arXiv research

Unified theory for UCB policies in total and max bandit problems.

Two novel procedures track quantiles efficiently using an oracle.

New methods solve optimization problems with heavy-tailed noise, improving upon existing complexity bounds.

New algorithms optimize actions under time-varying constraints without projecting.

New algorithm reduces contextual bandit identification to argmax calls.

The Lasso is a computationally efficient regression regularization procedure that can produce sparse estimators when the number of predictors (p) is large. Oracle inequalities provide probability loss bounds for the Lasso estimator at a deterministic choice of the regularization parameter. These bounds tend to zero if …

Novel method to quantify aleatoric uncertainty of treatment effects from observational data.

Prior knowledge on properties of a target model often come as discrete or combinatorial descriptions. This work provides a unified computational framework for defining norms that promote such structures. More specifically, we develop associated tools for optimization involving such norms given only the orthogonal proje…

New method improves solving combinatorial optimization problems with smoothed policies.

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

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

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…

Develops NPMC method for noisy labels, improving multiclass classification accuracy.

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%.

New algorithm tackles complex optimization problems with inexact and stochastic methods.

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.

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.

Panoptic trades options without oracles on Ethereum.

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…

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 …

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.

New RLHF framework handles general preference oracles without reward functions.

Study on gradient complexity of private optimization with private oracles.

Evolutions of the trading landscape lead to the capability to exchange the same financial instrument on different venues. Because of liquidity issues, the trading firms split large orders across several trading destinations to optimize their execution. To solve this problem we devised two stochastic recursive learning …

Study on costs of manipulating AMM-based price oracles.

CJE calibrates cheap LLM judges against an oracle, achieving high accuracy at a fraction of the cost.

Oracle inequality for sparse neural nets adapts to unknown structure.

We give nearly matching upper and lower bounds on the oracle complexity of finding $ε$-stationary points ($\| \nabla F(x) \| \leqε$) in stochastic convex optimization. We jointly analyze the oracle complexity in both the local stochastic oracle model and the global oracle (or, statistical learning) model. This allows u…

Oracle-efficient algorithms for online learning with smoothed and hint-adversaries.