arXiv research

The paper introduces Robust Correlated Equilibrium for games with time-varying costs and proposes an algorithm to achieve it.

Study insurance pricing under correlation ambiguity without increasing prices or reducing utility.

New research shows no-regret learning is impossible in Markov games under certain assumptions.

We introduce CSE for MLSF games and devise online learning algorithms for achieving no-external Stackelberg-regret.

DREAM learns optimal strategies in imperfect games without needing a simulator.

In this paper we explore the functional correlation approach to operational risk. We consider networks with heterogeneous a-priori conditional and unconditional failure probability. In the limit of sparse connectivity, self-consistent expressions for the dynamical evolution of order parameters are obtained. Under equil…

Proposes PSCCA for estimating correlations and canonical correlations in sparse count data.

This work discusses the problem of sparse signal recovery when there is correlation among the values of non-zero entries. We examine intra-vector correlation in the context of the block sparse model and inter-vector correlation in the context of the multiple measurement vector model, as well as their combination. Algor…

We consider a market model that consists of financial investors and producers of a commodity. Producers optionally store some production for future sale and go short on forward contracts to hedge the uncertainty of the future commodity price. Financial investors take positions in these contracts in order to diversify t…

Path-independent equilibrium models improve network performance on harder problems.

This paper improves sample efficiency for learning equilibria in multi-player games.

This paper introduces Schur-constant equilibrium distribution models of dimension n for arithmetic non-negative random variables. Such a model is defined through the (several orders) equilibrium distributions of a univariate survival function. First, the bivariate case is considered and analyzed in depth, stressing the…

We study optimal behavior of energy producers under a CO_2 emission abatement program. We focus on a two-player discrete-time model where each producer is sequentially optimizing her emission and production schedules. The game-theoretic aspect is captured through a reduced-form price-impact model for the CO_2 allowance…

Sparse GCA finds linear relationships in multiple datasets, using gradient descent.

Proposes $\ell_0$-CCA for sparse CCA with improved representation learning.

Models analyze strategic risk-taking in continuous action games.

We address the sparse signal recovery problem in the context of multiple measurement vectors (MMV) when elements in each nonzero row of the solution matrix are temporally correlated. Existing algorithms do not consider such temporal correlations and thus their performance degrades significantly with the correlations. I…

Correlations and other collective phenomena in a schematic model of heterogeneous binary agents (individual spin-glass samples) are considered on the complete graph and also on 2d and 3d regular lattices. The system's stochastic dynamics is studied by numerical simulations. The dynamics is so slow that one can meaningf…

Study efficient offline RL in Markov games with general models.

Given two data matrices $X$ and $Y$, sparse canonical correlation analysis (SCCA) is to seek two sparse canonical vectors $u$ and $v$ to maximize the correlation between $Xu$ and $Yv$. However, classical and sparse CCA models consider the contribution of all the samples of data matrices and thus cannot identify an unde…

Optimal algorithm for two-player zero-sum games with linear parameterization.

Efficient reinforcement learning for simultaneous-move zero-sum games using optimistic value iteration.

Method estimates sparse inverse covariance and partial correlation matrices efficiently.

This paper tackles traffic volume estimation challenges with a deep learning method.

Canonical correlation analysis was proposed by Hotelling [6] and it measures linear relationship between two multidimensional variables. In high dimensional setting, the classical canonical correlation analysis breaks down. We propose a sparse canonical correlation analysis by adding l1 constraints on the canonical vec…

Paper proposes ASCCA for sparse CCA with trace Lasso regularization.

V-learning tackles multiagent reinforcement learning by reducing sample complexity.

Detecting correlated trees helps align sparse graphs.

We consider the high-dimensional sparse linear regression problem of accurately estimating a sparse vector using a small number of linear measurements that are contaminated by noise. It is well known that the standard cadre of computationally tractable sparse regression algorithms---such as the Lasso, Orthogonal Matchi…

The performance of sparse signal recovery from noise corrupted, underdetermined measurements can be improved if both sparsity and correlation structure of signals are exploited. One typical correlation structure is the intra-block correlation in block sparse signals. To exploit this structure, a framework, called block…

A new method for sparse Gaussian process regression using correlated experts.

Proposes a flexible MGP model for dynamic, sparse correlations.

Proposes GCCA for detecting latent relations in multiview data with sparse structures.

We study historical dynamics of joint equilibrium distribution of stock returns in the U.S. stock market using the Boltzmann distribution model being parametrized by external fields and pairwise couplings. Within Boltzmann learning framework for statistical inference, we analyze historical behavior of the parameters in…

The paper examines insurance market dynamics and optimal regulation.

The paper solves a portfolio selection problem in incomplete markets by balancing utility and risk.

msPCA solves sparse PCA for multiple components efficiently.

We present a novel method for solving Canonical Correlation Analysis (CCA) in a sparse convex framework using a least squares approach. The presented method focuses on the scenario when one is interested in (or limited to) a primal representation for the first view while having a dual representation for the second view…

We consider the scenario where one observes an outcome variable and sets of features from multiple assays, all measured on the same set of samples. One approach that has been proposed for dealing with this type of data is ``sparse multiple canonical correlation analysis'' (sparse mCCA). All of the current sparse mCCA t…

New methods learn correlated equilibria in large games without structural assumptions.

Iterative reweighted algorithms, as a class of algorithms for sparse signal recovery, have been found to have better performance than their non-reweighted counterparts. However, for solving the problem of multiple measurement vectors (MMVs), all the existing reweighted algorithms do not account for temporal correlation…

Modeling price formation in intraday electricity markets with renewable generation.

We examine the recovery of block sparse signals and extend the framework in two important directions; one by exploiting signals' intra-block correlation and the other by generalizing signals' block structure. We propose two families of algorithms based on the framework of block sparse Bayesian learning (BSBL). One fami…

Study on Kyle's model with stochastic liquidity impacts asset volatility.

Canonical Correlation Analysis (CCA) is a classical tool for finding correlations among the components of two random vectors. In recent years, CCA has been widely applied to the analysis of genomic data, where it is common for researchers to perform multiple assays on a single set of patient samples. Recent work has pr…

Sparse codes improve optimal control tasks with correlated inputs.

New method estimates sparse covariance matrices in logit mixtures.

Exact and scalable algorithm for Gaussian process regression with Matérn correlations.