arXiv research

Bayesian optimisation algorithm for unknown search spaces with sub-linear regret.

New method speeds up diffusion models inference to sub-linear time.

Study finds exact limits for sparse regression with fewer observations than usual.

Kernel $ε$-Greedy optimizes multi-armed bandits with covariates for sub-linear regret.

In this paper, we study a class of online optimization problems with long-term budget constraints where the objective functions are not necessarily concave (nor convex) but they instead satisfy the Diminishing Returns (DR) property. Specifically, a sequence of monotone DR-submodular objective functions $\{f_t(x)\}_{t=1…

Optimal Liouville theorem for minimal disks in any codimension.

Paper tackles LDP bandits learning with improved results and sub-linear regret.

First robust bandit algorithm for contextual bandits with sub-linear regret.

In this paper, we introduce Ballooning Multi-Armed Bandits (BL-MAB), a novel extension of the classical stochastic MAB model. In the BL-MAB model, the set of available arms grows (or balloons) over time. In contrast to the classical MAB setting where the regret is computed with respect to the best arm overall, the regr…

New algorithm tackles adversarial corruption in Lipschitz bandits with sub-linear regret.

In this work, we study the problem of aggregating a finite number of predictors for nonstationary sub-linear processes. We provide oracle inequalities relying essentially on three ingredients: (1) a uniform bound of the $\ell^1$ norm of the time varying sub-linear coefficients, (2) a Lipschitz assumption on the predict…

In $\mathcal{X}$-armed bandit problem an agent sequentially interacts with environment which yields a reward based on the vector input the agent provides. The agent's goal is to maximise the sum of these rewards across some number of time steps. The problem and its variations have been a subject of numerous studies, su…

New algorithms reduce slate bandit regret for large slates, outperforming existing methods.

Deep Retrieval learns a retrievable structure for efficient large-scale recommendations.

In this paper, we consider the problem of preserving privacy in the online learning setting. We study the problem in the online convex programming (OCP) framework---a popular online learning setting with several interesting theoretical and practical implications---while using differential privacy as the formal privacy …

New algorithms for planning with adversarial changes in costs.

Paper tackles domain adaptation for contextual bandits with sub-linear regret.

Study of deep Stable neural networks with various activation functions.

Efficient algorithms for $k$-means clustering frequently converge to suboptimal partitions, and given a partition, it is difficult to detect $k$-means optimality. In this paper, we develop an a posteriori certifier of approximate optimality for $k$-means clustering. The certifier is a sub-linear Monte Carlo algorithm b…

COBRA addresses strategic behavior in online platforms by ensuring truthful reporting without monetary incentives.

Proposes a transductive matrix completion method with calibration for multi-task learning.

Safe Gaussian Process Bandit Optimization with sub-linear regret bounds.

We provide a novel -- and to the best of our knowledge, the first -- algorithm for high dimensional sparse regression with constant fraction of corruptions in explanatory and/or response variables. Our algorithm recovers the true sparse parameters with sub-linear sample complexity, in the presence of a constant fractio…

Proposes SPFB method for optimizing partition functions in stochastic learning.

New algorithm reduces risk in online games with limited feedback.

Upper bounds on ribbonlength of various knots, showing linear and sub-linear behavior.

A new algorithm improves stochastic linear bandit performance using residual bootstrap.

New algorithms for fair item allocation with limited copies.

The paper tackles minimax optimality in continuum contextual bandits with Hölder continuity.

This paper addresses robust CBs for linear SEMs with model fluctuations.

G-framework is presented by Peng [41] for measure risk under uncertainty. In this paper, we define fractional G-Brownian motion (fGBm). Fractional G-Brownian motion is a centered G-Gaussian process with zero mean and stationary increments in the sense of sub-linearity with Hurst index $H\in (0,1)$. This process has sta…

New NTK bounds show deep networks with minimum over-parameterization can still memorize and optimize.

New BO method optimizes functions efficiently even with unknown hyperparameters.

Two models predict similar high-frequency price dynamics but differ in low-frequency impact strength.

Paper tackles online DR-submodular maximization with stochastic constraints.

Study designs steering rewards for MFGs with unknown dynamics and model uncertainty.

Algorithm learns optimal arm selection in unsupervised sequential selection with contextual information.

A new algorithm for restless bandits handles long-range dependencies.

Revisits elastic string model to explain interest rate correlations.

LNUCB-TA improves MAB performance by dynamically adjusting exploration rates and recognizing spatiotemporal patterns.

We propose algorithms for online principal component analysis (PCA) and variance minimization for adaptive settings. Previous literature has focused on upper bounding the static adversarial regret, whose comparator is the optimal fixed action in hindsight. However, static regret is not an appropriate metric when the un…

Develops algorithms for CCBs with non-linear costs, improving safety and performance.

A multi-user multi-armed bandit (MAB) framework is used to develop algorithms for uncoordinated spectrum access. The number of users is assumed to be unknown to each user. A stochastic setting is first considered, where the rewards on a channel are the same for each user. In contrast to prior work, it is assumed that t…

Constrained Markov Decision Processes are a class of stochastic decision problems in which the decision maker must select a policy that satisfies auxiliary cost constraints. This paper extends upper confidence reinforcement learning for settings in which the reward function and the constraints, described by cost functi…

Robustness to distributional shift is one of the key challenges of contemporary machine learning. Attaining such robustness is the goal of distributionally robust optimization, which seeks a solution to an optimization problem that is worst-case robust under a specified distributional shift of an uncontrolled covariate…

Policy gradient methods achieve linear convergence in simple MDPs.

For a dataset of label-count pairs, an anonymized histogram is the multiset of counts. Anonymized histograms appear in various potentially sensitive contexts such as password-frequency lists, degree distribution in social networks, and estimation of symmetric properties of discrete distributions. Motivated by these app…

In some reinforcement learning problems an agent may be provided with a set of input policies, perhaps learned from prior experience or provided by advisors. We present a reinforcement learning with policy advice (RLPA) algorithm which leverages this input set and learns to use the best policy in the set for the reinfo…