arXiv research

Proposes a method to handle sparse multiway count data with false zeros using zero-truncated Poisson regression.

We study the Thompson sampling algorithm in an adversarial setting, specifically, for adversarial bit prediction. We characterize the bit sequences with the smallest and largest expected regret. Among sequences of length $T$ with $k < \frac{T}{2}$ zeros, the sequences of largest regret consist of alternating zeros and …

A new metric, Weighted Regret, unifies FDR and power evaluation in online multiple testing.

Information systems have widely been the target of malware attacks. Traditional signature-based malicious program detection algorithms can only detect known malware and are prone to evasion techniques such as binary obfuscation, while behavior-based approaches highly rely on the malware training samples and incur prohi…

In high dimensional settings where a small number of regressors are expected to be important, the Lasso estimator can be used to obtain a sparse solution vector with the expectation that most of the non-zero coefficients are associated with true signals. While several approaches have been developed to control the inclu…

A new method detects and removes false trailing balances in credit data.

Proposes a new feature selection method integrating feature relationships.

Semi-supervised wrapper methods are concerned with building effective supervised classifiers from partially labeled data. Though previous works have succeeded in some fields, it is still difficult to apply semi-supervised wrapper methods to practice because the assumptions those methods rely on tend to be unrealistic i…

Neoclassical economics has two theories of competition between profit-maximizing firms (Marshallian and Cournot-Nash) that start from different premises about the degree of strategic interaction between firms, yet reach the same result, that market price falls as the number of firms in an industry increases. The Marsha…

The high-dimensional linear model $y = X β^0 + ε$ is considered and the focus is put on the problem of recovering the support $S^0$ of the sparse vector $β^0.$ We introduce Lasso-Zero, a new $\ell_1$-based estimator whose novelty resides in an "overfit, then threshold" paradigm and the use of noise dictionaries concate…

Solves selecting the best optimizing system problems.

Online monitor detects classifier drift and adapts predictions.

The PC algorithm allows investigators to estimate a complete partially directed acyclic graph (CPDAG) from a finite dataset, but few groups have investigated strategies for estimating and controlling the false discovery rate (FDR) of the edges in the CPDAG. In this paper, we introduce PC with p-values (PC-p), a fast al…

New method for sparse data using L1-NMF with improved sparsity control.

New method controls false discoveries in financial asset pricing.

Study examines flaws in probing LLMs' knowledge and introduces a new method.

New methods control false discoveries near the boundary in conformal novelty detection.

We present conditions under which positive alpha exists in the realm of active portfolio management- in contrast to the controversial result in Jarrow (2010, pg. 20) which implicates delegated portfolio management by surmising that positive alphas are illusionary. Specifically, we show that the critical assumption used…

There are two big unsolved mathematical questions in artificial intelligence (AI): (1) Why is deep learning so successful in classification problems and (2) why are neural nets based on deep learning at the same time universally unstable, where the instabilities make the networks vulnerable to adversarial attacks. We p…

New method controls false edge detections in Gaussian graphical models.

New algorithm optimizes AUC in binary classification and changepoint detection.

New indefinite false theta functions match homological blocks for a specific 3-manifold.

Learning a classifier with control on the false-positive rate plays a critical role in many machine learning applications. Existing approaches either introduce prior knowledge dependent label cost or tune parameters based on traditional classifiers, which lack consistency in methodology because they do not strictly adh…

Optimizes latency and false alarm probability in change detection problems.

New models extrapolate false alarms in ASV without new data.

New method calibrates false detection rates in sequential change detection.

Nonparametric IPSS selects features with false discovery control.

Accurate on-device keyword spotting (KWS) with low false accept and false reject rate is crucial to customer experience for far-field voice control of conversational agents. It is particularly challenging to maintain low false reject rate in real world conditions where there is (a) ambient noise from external sources s…

Paper tackles MIAs vulnerability by controlling FDR, providing guarantees on false discoveries.

Knockoffs method selects financial factors, controlling false discoveries.

In hypothesis testing, a false discovery occurs when a hypothesis is incorrectly rejected due to noise in the sample. When adaptively testing multiple hypotheses, the probability of a false discovery increases as more tests are performed. Thus the problem of False Discovery Rate (FDR) control is to find a procedure for…

Paper proposes knockoff-based methods to simplify deep neural networks by controlling false discovery rates.

Nowadays, advanced intrusion detection systems (IDSs) rely on a combination of anomaly detection and signature-based methods. An IDS gathers observations, analyzes behavioral patterns, and reports suspicious events for further investigation. A notorious issue anomaly detection systems (ADSs) and IDSs face is the possib…

Study controls error rates of binary classifiers using hypothesis testing.

New method controls false discoveries in online testing with deadlines.

Efron et al. (2001) proposed empirical Bayes formulation of the frequentist Benjamini and Hochbergs False Discovery Rate method (Benjamini and Hochberg,1995). This article attempts to unify the `two cultures' using concepts of comparison density and distribution function. We have also shown how almost all of the existi…

In this paper Hamiltonian system of time dependent periodic Newton equations is studied. It is shown that for dimensions $3$ and higher the following rigidity results holds true: If all the orbits in a neighborhood of infinity are action minimizing then the potential must be constant. This gives a generalization of the…

Develops robust knockoffs for controlling false discoveries in financial data.

New algorithm for adaptive experimental design in scientific settings.

Paper is withdrawn due to errors (superseded by math.AG/0604303). Formula 6.5 is false. Section 6 is false, and the main statement is true only for bundles $B$ with $c_1(B)$ SU(2)-invariant.

Paper withdrawn due to errors (superseded by math.AG/0604303). Proposition 11.4 is false, Section 12 is false, and the main statement is true only for bundles $B$ with $c_1(B)$ SU(2)-invariant.

This paper addresses the problem of inferring sparse causal networks modeled by multivariate auto-regressive (MAR) processes. Conditions are derived under which the Group Lasso (gLasso) procedure consistently estimates sparse network structure. The key condition involves a "false connection score." In particular, we sh…

Study optimizes sampling to avoid extreme tail risks in unknown heavy-tailed distributions.

Online anomaly detection in surveillance videos with false alarm rate bounds.

Study compares statistical properties and power of divergence measures for credit risk monitoring.

SDAMI enhances interpretable high-dimensional regression with sparse deep learning and footprint principle.

The paper develops a method to identify LLM-generated text without training.

Detects data drift in deep learning models using neural embeddings.