arXiv research

The paper supports task classification from eye movements, achieving 95.4% accuracy.

ADA augments data using AR replicas for robust regression.

AdaS adapts SGD learning rate based on knowledge gain metrics.

Reinforce-Ada improves RL for language models by adaptively sampling difficult prompts.

New algorithm boosts classification for imbalanced data.

This paper addresses detection of a reverse engineering (RE) attack targeting a deep neural network (DNN) image classifier; by querying, RE's aim is to discover the classifier's decision rule. RE can enable test-time evasion attacks, which require knowledge of the classifier. Recently, we proposed a quite effective app…

Ada-LISTA adapts neural solvers for varying models.

Single neuron with ADA learns XOR and outperforms other functions.

Two algorithms improve federated learning efficiency and resilience.

Adaptive stochastic gradient methods such as AdaGrad have gained popularity in particular for training deep neural networks. The most commonly used and studied variant maintains a diagonal matrix approximation to second order information by accumulating past gradients which are used to tune the step size adaptively. In…

Unified view of GNNs as graph signal denoising.

Proposes Ada-Sit method for mortality prediction of rare diseases.

As much as data science is playing a pivotal role everywhere, healthcare also finds it prominent application. Breast Cancer is the top rated type of cancer amongst women; which took away 627,000 lives alone. This high mortality rate due to breast cancer does need attention, for early detection so that prevention can be…

Ada-BKB optimizes black-box functions on continuous domains with adaptive discretization.

KD improves DNN performance on unseen data without hyperparameter tuning.

Accurate diagnosis of Alzheimer's Disease (AD) entails clinical evaluation of multiple cognition metrics and biomarkers. Metrics such as the Alzheimer's Disease Assessment Scale - Cognitive test (ADAS-cog) comprise multiple subscores that quantify different aspects of a patient's cognitive state such as learning, memor…

Cyber-Physical Systems (CPSs) have been pervasive including smart grid, autonomous automobile systems, medical monitoring, process control systems, robotics systems, and automatic pilot avionics. As usually implemented on embedded devices, CPS is typically constrained by computation capacity and energy consumption. In …

New algorithm reduces regret in online portfolio and quantum state learning.

This paper certifies cluster assignments from sum-of-norms clustering algorithms.

We introduce a novel personalized Gaussian Process Experts (pGPE) model for predicting per-subject ADAS-Cog13 cognitive scores -- a significant predictor of Alzheimer's Disease (AD) in the cognitive domain -- over the future 6, 12, 18, and 24 months. We start by training a population-level model using multi-modal data …

In this paper, we introduce the use of a personalized Gaussian Process model (pGP) to predict the key metrics of Alzheimer's Disease progression (MMSE, ADAS-Cog13, CDRSB and CS) based on each patient's previous visits. We start by learning a population-level model using multi-modal data from previously seen patients us…

Fair MP-Boost improves fairness and interpretability in boosting methods.

Survey of gradient boosting algorithms and their applications.

A new gradient tree boosting framework reduces variance and accelerates performance.

MP-Boost boosts accuracy faster and more interpretable than AdaBoost.

Boost-R uses gradient boosted trees for analyzing recurrence data.

Residual Networks are shown to be equivalent to boosting feature representation.

Boosted decision trees typically yield good accuracy, precision, and ROC area. However, because the outputs from boosting are not well calibrated posterior probabilities, boosting yields poor squared error and cross-entropy. We empirically demonstrate why AdaBoost predicts distorted probabilities and examine three cali…

Extends boosting to multiclass online agnostic classification.

Boosting is one of the most significant developments in machine learning. This paper studies the rate of convergence of $L_2$Boosting, which is tailored for regression, in a high-dimensional setting. Moreover, we introduce so-called \textquotedblleft post-Boosting\textquotedblright. This is a post-selection estimator w…

Robust boosting improves regression accuracy in noisy data.

We present a new boosting algorithm, motivated by the large margins theory for boosting. We give experimental evidence that the new algorithm is significantly more robust against label noise than existing boosting algorithm.

Improved agnostic boosting with better sample efficiency.

Excellent ranking power along with well calibrated probability estimates are needed in many classification tasks. In this paper, we introduce a technique, Calibrated Boosting-Forest that captures both. This novel technique is an ensemble of gradient boosting machines that can support both continuous and binary labels. …

Boosting algorithms are frequently used in applied data science and in research. To date, the distinction between boosting with either gradient descent or second-order Newton updates is often not made in both applied and methodological research, and it is thus implicitly assumed that the difference is irrelevant. The g…

The study provides statistical guarantees for Bayesian variational boosting.

In this paper we propose using the principle of boosting to reduce the bias of a random forest prediction in the regression setting. From the original random forest fit we extract the residuals and then fit another random forest to these residuals. We call the sum of these two random forests a \textit{one-step boosted …

New lower bounds nearly match existing upper bounds for boosted classifiers.

TF Boosted Trees (TFBT) is a new open-sourced frame-work for the distributed training of gradient boosted trees. It is based on TensorFlow, and its distinguishing features include a novel architecture, automatic loss differentiation, layer-by-layer boosting that results in smaller ensembles and faster prediction, princ…

SGLB boosts machine learning with Langevin diffusion for multimodal loss functions.

Boosting combines weak (biased) learners to obtain effective learning algorithms for classification and prediction. In this paper, we show a connection between boosting and kernel-based methods, highlighting both theoretical and practical applications. In the context of $\ell_2$ boosting, we start with a weak linear le…

New algorithm improves convergence of gradient boosting trees.

Gradient boosting is a prediction method that iteratively combines weak learners to produce a complex and accurate model. From an optimization point of view, the learning procedure of gradient boosting mimics a gradient descent on a functional variable. This paper proposes to build upon the proximal point algorithm, wh…

Proposes a boosting framework for sparsity in grouped covariates.

In this paper, we introduce a new machine learning (ML) model for nonlinear regression called the Boosted Smooth Transition Regression Trees (BooST), which is a combination of boosting algorithms with smooth transition regression trees. The main advantage of the BooST model is the estimation of the derivatives (partial…

FGBoost boosts gradient boosting for complex data.

Boosts weak online learners to strong ones with sublinear regret.

Boosting improves trend detection in financial data.