arXiv research

Majorizing measures control sequential complexities for online learning.

We consider the problem of sequential prediction and provide tools to study the minimax value of the associated game. Classical statistical learning theory provides several useful complexity measures to study learning with i.i.d. data. Our proposed sequential complexities can be seen as extensions of these measures to …

Unified deep sequential and state-space models for robust option pricing with uncertainty.

Characterizing temporal dependence patterns is a critical step in understanding the statistical properties of sequential data. Long Range Dependence (LRD) --- referring to long-range correlations decaying as a power law rather than exponentially w.r.t. distance --- demands a different set of tools for modeling the unde…

The study formalizes temporal precision and recall for anomaly detection in sequences.

We present an information-theoretic framework for sequential adaptive compressed sensing, Info-Greedy Sensing, where measurements are chosen to maximize the extracted information conditioned on the previous measurements. We show that the widely used bisection approach is Info-Greedy for a family of $k$-sparse signals b…

This paper presents a framework for exact discovery of the top-k sequential patterns under Leverage. It combines (1) a novel definition of the expected support for a sequential pattern - a concept on which most interestingness measures directly rely - with (2) SkOPUS: a new branch-and-bound algorithm for the exact disc…

We address the problem of retrieving relevant experiments given a query experiment, motivated by the public databases of datasets in molecular biology and other experimental sciences, and the need of scientists to relate to earlier work on the level of actual measurement data. Since experiments are inherently noisy and…

The paper extends consistency results for sequential design strategies to vector-valued Gaussian processes.

A new method reduces computational costs for testing RF variable importance measures.

New calibration measure SSCE ensures truthful prediction, unlike existing measures.

A new kernel Stein test assesses fit for variable-length sequential data.

New method for sequential probability assignment reduces regret using contextual Shtarkov sums.

Protocol learns pure quantum states with minimal disturbance.

CRPS improves GP-based sequential design for chemical space.

Sparsity and low-rank models have been popular for reconstructing images and videos from limited or corrupted measurements. Dictionary or transform learning methods are useful in applications such as denoising, inpainting, and medical image reconstruction. This paper proposes a framework for online (or time-sequential)…

RNN-HAR model improves VaR forecasting with long-memory and non-linear dynamics.

A computer code can simulate a system's propagation of variation from random inputs to output measures of quality. Our aim here is to estimate a critical output tail probability or quantile without a large Monte Carlo experiment. Instead, we build a statistical surrogate for the input-output relationship with a modest …

New private algorithm for sequential hypothesis testing with privacy and error rate guarantees.

New algorithms minimize MMD to approximate probability measures efficiently.

New method for mixed data FI controls type I error and achieves high power.

We analyze the problem of sequential probability assignment for binary outcomes with side information and logarithmic loss, where regret---or, redundancy---is measured with respect to a (possibly infinite) class of experts. We provide upper and lower bounds for minimax regret in terms of sequential complexities of the …

A two-step approach efficiently selects hyperparameters for FCMs.

The paper develops a method to learn cost-optimal sequential testing policies from retrospective data.

Recent sequential pattern mining methods have used the minimum description length (MDL) principle to define an encoding scheme which describes an algorithm for mining the most compressing patterns in a database. We present a novel subsequence interleaving model based on a probabilistic model of the sequence database, w…

The paper studies the distance from calibration in sequential prediction, proving upper and lower bounds.

The paper analyzes greedy algorithms for MMD minimization, showing their efficiency and approximation error.

RLMM extends psychometric models to larger tasks.

Bayesian optimization through Gaussian process regression is an effective method of optimizing an unknown function for which every measurement is expensive. It approximates the objective function and then recommends a new measurement point to try out. This recommendation is usually selected by optimizing a given acquis…

New method selects features for sequential decision making.

EvoRate metric assesses learnability of sequential data by measuring predictive information.

New test detects independence in streaming data, adapting to data complexity.

Algorithm optimizes measurement sequence to minimize data acquisition.

Sequential tests for two-sample and independence testing using betting strategies.

Systemic risk measures are crucial for the stability of financial markets, yet classical formulations fail to capture the complexity of market volatility. We propose a new framework for systemic risk measurement on the variable-exponent Bochner-Lebesgue space $L^{p(\cdot)}$, where the exponent $p(\cdot)$ is a random va…

Optimizes treatment allocation using covariates for better outcomes.

New bounds for sequential tests under power-one error levels.

URGE improves diffusion model quality without gradients or Hessian.

Heterogeneous SVO leads to diverse policies in sequential social dilemmas.

A key limitation of sampling algorithms for approximate inference is that it is difficult to quantify their approximation error. Widely used sampling schemes, such as sequential importance sampling with resampling and Metropolis-Hastings, produce output samples drawn from a distribution that may be far from the target …

New methods optimize experiment selection for sequential data, improving model accuracy.

New method uses transport maps for efficient Bayesian inference.

Combines multi-fidelity and asynchronous batch methods for faster experimental design.

This paper proposes a novel dynamic Hierarchical Dirichlet Process topic model that considers the dependence between successive observations. Conventional posterior inference algorithms for this kind of models require processing of the whole data through several passes. It is computationally intractable for massive or …

In this paper we assume a multivariate risk model has been developed for a portfolio and its capital derived as a homogeneous risk measure. The Euler (or gradient) principle, then, states that the capital to be allocated to each component of the portfolio has to be calculated as an expectation conditional to a rare eve…

Recent studies identified that sequential Recommendation is improved by the attention mechanism. By following this development, we propose Relation-Aware Kernelized Self-Attention (RKSA) adopting a self-attention mechanism of the Transformer with augmentation of a probabilistic model. The original self-attention of Tra…

Study efficient resource allocation for detecting extreme values.

In this paper we develop a Bayesian procedure for estimating multivariate stochastic volatility (MSV) using state space models. A multiplicative model based on inverted Wishart and multivariate singular beta distributions is proposed for the evolution of the volatility, and a flexible sequential volatility updating is …