies differential privacy guarantees.
result Achieved asymptotically optimal privacy amplification with nearly optimal dependence in ε.
Improved shuffling gradient methods converge faster for nonsmooth convex optimization.
problem Improving convergence rates for nonsmooth convex optimization problems.
method Analysis of shuffling gradient methods, focusing on Random Reshuffle and Single Shuffle strategies.
result Shuffling gradient methods, particularly Random Reshuffle and Single Shuffle, converge faster than Proximal Gradient Descent for nonsmooth convex optimization.
The paper analyzes sampling efficiency of discrete diffusion models, providing sharp and adaptive guarantees.
problem Theoretical foundations of discrete diffusion models, especially sampling efficiency.
method Continuous-time Markov chain (CTMC) formulation, τ-leaping-based samplers, effective total correlation. result The τ-leaping algorithm achieves an iteration complexity of order ildeO(d/ε) for uniform discrete diffusion, improving existing bounds by a factor of d. Enhanced financial reward with shuffled feature CNN-DRL.
problem Improving reward in financial data using CNN-DRL.
method Applying shuffled features to financial data for CNN-DRL.
result Substantial enhancement in reward attainment.
New protocol makes federated learning more scalable and private.
problem Securely aggregate data from distributed, private datasets.
method Proposes a new protocol for aggregation in the shuffled model that is more efficient in terms of communication and error.
result Achieves differential privacy guarantees with polylogarithmic scaling in the number of users.
New neural model processes 2D data with long-range dependencies efficiently.
problem Limited receptive field of convolutions for complex 2D tasks.
method Proposes Matrix Shuffle-Exchange network with O(logn) layers and O(n2logn) complexity. result Exceeds convolutional and graph neural network baselines in long-range dependency modeling.
Study efficient estimation of hidden subspaces in Gaussian Multi-index models.
problem Estimating hidden subspaces in Gaussian Multi-index models with low-dimensional projections.
method Introduced the generative leap exponent and developed an agnostic sequential estimation procedure using spectral U-statistics.
result Achieved optimal sample complexity of $n=Θ(d^{1 \vee \k/2})$ for efficient estimation.
Power of network tests degrades when vertices are misaligned.
problem Power loss in network hypothesis testing due to vertex shuffling.
method Theoretical analysis and simulations of Frobenius norm differences in random dot product and stochastic block models.
result Shuffling vertices can significantly reduce the power of network tests.
Random shuffle method boosts HF dataset size 10-21 times.
problem Narrow dataset cardinality in HF clinical studies.
method Random shuffle method to enhance dataset size.
result Enhanced HF dataset size 10-21 times.
Randomly shuffled kernels can be compressed efficiently.
problem Reducing storage cost of CNN parameters on resource-limited platforms.
method Randomly-shuffled tensor decomposition (RsTD) to embed kernels into random low-rank subspaces.
result CNNs can be significantly compressed even with randomly shuffled kernels, achieving more stable accuracy.
Data poisoning is an attack on machine learning models wherein the attacker adds examples to the training set to manipulate the behavior of the model at test time. This paper explores poisoning attacks on neural nets. The proposed attacks use "clean-labels"; they don't require the attacker to have any control over the …
We consider the problem of inference in a linear regression model in which the relative ordering of the input features and output labels is not known. Such datasets naturally arise from experiments in which the samples are shuffled or permuted during the protocol. In this work, we propose a framework that treats the un…
Sampling without replacement speeds up optimization in minimax problems.
problem Optimizing minimax problems with faster convergence rates.
method Analysis of gradient descent ascent and proximal point method with two sampling strategies.
result Sampling without replacement leads to faster convergence rates in minimax optimization.
Paper proves privacy guarantees for shuffled and online PNSGD, reducing noise over time.
problem Privacy amplification in shuffled and online PNSGD settings.
method Iterative analysis of PNSGD with hidden updates, proving privacy guarantees for shuffled and online settings.
result Privacy guarantees for shuffled and online PNSGD with reduced noise over time.
The paper analyzes the variance of different shuffling methods in stochastic gradient descent.
problem Understanding the variance of different shuffling methods in stochastic gradient descent.
method Power spectral density analysis to study the noise sequences of stochastic gradients.
result The stationary variances of iterates decrease in the order of SGD, SGD-RR, and SGD-SO.