arXiv research

Tangent Works won GEFCom 2017 using automatic model building.

The minute fluctuations of of S&P 500 and NASDAQ 100 indices display Boltzmann statistics over a wide range of positive as well as negative returns, thus allowing us to define a {\em market temperature} for either sign. With increasing time the sharp Boltzmann peak broadens into a Gaussian whose volatility $σ$ measure…

New model uses heteroscedastic Gaussian process for alkenone SST proxy.

Develops shuffling gradient-based methods for nonconvex-concave minimax optimization.

SMG combines shuffling and momentum for non-convex optimization.

A new algorithm improves convergence rates for convex optimization problems.

A new Gaussian mechanism for differential privacy in the shuffle model is introduced.

Improves privacy amplification by shuffling for differential privacy.

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 …

New shuffling methods improve convergence without Lipschitz smoothness.

This work studies differential privacy in the context of the recently proposed shuffle model. Unlike in the local model, where the server collecting privatized data from users can track back an input to a specific user, in the shuffle model users submit their privatized inputs to a server anonymously. This setup yields…

Simpler, faster algorithm for uniformity testing in the shuffle model.

A new method resolves permutation issues in shuffled linear regression for large-scale applications.

BUDS balances privacy and utility by shuffling data, achieving strong privacy with minimal loss.

New convergence rates for shuffling gradient methods without strong convexity.

Optimal SGD rates achieved with shuffling, covering non-convex and convex cases.

SGD with random shuffling achieves lower optimization error than repeated shuffling.

Improved privacy-preserving summation protocol with fewer messages.

Unified analysis for shuffling-type gradient methods in optimization.

New protocols improve privacy in counting and selection problems with multiple messages.

New convergence bounds for shuffling-based SGD methods in distributed learning.

Proposes methods to recover labels from shuffled networks using graph averages.

New method to rank metrics on non-shuffled traffic.

Improved shuffling technique amplifies privacy guarantees for anonymous data contributions.

Improved shuffling gradient methods converge faster for nonsmooth convex optimization.

Enhanced financial reward with shuffled feature CNN-DRL.

New protocol makes federated learning more scalable and private.

New neural model processes 2D data with long-range dependencies efficiently.

New method decomposes local projections to reveal historical drivers of estimates.

Stochastic model prices weather derivatives for Indian states, highlighting temperature volatility impacts.

Power of network tests degrades when vertices are misaligned.

Random shuffle method boosts HF dataset size 10-21 times.

Water managers in the western United States (U.S.) rely on longterm forecasts of temperature and precipitation to prepare for droughts and other wet weather extremes. To improve the accuracy of these longterm forecasts, the U.S. Bureau of Reclamation and the National Oceanic and Atmospheric Administration (NOAA) launch…

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.

Paper proves privacy guarantees for shuffled and online PNSGD, reducing noise over time.

The paper analyzes the variance of different shuffling methods in stochastic gradient descent.

Proof of injection from double shuffle to Kashiwara-Vergne Lie algebra.

New method uses exponential family priors to handle shuffled data problems.

Balls-and-Bins sampling improves DP-SGD privacy and utility.

Is it possible to perform linear regression on datasets whose labels are shuffled with respect to the inputs? We explore this question by proposing several estimators that recover the weights of a noisy linear model from labels that are shuffled by an unknown permutation. We show that the analog of the classical least-…

A novel RNN model with shuffled hidden states.

Precisely forecasting wind speed is essential for wind power producers and grid operators. However, this task is challenging due to the stochasticity of wind speed. To accurately predict short-term wind speed under uncertainties, this paper proposed a multi-variable stacked LSTMs model (MSLSTM). The proposed method uti…

ShuffleNet is a state-of-the-art light weight convolutional neural network architecture. Its basic operations include group, channel-wise convolution and channel shuffling. However, channel shuffling is manually designed empirically. Mathematically, shuffling is a multiplication by a permutation matrix. In this paper, …

Random Reshuffling outperforms Stochastic Gradient Descent in smooth convex optimization.

Paper improves privacy bounds for shuffle model using novel numerical techniques.

Paper compares neural networks and time-series models for weather derivative pricing.

New algorithms for private generalized linear contextual bandits.