arXiv research

This paper studies the geometry of minimum-volume confidence sets for multinomial parameters.

New bounds found for minimum tests needed in group testing.

Paper proposes quantum methods for optimizing machine learning functions.

The Australian Government uses the means-test as a way of managing the pension budget. Changes in Age Pension policy impose difficulties in retirement modelling due to policy risk, but any major changes tend to be `grandfathered' meaning that current retirees are exempt from the new changes. In 2015, two important chan…

In this study, we construct two tests for the weights of the global minimum variance portfolio (GMVP) in a high-dimensional setting, namely, when the number of assets $p$ depends on the sample size $n$ such that $\frac{p}{n}\to c \in (0,1)$ as $n$ tends to infinity. In the case of a singular covariance matrix with rank…

OCmst detects anomalies using CNN features and MSTs.

Study excess risk in statistical inference with transformations.

Optimizes balanced treatment assignment for experiments.

Paper certifies intersection of minimum-volume confidence sets for multinomial outcomes.

Learning the minimum/maximum mean among a finite set of distributions is a fundamental sub-task in planning, game tree search and reinforcement learning. We formalize this learning task as the problem of sequentially testing how the minimum mean among a finite set of distributions compares to a given threshold. We deve…

We introduce a norm on the space of test configurations, which we call the minimum norm. We conjecture that uniform K-stability with respect to this norm is equivalent to the existence of a constant scalar curvature Kähler metric. This notion of uniform K-stability is analogous to coercivity of the Mabuchi functional. …

The paper improves A/B testing for non-Gaussian data, ensuring reliable results with large sample sizes.

Hill-ADAM optimizes loss landscapes by exploring state space deterministically.

Paper offers a method for finding the smallest sphere enclosing a set in d-dimensional space.

Nonparametric detection of existence of an anomalous structure over a network is investigated. Nodes corresponding to the anomalous structure (if one exists) receive samples generated by a distribution q, which is different from a distribution p generating samples for other nodes. If an anomalous structure does not exi…

Large learning rates improve neural network generalization, study shows.

A common challenge in nonparametric inference is its high computational complexity when data volume is large. In this paper, we develop computationally efficient nonparametric testing by employing a random projection strategy. In the specific kernel ridge regression setup, a simple distance-based test statistic is prop…

The nonparametric problem of detecting existence of an anomalous interval over a one dimensional line network is studied. Nodes corresponding to an anomalous interval (if exists) receive samples generated by a distribution q, which is different from the distribution p that generates samples for other nodes. If anomalou…

In this report, we present an unsupervised machine learning method for determining groups of molecular systems according to similarity in their dynamics or structures using Ward's minimum variance objective function. We first apply the minimum variance clustering to a set of simulated tripeptides using the information …

When a feed-forward neural network (FNN) is trained for source ranging in an ocean waveguide, it is difficult evaluating the range accuracy of the FNN on unlabeled test data. A fitting-based early stopping (FEAST) method is introduced to evaluate the range error of the FNN on test data where the distance of source is u…

We propose a non-parametric anomaly detection algorithm for high dimensional data. We first rank scores derived from nearest neighbor graphs on $n$-point nominal training data. We then train limited complexity models to imitate these scores based on the max-margin learning-to-rank framework. A test-point is declared as…

New algorithm tests model calibration in nearly-linear time.

Optimizes test set size for accurate diagnosis using machine learning.

Plug-in robust NPE method adapts summaries independently of pretrained NPE.

As a consequence of the dependence experienced in loan portfolios, the standard binomial test which is based on the assumption of independence does not appear appropriate for validating probabilities of default (PDs). The model underlying the new rules for minimum capital requirements (Basle II) is taken as a point of …

The calculation of minimum energy paths for transitions such as atomic and/or spin re-arrangements is an important task in many contexts and can often be used to determine the mechanism and rate of transitions. An important challenge is to reduce the computational effort in such calculations, especially when ab initio …

Adaptor 'E' extends gradient-based optimizers to explore loss landscapes, improving generalization.

New framework tests mean-variance spanning in high dimensions.

TPLVM models portfolio construction for non-Gaussian financial data.

Three algorithms improve starting solutions for clustering problems.

This paper optimizes portfolios using HRP and CLA algorithms on NIFTY 50 stocks.

The paper identifies the minimum mean-variance spanning set and its importance in asset evaluation.

Transformers learn linear models in-context without updates.

Inflating the minimum norm interpolator improves linear regression generalization error.

Paper develops a new test for high-dimensional matrix-valued data.

One-class classifiers are trained with target class only samples. Intuitively, their conservative modelling of the class description may benefit classical classification tasks where classes are difficult to separate due to overlapping and data imbalance. In this work, three methods are proposed which leverage on the co…

USP test improves on Pearson's chi-squared and $G$-test for independence.

Deviance Voronoi residuals improve earthquake insurance risk assessment.

We break dimension dependence in sparse distribution estimation with communication constraints.

This study proposes the segmentation procedure of univariate time series based on Fisher's exact test. We show that an adequate change point can be detected as the minimum value of p-value. It is shown that the proposed procedure can detect change points for an artificial time series. We apply the proposed method to fi…

GAAVI offers anytime-valid tests for CMF global null and contrasts.

In this work, we consider the sample complexity required for testing the monotonicity of distributions over partial orders. A distribution $p$ over a poset is monotone if, for any pair of domain elements $x$ and $y$ such that $x \preceq y$, $p(x) \leq p(y)$. To understand the sample complexity of this problem, we intro…

Information divergence functions play a critical role in statistics and information theory. In this paper we show that a non-parametric f-divergence measure can be used to provide improved bounds on the minimum binary classification probability of error for the case when the training and test data are drawn from the sa…

Study shows IRM framework can be unstable with small changes, leading to worse generalization.

There is some theoretical evidence that deep neural networks with multiple hidden layers have a potential for more efficient representation of multidimensional mappings than shallow networks with a single hidden layer. The question is whether it is possible to exploit this theoretical advantage for finding such represe…

Optimal policy for multi-hypothesis testing with controlled sensing to minimize delay and error.

Develops a neural network for global minimum variance portfolio optimization.

Study uses vine copulas to optimize financial portfolios during and after the financial crisis.