arXiv research

ARO overfits by making constraints dependent on uncertainty, leading to brittleness.

Adapts Bartnik method to Hilbert manifold structure for vacuum constraint equations.

New approach tackles resource constraints in bandit problems with weakly adaptive algorithms.

Adaptive allocation with constraints using Thompson sampling.

Fast algorithms developed for adaptive and fully adaptive submodular maximization problems.

Adaptive algorithm for multi-objective optimization with binary constraints.

We study distributed estimation methods under communication constraints in a distributed version of the nonparametric random design regression model. We derive minimax lower bounds and exhibit methods that attain those bounds. Moreover, we show that adaptive estimation is possible in this setting.

Study compares adaptive vs fixed query learning methods.

Paper tackles SMPC for linear systems with unknown noise distribution.

Study on adaptivity constraints in linear contextual bandits with optimal design.

New algorithm maximizes non-monotone adaptive submodular functions in linear time.

New constraints on space and adaptivity in bandits force more batches and memory use.

Paper tackles nonparametric classification with privacy constraints, achieving optimal accuracy.

Distributed sensors compress and send features to a fusion center for linear regression.

Study on distributed nonparametric function estimation with optimal rate and cost of adaptation.

This work proposes ACTC for adaptive distributed learning under communication constraints.

Adapting Hedge algorithm for semi-adversarial data with root-entropy regularization.

Autonomous robots need to interact with unknown, unstructured and changing environments, constantly facing novel challenges. Therefore, continuous online adaptation for lifelong-learning and the need of sample-efficient mechanisms to adapt to changes in the environment, the constraints, the tasks, or the robot itself a…

Efficient RL algorithms for linear function approximation with limited adaptivity constraints.

Paper studies federated nonparametric testing with privacy constraints, achieving optimal rates and adaptive testing.

The paper introduces GAER to assess market feasibility under geopolitical and institutional constraints.

The paper improves Gaussian processes by adding sum constraints, enhancing prediction accuracy.

We present an adaptive online gradient descent algorithm to solve online convex optimization problems with long-term constraints , which are constraints that need to be satisfied when accumulated over a finite number of rounds T , but can be violated in intermediate rounds. For some user-defined trade-off parameter $β$…

In this paper, we formulate the Canonical Correlation Analysis (CCA) problem on matrix manifolds. This framework provides a natural way for dealing with matrix constraints and tools for building efficient algorithms even in an adaptive setting. Finally, an adaptive CCA algorithm is proposed and applied to a change dete…

Unsupervised domain adaptation studies the problem of utilizing a relevant source domain with abundant labels to build predictive modeling for an unannotated target domain. Recent work observe that the popular adversarial approach of learning domain-invariant features is insufficient to achieve desirable target domain …

Paper analyzes adaptive Lasso for high-dimensional diffusion processes, improving support recovery and bias.

Proves generic nondegeneracy for solutions under volume constraint in closed manifolds.

Super-resolution is a classical problem in image processing, with numerous applications to remote sensing image enhancement. Here, we address the super-resolution of irregularly-sampled remote sensing images. Using an optimal interpolation as the low-resolution reconstruction, we explore locally-adapted multimodal conv…

The paper tackles sparse graph learning under Laplacian-related constraints, improving upon existing methods.

Investigates safe decision-making in interactive environments.

Method adapts frozen models for few-shot tasks without training.

We consider the problem of sequential sampling from a finite number of independent statistical populations to maximize the expected infinite horizon average outcome per period, under a constraint that the expected average sampling cost does not exceed an upper bound. The outcome distributions are not known. We construc…

LinearAPT optimizes decision-making under resource constraints for a linear threshold problem.

HATCH learns optimal recommendations with resource constraints.

Semi-supervised clustering methods incorporate a limited amount of supervision into the clustering process. Typically, this supervision is provided by the user in the form of pairwise constraints. Existing methods use such constraints in one of the following ways: they adapt their clustering procedure, their similarity…

In E-commerce advertising, where product recommendations and product ads are presented to users simultaneously, the traditional setting is to display ads at fixed positions. However, under such a setting, the advertising system loses the flexibility to control the number and positions of ads, resulting in sub-optimal p…

This paper proposes an Adaptive Stochastic Model Predictive Control (MPC) strategy for stable linear time-invariant systems in the presence of bounded disturbances. We consider multi-input, multi-output systems that can be expressed by a Finite Impulse Response (FIR) model. The parameters of the FIR model corresponding…

Physics-informed neural networks improve by measuring effective dimensionality of constraints.

A new algorithm tackles submodular bandit problems with multiple constraints.

Paper optimizes industrial refrigeration using adaptive exploration.

Adaptive Monte Carlo schemes developed over the last years usually seek to ensure ergodicity of the sampling process in line with MCMC tradition. This poses constraints on what is possible in terms of adaptation. In the general case ergodicity can only be guaranteed if adaptation is diminished at a certain rate. Import…

New algorithms improve online prediction from experts with privacy constraints.

FedCONST adapts update magnitudes to enhance feature generalization in FL.

Proposes a new algorithm for solving optimization problems with stochastic objectives and equality constraints.

Private algorithms adapt from public to private domains with minimal labeled data.

As convolutional neural networks (CNNs) enable state-of-the-art computer vision applications, their high energy consumption has emerged as a key impediment to their deployment on embedded and mobile devices. Towards efficient image classification under hardware constraints, prior work has proposed adaptive CNNs, i.e., …

Recent work in distance metric learning has focused on learning transformations of data that best align with provided sets of pairwise similarity and dissimilarity constraints. The learned transformations lead to improved retrieval, classification, and clustering algorithms due to the better adapted distance or similar…

Selective state-adaptive regularization improves offline RL performance.