arXiv research

Study sets a nontrivial upper limit on return forecasting accuracy.

What are the prices of random variables? In this paper, we define the least-squares prices of coin-flipping games, which are proved to be minimal, positive linear, and arbitrage-free. These prices depend both on a set of games that are available for investing simultaneously and on a risk-free interest rate. In addition…

This paper explores using nonlinear control for robust logarithmic growth in coin flipping games.

EagleEye detects localized density anomalies in multivariate data.

Just as semantic hashing can accelerate information retrieval, binary valued embeddings can significantly reduce latency in the retrieval of graphical data. We introduce a simple but effective model for learning such binary vectors for nodes in a graph. By imagining the embeddings as independent coin flips of varying b…

In this article, I will present a paradox whose purpose is to draw your attention to an important topic in finance, concerning the non-independence of the financial returns (non-ergodic hypothesis). In this paradox, we have two people sitting at a table separated by a black sheet so that they cannot see each other and …

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

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

A new watermarking method corrects bias in language models using maximal coupling.

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%.

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 …

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.

The stochastic variance-reduced gradient method (SVRG) and its accelerated variant (Katyusha) have attracted enormous attention in the machine learning community in the last few years due to their superior theoretical properties and empirical behaviour on training supervised machine learning models via the empirical ri…

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.

We introduce biased gradient oracles to capture a setting where the function measurements have an estimation error that can be controlled through a batch size parameter. Our proposed oracles are appealing in several practical contexts, for instance, risk measure estimation from a batch of independent and identically di…

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.

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.

SoQal uses selective oracle questioning to improve active learning of cardiac signals.

RPI combines imitation and reinforcement learning to improve policies efficiently.

New algorithms adapt to friendly environments in online learning.

We develop theory for using heuristics to solve computationally hard problems in differential privacy. Heuristic approaches have enjoyed tremendous success in machine learning, for which performance can be empirically evaluated. However, privacy guarantees cannot be evaluated empirically, and must be proven --- without…