arXiv research

New algorithm for reinforcement learning in uncertain environments with unknown thresholds.

Noise makes learning linear thresholds hard, but algorithms can still learn near-optimal thresholds.

A new SSL method uses instance-dependent thresholds to improve accuracy.

Paper introduces threshold invariant fairness to ensure equitable predictions across different groups.

The thresholded feature has recently emerged as an extremely efficient, yet rough empirical approximation, of the time-consuming sparse coding inference process. Such an approximation has not yet been rigorously examined, and standard dictionaries often lead to non-optimal performance when used for computing thresholde…

"Sparse" neural networks, in which relatively few neurons or connections are active, are common in both machine learning and neuroscience. Whereas in machine learning, "sparsity" is related to a penalty term that leads to some connecting weights becoming small or zero, in biological brains, sparsity is often created wh…

Iterative shrinkage/thresholding algorithm (ISTA) is a well-studied method for finding sparse solutions to ill-posed inverse problems. In this letter, we present a data-driven scheme for learning optimal thresholding functions for ISTA. The proposed scheme is obtained by relating iterations of ISTA to layers of a simpl…

This work interprets GELU and related activations via a first-order loss function.

Study evaluates thresholds for removing noise from DNN weights using random matrix theory.

AIHT improves online high-dimensional quantile regression by separating support discovery and refinement.

New method trains neural networks with threshold activation functions efficiently.

LTP learns per-layer thresholds for efficient pruning of deep networks.

MTSSL optimizes threshold τ for better semi-supervised learning performance.

This study optimizes multi-modal learning thresholds and algorithms in high dimensions.

Lactate threshold is considered an essential parameter when assessing performance of elite and recreational runners and prescribing training intensities in endurance sports. However, the measurement of blood lactate concentration requires expensive equipment and the extraction of blood samples, which are inconvenient f…

Study fairness in ordinal regression using threshold models.

The paper establishes a nearly-sharp statistical threshold for efficient learning in Latent MDPs with separated components.

Study active learning of PTFs with derivative access.

Multi-task feature learning aims to identity the shared features among tasks to improve generalization. It has been shown that by minimizing non-convex learning models, a better solution than the convex alternatives can be obtained. Therefore, a non-convex model based on the capped-$\ell_{1},\ell_{1}$ regularization wa…

We consider the problem of learning a non-negative linear classifier with a $1$-norm of at most $k$, and a fixed threshold, under the hinge-loss. This problem generalizes the problem of learning a $k$-monotone disjunction. We prove that we can learn efficiently in this setting, at a rate which is linear in both $k$ and…

MLShrink integrates machine learning with wavelet shrinkage for denoising.

Iterative thresholding algorithms seek to optimize a differentiable objective function over a sparsity or rank constraint by alternating between gradient steps that reduce the objective, and thresholding steps that enforce the constraint. This work examines the choice of the thresholding operator, and asks whether it i…

The mushroom body is the key network for the representation of learned olfactory stimuli in Drosophila and insects. The sparse activity of Kenyon cells, the principal neurons in the mushroom body, plays a key role in the learned classification of different odours. In the specific case of the fruit fly, the sparseness o…

A new adaptive binarization technique using fuzzy integrals improves image quality.

Paper proves fair classification can be done via simple thresholding.

New method learns sparse distributions by thresholding samples, improving performance and efficiency.

Improved learning bounds for corrupted data using thresholded gradient descent.

Developed a new thresholding method that connects soft and hard thresholding.

Robust learning mixtures of linear regressions improve robustness.

It has been reported repeatedly that discriminative learning of distance metric boosts the pattern recognition performance. A weak point of ITML-based methods is that the distance threshold for similarity/dissimilarity constraints must be determined manually and it is sensitive to generalization performance, although t…

The assumption that the values of model parameters are known or correctly learned, i.e., the Nishimori condition, is one of the requirements for the detectability analysis of the stochastic block model in statistical inference. In practice, however, there is no example demonstrating that we can know the model parameter…

We provide high probability finite sample complexity guarantees for hidden non-parametric structure learning of tree-shaped graphical models, whose hidden and observable nodes are discrete random variables with either finite or countable alphabets. We study a fundamental quantity called the (noisy) information threshol…

Sparsity in Deep Neural Networks (DNNs) is studied extensively with the focus of maximizing prediction accuracy given an overall parameter budget. Existing methods rely on uniform or heuristic non-uniform sparsity budgets which have sub-optimal layer-wise parameter allocation resulting in a) lower prediction accuracy o…

High-dimensional models become unstable when sample size falls below a critical level, leading to a phase transition.

System classifies lung CT scans into normal or COVID-19 using machine learning.

This paper is concerned with the hard thresholding operator which sets all but the $k$ largest absolute elements of a vector to zero. We establish a {\em tight} bound to quantitatively characterize the deviation of the thresholded solution from a given signal. Our theoretical result is universal in the sense that it ho…

Learning β for k-SAT with one sample is hard, especially for low degrees.

Analyzes biased random walks and corrupted intervals in adversarial settings.

Classifiers based on sparse representations have recently been shown to provide excellent results in many visual recognition and classification tasks. However, the high cost of computing sparse representations at test time is a major obstacle that limits the applicability of these methods in large-scale problems, or in…

Class imbalance presents a major hurdle in the application of data mining methods. A common practice to deal with it is to create ensembles of classifiers that learn from resampled balanced data. For example, bagged decision trees combined with random undersampling (RUS) or the synthetic minority oversampling technique…

Study stability thresholds of big line bundles, proving bounds and generalizing results.

Adaptive algorithm for outlier detection by balancing arm exploration and threshold estimation.

Paper bounds tensor decomposition's RLCT, aiding Bayesian inference.

In this paper we studied about the wavelet identification of the thresholds and time delay for more general case without the constraint that the time delay is smaller than the order of the model. Here we composed an empirical wavelet from the SETAR (Self-Exciting Threshold Autoregressive) model and identified the thres…

In this paper, we consider the Graphical Lasso (GL), a popular optimization problem for learning the sparse representations of high-dimensional datasets, which is well-known to be computationally expensive for large-scale problems. Recently, we have shown that the sparsity pattern of the optimal solution of GL is equiv…

The article examines different thresholding methods for improving PAM algorithm in cancer classification.

New RL method learns K-step lookahead Q-functions for fixed-horizon MDPs.

Framework uses human feedback to safely set OOD detection thresholds, reducing false positives.