arXiv research

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

New guarantees for adaptive combinatorial maximization with various objectives.

Efficiently selects seed nodes to maximize content influence in unknown social networks.

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

Adaptive cascade submodular maximization tackles sequential selection under uncertainty.

Entropy minimization has been widely used in unsupervised domain adaptation (UDA). However, existing works reveal that entropy minimization only may result into collapsed trivial solutions. In this paper, we propose to avoid trivial solutions by further introducing diversity maximization. In order to achieve the possib…

A characterization of maximal domains of existence of adapted complex structures for Riemannian homogeneous manifolds under certain extensibility assumptions on their geodesic flow is given. This is applied to generalized Heisenberg groups and naturally reductive Riemannian homogeneous spaces. As an application it is s…

This paper improves test-time adaptation for distribution shifts using confidence maximization and input transformation.

Adaptive learning method identifies and corrects corrupted data.

MaxVA improves Adam's step sizes by maximizing gradient variance.

The paper tackles adaptive policy selection to maximize social welfare, achieving optimal regret bounds.

ASD algorithm maximizes model estimates by adaptively labeling points.

We address the problem of maximizing an unknown submodular function that can only be accessed via noisy evaluations. Our work is motivated by the task of summarizing content, e.g., image collections, by leveraging users' feedback in form of clicks or ratings. For summarization tasks with the goal of maximizing coverage…

A new clustering algorithm fuses heat diffusion and turning angle for robustness.

Adaptive HMC improves sampling efficiency by optimizing mass matrix.

Work maximization guides machine learning models in adaptive systems.

Adaptive sequential decision making is one of the central challenges in machine learning and artificial intelligence. In such problems, the goal is to design an interactive policy that plans for an action to take, from a finite set of $n$ actions, given some partial observations. It has been shown that in many applicat…

New approach to multi-armed bandit problem aims to maximize highest total reward.

InfoOT improves data alignment by maximizing mutual information.

New method learns adaptive exploration strategies for dynamic tasks.

We consider four-dimensional vacuum spacetimes which admit a nonvanishing spacelike Killing field. The quotient with respect to the Killing action is a three-dimensional quotient spacetime $(M,g)$. We establish several results regarding maximal hypersurfaces (spacelike hypersurfaces of zero mean curvature) in such quot…

We propose a new concept named adaptive submodularity ratio to study the greedy policy for sequential decision making. While the greedy policy is known to perform well for a wide variety of adaptive stochastic optimization problems in practice, its theoretical properties have been analyzed only for a limited class of p…

The paper examines how sampling data affects the performance of submodular maximization.

In this paper, we investigate complete curvature-adapted submanifolds with maximal flat section and trivial normal holonomy group in symmetric spaces of compact type or non-compact type under certain condition, and derive the constancy of the principal curvatures of such submanifolds. As its result, we can derive that …

We present an adaptive approach to the construction of Gaussian process surrogates for Bayesian inference with expensive-to-evaluate forward models. Our method relies on the fully Bayesian approach to training Gaussian process models and utilizes the expected improvement idea from Bayesian global optimization. We adapt…

EVA adapts LoRA for faster, more efficient fine-tuning.

This paper addresses classification tasks on a particular target domain in which labeled training data are only available from source domains different from (but related to) the target. Two closely related frameworks, domain adaptation and domain generalization, are concerned with such tasks, where the only difference …

We propose and address a novel few-shot RL problem, where a task is characterized by a subtask graph which describes a set of subtasks and their dependencies that are unknown to the agent. The agent needs to quickly adapt to the task over few episodes during adaptation phase to maximize the return in the test phase. In…

There is an increasing concern that most current published research findings are false. The main cause seems to lie in the fundamental disconnection between theory and practice in data analysis. While the former typically relies on statistical independence, the latter is an inherently adaptive process: new hypotheses a…

Gradient-based meta-learning has proven to be highly effective at learning model initializations, representations, and update rules that allow fast adaptation from a few samples. The core idea behind these approaches is to use fast adaptation and generalization -- two second-order metrics -- as training signals on a me…

We show that a large class of Estimation of Distribution Algorithms, including, but not limited to, Covariance Matrix Adaption, can be written as a Monte Carlo Expectation-Maximization algorithm, and as exact EM in the limit of infinite samples. Because EM sits on a rigorous statistical foundation and has been thorough…

This paper presents a solution for persistent monitoring of real-world stochastic phenomena, where the underlying covariance structure changes sharply across time, using a small number of mobile robot sensors. We propose an adaptive solution for the problem where stochastic real-world dynamics are modeled as a Gaussian…

Maximal representations are studied using tree embeddings and geodesic currents.

New accelerators for EM improve convergence speed in complex mixture models.

Improved adaptive rates for Lipschitz bandit problem.

The paper extends physics-based information maximization to complex bandit problems.

In this paper we describe a new algorithm called Fast Adaptive Sequencing Technique (FAST) for maximizing a monotone submodular function under a cardinality constraint $k$ whose approximation ratio is arbitrarily close to $1-1/e$, is $O(\log(n) \log^2(\log k))$ adaptive, and uses a total of $O(n \log\log(k))$ queries. …

Optimizes group testing for COVID-19 to reduce test numbers.

Domain adaptation (DA) is the task of classifying an unlabeled dataset (target) using a labeled dataset (source) from a related domain. The majority of successful DA methods try to directly match the distributions of the source and target data by transforming the feature space. Despite their success, state of the art m…

QATS efficiently decodes HMMs with polylogarithmic complexity.

LARGE adapts regularization for better graph estimation in high-dimensional data.

JADAI optimizes design and inference for parameter estimation.

Paper tackles cold-start domain adaptation with language descriptions.

Unsupervised domain adaptation aims to transfer and adapt knowledge learned from a labeled source domain to an unlabeled target domain. Key components of unsupervised domain adaptation include: (a) maximizing performance on the target, and (b) aligning the source and target domains. Traditionally, these tasks have eith…

Ad-SVGD optimizes kernel parameters for SVGD, improving inference performance.

New algorithm balances user reward and statistical inference by mixing TS with UR based on difference size.

We study adversarial robustness of neural networks from a margin maximization perspective, where margins are defined as the distances from inputs to a classifier's decision boundary. Our study shows that maximizing margins can be achieved by minimizing the adversarial loss on the decision boundary at the "shortest succ…

We introduce a balloon estimator in a generalized expectation-maximization method for estimating all parameters of a Gaussian mixture model given one data sample per mixture component. Instead of limiting explicitly the model size, this regularization strategy yields low-complexity sparse models where the number of eff…