A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
New trade-off found between accuracy and adversarial robustness in regression.
A trade-off between accuracy and fairness is almost taken as a given in the existing literature on fairness in machine learning. Yet, it is not preordained that accuracy should decrease with increased fairness. Novel to this work, we examine fair classification through the lens of mismatched hypothesis testing: trying …
Vecchia approximations provide the best accuracy-runtime trade-off for Gaussian process approximations.
Efficiently approximates fairness-accuracy trade-offs for diverse datasets.
We provide a general framework for characterizing the trade-off between accuracy and robustness in supervised learning. We propose a method and define quantities to characterize the trade-off between accuracy and robustness for a given architecture, and provide theoretical insight into the trade-off. Specifically we in…
New insights into convergence and accuracy trade-offs in federated and meta-learning.
This paper analyzes the trade-off between accuracy and communication in personalized federated learning.
SCORE resolves the robustness vs accuracy trade-off by redefining robust error.
Proposes FACT, a diagnostic for understanding group fairness trade-offs.
Paper analyzes trade-offs in top-k classification accuracies and proposes a new loss function.
We analyze the trade-off between model complexity and accuracy for random forests by breaking the trees up into individual classification rules and selecting a subset of them. We show experimentally that already a few rules are sufficient to achieve an acceptable accuracy close to that of the original model. Moreover, …
Crowdsourced data used in machine learning services might carry sensitive information about attributes that users do not want to share. Various methods have been proposed to minimize the potential information leakage of sensitive attributes while maximizing the task accuracy. However, little is known about the theory b…
Unified approach improves accuracy in private estimation.
We present any-precision deep neural networks (DNNs), which are trained with a new method that allows the learned DNNs to be flexible in numerical precision during inference. The same model in runtime can be flexibly and directly set to different bit-widths, by truncating the least significant bits, to support dynamic …
We empirically investigate the best trade-off between sparse and uniformly-weighted multiple kernel learning (MKL) using the elastic-net regularization on real and simulated datasets. We find that the best trade-off parameter depends not only on the sparsity of the true kernel-weight spectrum but also on the linear dep…
Study on trade-offs between accuracy and interpretability in machine learning.
Study optimizes privacy in distributed optimization, balancing accuracy and communication.
New method optimizes privacy and compute trade-offs for deep learning.
New expressive losses improve adversarial robustness without sacrificing accuracy.
Learning domain-invariant representation is a dominant approach for domain generalization (DG), where we need to build a classifier that is robust toward domain shifts. However, previous domain-invariance-based methods overlooked the underlying dependency of classes on domains, which is responsible for the trade-off be…
This paper considers generalized linear models using rule-based features, also referred to as rule ensembles, for regression and probabilistic classification. Rules facilitate model interpretation while also capturing nonlinear dependences and interactions. Our problem formulation accordingly trades off rule set comple…
Study privacy and accuracy in high-dimensional LASSO with perturbation mechanisms.
We identify a trade-off between robustness and accuracy that serves as a guiding principle in the design of defenses against adversarial examples. Although this problem has been widely studied empirically, much remains unknown concerning the theory underlying this trade-off. In this work, we decompose the prediction er…
Recent works on adversarial perturbations show that there is an inherent trade-off between standard test accuracy and adversarial accuracy. Specifically, they show that no classifier can simultaneously be robust to adversarial perturbations and achieve high standard test accuracy. However, this is contrary to the stand…
New causal approach resolves fairness and accuracy trade-offs.
This work explores the trade-offs between stability and accuracy in statistical estimation.
For massive and heterogeneous modern datasets, it is of fundamental interest to provide guarantees on the accuracy of estimation when computational resources are limited. In the application of learning to rank, we provide a hierarchy of rank-breaking mechanisms ordered by the complexity in thus generated sketch of the …
Proposes a framework for balancing fairness and accuracy in data-restricted binary classification.
Optimizes communication in federated learning using rate-distortion theory.
A new discrete privacy mechanism for federated learning.
Paper analyzes trade-offs between fairness, privacy, and accuracy using Chernoff Information.
LiST improves neural network robustness and calibration without manual tuning.
FairVIC improves fairness in neural networks without sacrificing accuracy.
Deploying machine learning systems in the real world requires both high accuracy on clean data and robustness to naturally occurring corruptions. While architectural advances have led to improved accuracy, building robust models remains challenging. Prior work has argued that there is an inherent trade-off between robu…
Crowdsourcing platforms provide marketplaces where task requesters can pay to get labels on their data. Such markets have emerged recently as popular venues for collecting annotations that are crucial in training machine learning models in various applications. However, as jobs are tedious and payments are low, errors …
This work improves surrogate models for balancing accuracy and cost in multi-fidelity methods.
We introduce a new spatial data structure for high dimensional data called the \emph{approximate principal direction tree} (APD tree) that adapts to the intrinsic dimension of the data. Our algorithm ensures vector-quantization accuracy similar to that of computationally-expensive PCA trees with similar time-complexity…
Paper introduces EO_k for quantifying accuracy-fairness trade-offs in FRL.
New framework learns interpretable rule ensembles without sacrificing accuracy.
Despite achieving remarkable success in various domains, recent studies have uncovered the vulnerability of deep neural networks to adversarial perturbations, creating concerns on model generalizability and new threats such as prediction-evasive misclassification or stealthy reprogramming. Among different defense propo…
A third of adults in America use the Internet to diagnose medical concerns, and online symptom checkers are increasingly part of this process. These tools are powered by diagnosis models similar to clinical decision support systems, with the primary difference being the coverage of symptoms and diagnoses. To be useful …
iMOCA optimizes multiple objectives with continuous approximations for resource efficiency.
New algorithm balances spatial data approximation and prediction accuracy.
Two Fisher information matrix estimators are analyzed for neural networks, focusing on their variances and trade-offs.
In critical decision-making scenarios, optimizing accuracy can lead to a biased classifier, hence past work recommends enforcing group-based fairness metrics in addition to maximizing accuracy. However, doing so exposes the classifier to another kind of bias called infra-marginality. This refers to individual-level bia…
This work analyzes fairness-accuracy trade-offs using causal methods.
Algorithm speeds up search for stationary targets with guaranteed accuracy.
Most of the work on interpretable machine learning has focused on designing either inherently interpretable models, which typically trade-off accuracy for interpretability, or post-hoc explanation systems, which lack guarantees about their explanation quality. We propose an alternative to these approaches by directly r…