MOMENT selects and estimates mixed-effects models using moment identities.
problem Selecting and estimating random-effects covariance matrix and fixed-effects coefficients in multiresponse linear mixed-effects models.
method MOMENT is a stage-wise moment-based framework that reduces the random-effects selection problem to a smooth constrained convex optimization problem.
result MOMENT performs competitively and can outperform separate univariate analyses for correlated responses.
The paper improves generalization bounds for domain adaptation.
problem Improving generalization bounds for domain adaptation under practical conditions.
method Derives generalization bounds for domain adaptation based on finitely many moments and smoothness conditions.
result Obtains generalization bounds for domain adaptation.
A new method for smoothing and parameter inference of Markov jump processes.
problem Approximate inference for Markov jump processes with latent variables.
method Moment-based variational inference with partitioning of transition classes.
result Expressed KL divergence in terms of moment functions.
Estimates MLDS using tensor decomposition, improving upon existing methods.
problem Learning mixtures of linear dynamical systems from input-output data.
method Proposes a moment-based estimator using tensor decomposition.
result Improves sample complexity bounds for estimating MLDS.
A fast method for fitting deep hierarchical models to large datasets.
problem Fitting a deeply-nested hierarchical model to a large book review dataset.
method Moment-based estimator extension for arbitrarily deep hierarchies.
result Orders of magnitude faster than standard maximum likelihood procedures.
The total duration of drawdowns is shown to provide a moment-free, unbiased, efficient and robust estimator of Sharpe ratios both for Gaussian and heavy-tailed price returns. We then use this quantity to infer an analytic expression of the bias of moment-based Sharpe ratio estimators as a function of the return distrib…
Proposes a method to propagate uncertainty through tanh activation in reservoir computing.
problem Computing output distribution for neural networks with tanh activation when given a probability distribution.
method Moment-based approach to propagate uncertainty through Echo State Network (ESN).
result Probabilistic Echo State Network (PESN) shows better performance than deterministic ESNs.
This thesis studies domain adaptation under minimal distribution similarity assumptions using moments.
problem Learning from samples with distributions different from training samples.
method Uses minimal similarity assumptions modeled by moments.
result Establishes learning bounds and algorithms for domain adaptation.
Estimates RL data for dynamic treatment effects using GMM.
problem Estimating dynamic treatment effects from RL data with nonstationary behavior policies.
method Weighted GMM approach to stabilize variance in adaptive RL settings.
result Valid hypothesis testing and confidence regions for dynamic treatment effects.
A model for pricing dividends and interest rates.
problem Modeling the term structures of dividends and interest rates.
method Polynomial jump-diffusions and moment-based approximation for option pricing.
result A parsimonious model fits interest rate swaps, swaptions, and dividend futures and options.
We present a general probabilistic perspective on Gaussian filtering and smoothing. This allows us to show that common approaches to Gaussian filtering/smoothing can be distinguished solely by their methods of computing/approximating the means and covariances of joint probabilities. This implies that novel filters and …
The performance of a modulation classifier is highly sensitive to channel signal-to-noise ratio (SNR). In this paper, we focus on amplitude-phase modulations and propose a modulation classification framework based on centralized data fusion using multiple radios and the hybrid maximum likelihood (ML) approach. In order…
New model estimates corporate defaults using pure jump processes, capturing extreme events.
problem Estimating corporate defaults using standard diffusion models that underestimate short-term probabilities.
method Introduced pure jump processes with negative jumps only, derived formulas, calibrated parameters, and implemented practical tools.
result Models redistribute credit risk towards shorter maturities, improving short-term default probability estimates.
We present an efficient algorithm for learning mixed membership models when the number of variables p is much larger than the number of hidden components k. This algorithm reduces the computational complexity of state-of-the-art tensor methods, which require decomposing an O(p3) tensor, to factorizing…
The paper proposes a method to monitor deep learning predictions for retraining, reducing costs.
problem Reducing computational costs in deep learning by detecting when predictions are no longer valid.
method Sequential monitoring of network predictions based on projected second moments monitoring.
result The proposed method can drastically reduce computational costs in deep learning.
Second-order optimization speeds up deep hedging for complex options.
problem Hedging exotic options with market frictions in realistic markets.
method Second-order optimization scheme leveraging pathwise differentiability and Kronecker-factoring.
result Our method optimizes the policy in 1/4 the steps of standard optimization.
We present a detailed analysis of \emph{observable} moments based parameter estimators for the Heston SDEs jointly driving the rate of returns Rt and the squared volatilities Vt. Since volatilities are not directly observable, our parameter estimators are constructed from empirical moments of realized volatilitie…
Corrected moment-based methods improve inference in topic model regression.
problem Inferential difficulties in topic model plug-in workflow for regression.
method Corrected spectral moment methods for LDA, response-weighted word moments.
result Direct identification of regression coefficients without estimating topic shares.
New method approximates diffusion process posteriors using moment functions.
problem Approximating posteriors of stochastic differential equations.
method Constructs variational process as controlled prior, approximates posterior with moment functions, uses natural gradient descent.
result Richer variational approximations for state-dependent diffusion terms.
DOLCE improves off-policy evaluation and learning by decomposing effects.
problem Bias in off-policy evaluation and learning due to policy mismatch.
method Uses lagged contexts and a moment-based training procedure to decompose and cancel bias.
result DOLCE achieves substantial improvements in off-policy evaluation and learning.
New algorithms for risk management in incomplete markets.
problem Risk management in incomplete markets with various sources of incompleteness.
method Machine-learning-based algorithms to solve hedging problems.
result One algorithm is flexible and can use multiple risk criteria.
The paper proposes estimators for bid-ask spreads with and without serial dependence.
problem Estimating bid-ask spreads in financial markets with and without serial dependence.
method The authors propose moment-based estimators for bid-ask spreads, considering both geometric Brownian motion and geometric fractional Brownian motion for price dynamics, and Ornstein-Uhlenbeck process for microstructure noise.
result The estimators are consistent and asymptotically normal, and perform well compared to existing approaches on simulated data.
This work provides a computationally efficient and statistically consistent moment-based estimator for mixtures of spherical Gaussians. Under the condition that component means are in general position, a simple spectral decomposition technique yields consistent parameter estimates from low-order observable moments, wit…
Stochastic Kronecker graphs supply a parsimonious model for large sparse real world graphs. They can specify the distribution of a large random graph using only three or four parameters. Those parameters have however proved difficult to choose in specific applications. This article looks at method of moments estimators…
Predictive state representations (PSRs) offer an expressive framework for modelling partially observable systems. By compactly representing systems as functions of observable quantities, the PSR learning approach avoids using local-minima prone expectation-maximization and instead employs a globally optimal moment-base…
We describe a method for parameter estimation in bipartite probabilistic graphical models for joint prediction of clinical conditions from the electronic medical record. The method does not rely on the availability of gold-standard labels, but rather uses noisy labels, called anchors, for learning. We provide a likelih…
Enhanced Adam uses higher-order moments for better performance.
problem Improving the performance of Adam optimization algorithm.
method Proposes HAdam, an extension of Adam using higher-order moments of the stochastic gradient.
result Higher-order moments of the stochastic gradient can lead to better performance than vanilla Adam.
Improved neural network models predict molecular and material properties efficiently.
problem Training neural networks for accurate interatomic potentials is computationally expensive.
method Gaussian moment-based neural networks with improved architecture and active learning.
result The new models achieve high accuracy and reduced training times.
This paper proposes a novel model of financial prices where: (i) prices are discrete; (ii) prices change in continuous time; (iii) a high proportion of price changes are reversed in a fraction of a second. Our model is analytically tractable and directly formulated in terms of the calendar time and price impact curve. …
Modeling asset trading strategies with noisy information.
problem Understanding heterogeneous trading strategies in markets with information friction.
method Developed a behavioral asset pricing model using a thin set and extended method of moments.
result The model accurately predicts return time series moments of real data.
New method improves Bayesian cross-validation.
problem Finding good proposal distributions for importance sampling.
method Implicitly adaptive importance sampling that iteratively matches moments.
result Better than many existing parametric adaptive importance sampling methods.
Neural Hawkes method estimates cryptocurrency market microstructure and causality.
problem Estimating non-parametric Hawkes processes in high dimensions.
method Physics-informed neural networks for solving integral equations.
result Robust estimation of Hawkes processes in high dimensions.
New statistical test for change-point detection using relative entropy.
problem Offline change-point detection using divergence metrics.
method Study of empirical relative entropy distributions, derivation of approximations, introduction of new Berry-Esseen bounds.
result Theoretical and practical validation of relative entropy for change-point detection.
Simple mean and std-based classifier outperforms chance on 69 out of 128 time-series problems.
problem Time-series classification accuracy comparison
method Linear classifier using mean and standard deviation features
result Simple distributional features outperform chance on 69 out of 128 time-series problems
BGM-IV uses AI to estimate causal effects in complex data.
problem Estimating causal effects in high-dimensional, nonlinear settings with endogeneity.
method Structured latent generative modeling for posterior inference in a causally structured latent space.
result BGM-IV outperforms existing methods in high-dimensional covariate regimes.
The paper introduces a scale law for detecting distribution shift in high-dimensional embeddings.
problem Detecting changes in high-dimensional embedding streams.
method The paper presents a scale law that constrains moment-based statistics for detecting distribution shift. It also introduces a calibration rule for kernel tests.
result The scale law predicts the optimal bandwidth for kernel tests, leading to superior performance in detecting distribution shifts.
Suppose k centers are fit to m points by heuristically minimizing the k-means cost; what is the corresponding fit over the source distribution? This question is resolved here for distributions with p≥4 bounded moments; in particular, the difference between the sample cost and distribution cost decays with $…
A robust approach compensates for small-data tasks in mixed linear regression.
problem Learning from small batches of data in tasks with many similar but insufficiently labeled examples.
method Spectral approach combining outlier-robust PCA and sum-of-squares algorithms.
result The approach achieves a graceful statistical trade-off, allowing smaller tasks than previously required.
Completely random measures (CRM) represent the key building block of a wide variety of popular stochastic models and play a pivotal role in modern Bayesian Nonparametrics. A popular representation of CRMs as a random series with decreasing jumps is due to Ferguson and Klass (1972). This can immediately be turned into a…
Extends post-prediction inference method for more accurate AI/ML data analysis.
problem Naively using AI/ML predictions as true observations leads to biased results.
method Extends Wang et al. method to relax assumptions and incorporate a scaling factor.
result Yields unbiased point estimates and proper coverage in simulations.
A novel method for learning DAGs from positive-valued data.
problem Causal discovery from observational data of positive-valued variables.
method Hybrid Moment-Ratio Scoring (H-MRS) algorithm combining moment-based scoring and log-scale regression.
result H-MRS integrates log-scale Ridge regression for moment-ratio estimation with a greedy ordering procedure based on raw-scale moment ratios, followed by Elastic Net-based parent selection.
Tensor methods tackle high-dimensional additive index models with discordance and heterogeneity.
problem High-dimensional datasets with sampling problems and heterogeneity.
method Method of moments based procedures for estimating indices of discordant additive index models.
result Rates of convergence of estimators in both high and low-dimensional settings.
Algorithm learns near-optimal policies for reward-mixing MDPs with few latent contexts.
problem Episodic reinforcement learning in reward-mixing Markov decision processes with a few latent contexts.
method Sample-efficient algorithm EM^2 using higher-order method-of-moments approach.
result Provides an ε-optimal policy using O(ε^(-2) * S^d A^d * poly(H, Z)^d) episodes for arbitrary M ≥ 2.
We propose a fair principal component analysis method that balances reconstruction error and subgroup fairness.
problem Fairness and robustness in principal component analysis for consequential domains.
method Distributionally robust optimization over the Stiefel manifold with a Riemannian subgradient descent.
result The proposed method achieves better performance on real-world datasets compared to state-of-the-art baselines.
The paper compares three option pricing models with varying volatility dynamics.
problem Comparing the accuracy and efficiency of different option pricing models with changing volatility.
method Used stochastic volatility models including Heston and MSV, and compared them with existing models on 15 index option datasets.
result Stochastic volatility models achieve comparable accuracy to existing models and are faster to calibrate.
Proposes DWMD for better matching of hidden representations across domains.
problem Measuring data distribution discrepancy between semantically related domains for feature representation matching.
method DWMD, a moment-based probability distribution metric that explicitly orders and weights higher-order moments.
result DWMD is error-free and can strictly reflect distribution differences without feature distribution assumptions.
Study on convergence of graph neural networks on random graphs.
problem Convergence of message passing graph neural networks on large random graphs.
method Extended convergence results to a broad class of aggregation functions using McDiarmid inequality.
result Non-asymptotic bounds for convergence quantified with high probability.
New framework uses score-based priors to solve ill-conditioned polynomial equations, improving signal recovery from noisy data.
problem Recovering signals from low-order moments in inverse problems, especially ill-conditioned polynomial equations.
method Integrates score-based diffusion priors with moment-based estimators to regularize and solve nonlinear inverse problems.
result Diffusion priors improve recovery from third-order moments and make super-resolution MTD feasible.