arXiv research

This paper analyzes stock market data to predict share prices using regression models.

Algorithm reduces regret in distributed kernel bandits with shared randomness.

Weight-sharing is rarely helpful in NAS, especially on small datasets.

Exact optimality achieved in distributed mean estimation with shared randomness.

The paper shows how shared random seeds can reduce variance in machine learning evaluations.

The paper solves MMV and MV problems with random coefficients and finds shared optimal strategies.

Efficient privacy-preserving machine learning framework using random transformations.

Neural architecture search (NAS) is a promising research direction that has the potential to replace expert-designed networks with learned, task-specific architectures. In this work, in order to help ground the empirical results in this field, we propose new NAS baselines that build off the following observations: (i) …

This research shows how to learn shared representations from unpaired data.

New risk-sharing rules induced by capital allocation principles.

The vulnerability of machine learning systems to adversarial attacks questions their usage in many applications. In this paper, we propose a randomized diversification as a defense strategy. We introduce a multi-channel architecture in a gray-box scenario, which assumes that the architecture of the classifier and the t…

The paper improves classification accuracy by leveraging a shared signal across domains in high-dimensional classification.

The large majority of risk-sharing transactions involve few agents, each of whom can heavily influence the structure and the prices of securities. This paper proposes a game where agents' strategic sets consist of all possible sharing securities and pricing kernels that are consistent with Arrow-Debreu sharing rules. F…

We introduce and discuss a nonlinear kinetic equation of Boltzmann type which describes the evolution of wealth in a pure gambling process, where the entire sum of wealths of two agents is up for gambling, and randomly shared between the agents. For this equation the analytical form of the steady states is found for va…

Efficient search methods can outperform random search on challenging tasks.

Optimal distributed testing under communication constraints with shared randomness.

Multiplex networks, a special type of multilayer networks, are increasingly applied in many domains ranging from social media analytics to biology. A common task in these applications concerns the detection of community structures. Many existing algorithms for community detection in multiplexes attempt to detect commun…

Model improves covariance estimation from shared and distinct datasets.

The paper studies an oligopolistic equilibrium model of financial agents who aim to share their random endowments. The risk-sharing securities and their prices are endogenously determined as the outcome of a strategic game played among all the participating agents. In the complete-market setting, each agent's set of st…

Random walk constructs Morse functions on surfaces.

The paper examines stability of shares in Proof of Stake protocol, identifying different investor behaviors and phase transitions.

New model for multiplex networks learns shared structure.

A method for sharing synthetic data without revealing actual data or model parameters.

We propose a new set of stylized facts quantifying the structure of financial markets. The key idea is to study the combined structure of both investment strategies and prices in order to open a qualitatively new level of understanding of financial and economic markets. We study the detailed order flow on the Shenzhen …

SIMPLE-RC method tests group membership profiles in large networks with weak signals.

Optimal strategies are found for a repeated betting game using diffusion approximation.

Developed policy gradient methods for stochastic control with exit time, outperforming traditional techniques in share repurchase pricing.

Researchers predict NBA player salaries using machine learning, avoiding overfitting.

The group membership prediction (GMP) problem involves predicting whether or not a collection of instances share a certain semantic property. For instance, in kinship verification given a collection of images, the goal is to predict whether or not they share a {\it familial} relationship. In this context we propose a n…

This paper uses graph convolutional networks to improve the accuracy of neural architecture search.

Advances in molecular "omics'" technologies have motivated new methodology for the integration of multiple sources of high-content biomedical data. However, most statistical methods for integrating multiple data matrices only consider data shared vertically (one cohort on multiple platforms) or horizontally (different …

New strategy achieves optimal regret without communication or collisions in multi-player bandit.

In increasingly many settings, data sets consist of multiple samples from a population of networks, with vertices aligned across these networks. For example, brain connectivity networks in neuroscience consist of measures of interaction between brain regions that have been aligned to a common template. We consider the …

PLS-SVD struggles with missing data in multimodal datasets, showing a phase transition in performance.

Randomized block-diagonal preconditioning improves parallel learning convergence.

We develop necessary and sufficient conditions and a novel provably consistent and efficient algorithm for discovering topics (latent factors) from observations (documents) that are realized from a probabilistic mixture of shared latent factors that have certain properties. Our focus is on the class of topic models in …

For a risk vector $V$, whose components are shared among agents by some random mechanism, we obtain asymptotic lower and upper bounds for the individual agents' exposure risk and the aggregated risk in the market. Risk is measured by Value-at-Risk or Conditional Tail Expectation. We assume Pareto tails for the componen…

BIDIFAC+ factorizes linked matrices for cancer studies.

The likelihood model of high dimensional data $X_n$ can often be expressed as $p(X_n|Z_n,θ)$, where $θ\mathrel{\mathop:}=(θ_k)_{k\in[K]}$ is a collection of hidden features shared across objects, indexed by $n$, and $Z_n$ is a non-negative factor loading vector with $K$ entries where $Z_{nk}$ indicates the strength of …

Model predicts stock prices using Twitter sentiment data.

New insights into choosing between two data integration methods based on SVD.

Estimates network topologies from shared graphon models across different networks.

When observations are organized into groups where commonalties exist amongst them, the dependent random measures can be an ideal choice for modeling. One of the propositions of the dependent random measures is that the atoms of the posterior distribution are shared amongst groups, and hence groups can borrow informatio…

We propose three measures of mutual dependence between multiple random vectors. All the measures are zero if and only if the random vectors are mutually independent. The first measure generalizes distance covariance from pairwise dependence to mutual dependence, while the other two measures are sums of squared distance…

The beta-negative binomial process (BNBP), an integer-valued stochastic process, is employed to partition a count vector into a latent random count matrix. As the marginal probability distribution of the BNBP that governs the exchangeable random partitions of grouped data has not yet been developed, current inference f…

The high-order relations between the content in social media sharing platforms are frequently modeled by a hypergraph. Either hypergraph Laplacian matrix or the adjacency matrix is a big matrix. Randomized algorithms are used for low-rank factorizations in order to approximately decompose and eventually invert such big…

Unified market-based description of returns and variances of trades.

Better signal detection in undersampled data using joint and cross covariances.