arXiv research

The paper improves count data regression models for overdispersed data.

Enhances UPSA to reduce noise in financial data.

Oracle inequalities and variable selection properties for the Lasso in linear models have been established under a variety of different assumptions on the design matrix. We show in this paper how the different conditions and concepts relate to each other. The restricted eigenvalue condition (Bickel et al., 2009) or the…

Non-linear shrinkage isn't optimal for portfolio optimization, especially when asset dependence is non-stationary.

We investigate the computational complexity of several basic linear algebra primitives, including largest eigenvector computation and linear regression, in the computational model that allows access to the data via a matrix-vector product oracle. We show that for polynomial accuracy, $Θ(d)$ calls to the oracle are nece…

$Q$-learning with function approximation is one of the most popular methods in reinforcement learning. Though the idea of using function approximation was proposed at least 60 years ago, even in the simplest setup, i.e, approximating $Q$-functions with linear functions, it is still an open problem on how to design a pr…

Optimal CATE estimation with structured contrast functions using KRR.

Paper analyzes holdout cross-validation for large non-Gaussian covariance estimation.

Low-rank tensor regression, a new model class that learns high-order correlation from data, has recently received considerable attention. At the same time, Gaussian processes (GP) are well-studied machine learning models for structure learning. In this paper, we demonstrate interesting connections between the two, espe…

FSPA bypasses eigenvalue estimation for quantum PCA, achieving optimal complexity and robustness.

Oracle-efficient algorithms reduce combinatorial semi-bandit regret to logarithmic time.

New analysis shows Thompson Sampling can work with greedy approximations in combinatorial bandits.

Multi-view data have been routinely collected in various fields of science and engineering. A general problem is to study the predictive association between multivariate responses and multi-view predictor sets, all of which can be of high dimensionality. It is likely that only a few views are relevant to prediction, an…

New algorithms sample convex bodies using Markov chains and restricted Gaussian oracles.

MAMBA learns policies competitive with multiple conflicting oracles.

We study the problem of interactively learning a binary classifier using noisy labeling and pairwise comparison oracles, where the comparison oracle answers which one in the given two instances is more likely to be positive. Learning from such oracles has multiple applications where obtaining direct labels is harder bu…

New oracle uses uncertainty for active classification with noisy feedback.

Quantum oracles help identify counterfactuals better than classical ones.

SoQal reduces oracle label requests in active learning by up to 35%.

Universal algorithm learns unknown distribution for various decision-making problems.

Paper addresses online alignment of large language models under uncertain preference feedback.

We consider the problem of minimizing the sum of submodular set functions assuming minimization oracles of each summand function. Most existing approaches reformulate the problem as the convex minimization of the sum of the corresponding Lovász extensions and the squared Euclidean norm, leading to algorithms requiring …

New oracles improve stochastic optimization with noisy or biased measurements.

Algorithm solves online binary classification and infinite games using ERM oracle.

New algorithm learns efficiently with a simple 'yes/no' oracle.

The paper analyzes $\ell_q$ optimization methods for high-dimensional linear regression.

New study shows Gaussian samplers struggle with heavy-tailed targets, while stable samplers excel.

Average Oracle outperforms DCC+NLS in portfolio optimization.

New lower bounds for bilevel optimization with first-order oracles.

Study on tradeoffs between mistakes and ERM oracle calls in online and transductive learning.

In this paper, we propose to combine imitation and reinforcement learning via the idea of reward shaping using an oracle. We study the effectiveness of the near-optimal cost-to-go oracle on the planning horizon and demonstrate that the cost-to-go oracle shortens the learner's planning horizon as function of its accurac…

Panoptic trades options without oracles on Ethereum.

The paper tackles sequential mode estimation with oracle queries.

Semi-supervised active clustering (SSAC) utilizes the knowledge of a domain expert to cluster data points by interactively making pairwise "same-cluster" queries. However, it is impractical to ask human oracles to answer every pairwise query. In this paper, we study the influence of allowing "not-sure" answers from a w…

Three new oracle-efficient algorithms for private synthetic data release.

Unified formulation bridges adversarial and nonstationary bandits.

Folded concave penalization methods have been shown to enjoy the strong oracle property for high-dimensional sparse estimation. However, a folded concave penalization problem usually has multiple local solutions and the oracle property is established only for one of the unknown local solutions. A challenging fundamenta…

We consider online learning in an adversarial, non-convex setting under the assumption that the learner has an access to an offline optimization oracle. In the general setting of prediction with expert advice, Hazan et al. (2016) established that in the optimization-oracle model, online learning requires exponentially …

Study modular concept learning with different oracle interfaces.

Pairwise "same-cluster" queries are one of the most widely used forms of supervision in semi-supervised clustering. However, it is impractical to ask human oracles to answer every query correctly. In this paper, we study the influence of allowing "not-sure" answers from a weak oracle and propose an effective algorithm …

Study improves CTS's approximation regret for combinatorial bandits.

Improved Frank-Wolfe algorithm for constrained convex optimization with nearest extreme point oracle.

An algorithm learns from multiple models to match an oracle's risk.

Riemannian Proximal Sampler improves sampling on manifold data.

Given the observation of a high-dimensional Ornstein-Uhlenbeck (OU) process in continuous time, we proceed to the inference of the drift parameter under a row-sparsity assumption. Towards that aim, we consider the negative log-likelihood of the process, penalized by an $\ell^1$-penalization (Lasso and Adaptive Lasso). …

New RLHF framework handles general preference oracles without reward functions.

Study on gradient complexity of private optimization with private oracles.

Study on costs of manipulating AMM-based price oracles.