arXiv research

Study shows finite agent equilibrium converges to mean-field limit in asset pricing.

Study on optimal trading in a finite population with market frictions and asymmetric information.

Existence of Radner equilibrium proven with growing population.

Many modern data analysis problems involve inferences from streaming data. However, streaming data is not easily amenable to the standard probabilistic modeling approaches, which assume that we condition on finite data. We develop population variational Bayes, a new approach for using Bayesian modeling to analyze strea…

NeuPL learns diverse policies in strategy games efficiently.

The study investigates the consistency of $k$-means clustering under finite expectation assumptions.

In this work, we systematically investigate mean field games and mean field type control problems with multiple populations using a coupled system of forward-backward stochastic differential equations of McKean-Vlasov type stemming from Pontryagin's stochastic maximum principle. Although the same cost functions as well…

Parallel recordings of neural spike counts have revealed the existence of context-dependent noise correlations in neural populations. Theories of population coding have also shown that such correlations can impact the information encoded by neural populations about external stimuli. Although studies have shown that the…

Consider the problem of sampling sequentially from a finite number of $N \geq 2$ populations, specified by random variables $X^i_k$, $i = 1,\ldots , N,$ and $k = 1, 2, \ldots$; where $X^i_k$ denotes the outcome from population $i$ the $k^{th}$ time it is sampled. It is assumed that for each fixed $i$, $\{ X^i_k \}_{k …

SDRF estimates complex survey designs for conditional distributions.

Paper extends conformal prediction to complex survey data.

Study controlled contagion with state-dependent killing, proving a comparison principle.

Estimates population profile from small random samples.

DICE learns population dynamics from discrete samples.

New algorithm finds approximate stationary points faster under differential privacy constraints.

This study improves audit sampling by using sequential procedures with statistical guarantees.

Combines trial and observational data to improve policy evaluation.

Paper analyzes SVGD algorithm for non-asymptotic convergence.

Efficiently estimates variable importance in prediction tasks using Shapley values.

Proposes a neural network method to combine nonprobability and probability survey samples.

New algorithm for countable bandits with optimal regret.

Develops an equilibrium model for securities pricing in a mixed cooperative and non-cooperative market.

We consider the \mnk{classical} problem of a controller activating (or sampling) sequentially from a finite number of $N \geq 2$ populations, specified by unknown distributions. Over some time horizon, at each time $n = 1, 2, \ldots$, the controller wishes to select a population to sample, with the goal of sampling fro…

Consider the problem of a controller sampling sequentially from a finite number of $N \geq 2$ populations, specified by random variables $X^i_k$, $i = 1,\ldots , N,$ and $k = 1, 2, \ldots$; where $X^i_k$ denotes the outcome from population $i$ the $k^{th}$ time it is sampled. It is assumed that for each fixed $i$, $\{…

Policy mirror ascent achieves Nash equilibrium in mean field games without a population generative model.

This study analyzes LTS in sparse models with finite sample error bounds.

We analyze double descent in finite-width neural networks using influence functions.

Model-based clustering imposes a finite mixture modelling structure on data for clustering. Finite mixture models assume that the population is a convex combination of a finite number of densities, the distribution within each population is a basic assumption of each particular model. Among all distributions that have …

We develop a general framework for proving rigorous guarantees on the performance of the EM algorithm and a variant known as gradient EM. Our analysis is divided into two parts: a treatment of these algorithms at the population level (in the limit of infinite data), followed by results that apply to updates based on a …

The paper analyzes the maximum margin algorithm's performance on noisy data.

Technological progress is leading to proliferation and diversification of trading venues, thus increasing the relevance of the long-standing question of market fragmentation versus consolidation. To address this issue quantitatively, we analyse systems of adaptive traders that choose where to trade based on their previ…

Study of entropy-regularized LQG MFGs with exploratory actions.

Bayesian approach learns nonparametric mixture components from heterogeneous data.

Additive principal components (APCs for short) are a nonlinear generalization of linear principal components. We focus on smallest APCs to describe additive nonlinear constraints that are approximately satisfied by the data. Thus APCs fit data with implicit equations that treat the variables symmetrically, as opposed t…

We formalize AURC and develop estimators for SC systems.

The method approximates stationary distributions of Markov models by truncating irrelevant states.

In order to interact intelligently with objects in the world, animals must first transform neural population responses into estimates of the dynamic, unknown stimuli which caused them. The Bayesian solution to this problem is known as a Bayes filter, which applies Bayes' rule to combine population responses with the pr…

The emergence of complex life on Earth is often attributed to the arms race that ensued from a huge number of organisms all competing for finite resources. We present an artificial intelligence research environment, inspired by the human game genre of MMORPGs (Massively Multiplayer Online Role-Playing Games, a.k.a. MMO…

Trimming helps in conformal prediction when it separates anomaly scores.

We study convergence of a generative modeling method that first estimates the score function of the distribution using Denoising Auto-Encoders (DAE) or Denoising Score Matching (DSM) and then employs Langevin diffusion for sampling. We show that both DAE and DSM provide estimates of the score of the Gaussian smoothed p…

New method improves NN performance across various settings.

The paper derives statistics of multi-factor functions from their Fourier transforms.

FAQ efficiently evaluates LLMs with statistical guarantees using adaptive query selection.

Estimates class prior for unlabeled data using kernel embedding.

Study optimal investment in large populations of competitive, heterogeneous agents.

We consider the problem of sequential sampling from a finite number of independent statistical populations to maximize the expected infinite horizon average outcome per period, under a constraint that the expected average sampling cost does not exceed an upper bound. The outcome distributions are not known. We construc…

Develops certificates for local population-risk increments using cross-fitted ridge calibration.

Estimates intrinsic dimension of data for GANs.