arXiv research

Study learns linear utility functions from comparisons, showing learnability gaps between passive and active learning.

Paper tackles clustering with ordinal comparisons, achieving near-optimal results.

We consider the problem of classification in a comparison-based setting: given a set of objects, we only have access to triplet comparisons of the form "object $x_i$ is closer to object $x_j$ than to object $x_k$." In this paper we introduce TripletBoost, a new method that can learn a classifier just from such triplet …

Paper introduces a new multi-kernel algorithm for better gradient approximation.

New RL method learns from passive data by modeling intentions.

Active learning can't improve over passive in certain settings.

This paper aims to incorporate passive symmetries in machine learning for better generalization.

This paper investigates the market impact of passive orders.

Optimal transfer learning for missing not-at-random matrix completion using source data.

In the world of big data, large but costly to label datasets dominate many fields. Active learning, a semi-supervised alternative to the standard PAC-learning model, was introduced to explore whether adaptive labeling could learn concepts with exponentially fewer labeled samples. While previous results show that active…

We study the role of active and passive investors in an investment market with uncertainties. Active investors concentrate on a single or a few stocks with a given probability of determining the quality of them. Passive investors spread their investment uniformly, resembling buying the market index. In this toy market …

Passive investing can incur hidden costs due to market timing inefficiencies.

We study the theoretical advantages of active learning over passive learning. Specifically, we prove that, in noise-free classifier learning for VC classes, any passive learning algorithm can be transformed into an active learning algorithm with asymptotically strictly superior label complexity for all nontrivial targe…

Model predicts active and passive cosponsorship motivations in Congress.

Active learning aims to obtain a classifier of high accuracy by using fewer label requests in comparison to passive learning by selecting effective queries. Many active learning methods have been developed in the past two decades, which sample queries based on informativeness or representativeness of unlabeled data poi…

Study shows big winner stocks significantly impact passive and active investment strategies.

JIT liquidity providers can sometimes reduce overall market liquidity by crowding out passive LPs.

Exchange Traded Funds (ETFs) have been gaining increasing popularity in the investment community as is evidenced by the high growth both in the number of ETFs and their net assets since 2000. As ETFs are in nature similar to index mutual funds, in this paper we examined if this growing demand for ETFs can be explained …

Finsler space is differentiable manifold for which Minkowski space is the fiber of the tangent bundle. To understand structure of the reference frame in Finsler space, we need to understand the structure of orthonormal basis in Minkowski space. In this paper, we considered the definition of orthonormal basis in Minkows…

In this paper we study a symmetry group of vector space. Basis manifold is a homogeneous space of a symmetry group. This concept leads us to the definition of active and passive transformations on basis manifold. Active transformation can be expressed as a transformation of vector space. Passive transformation gives ab…

Mean representations of VAEs are correlated but still useful for tasks.

Novel framework for efficient entity alignment labeling.

The Heston model optimizes portfolio management based on real market data.

We provide new results concerning label efficient, polynomial time, passive and active learning of linear separators. We prove that active learning provides an exponential improvement over PAC (passive) learning of homogeneous linear separators under nearly log-concave distributions. Building on this, we provide a comp…

In Passive POMDPs actions do not affect the world state, but still incur costs. When the agent is bounded by information-processing constraints, it can only keep an approximation of the belief. We present a variational principle for the problem of maintaining the information which is most useful for minimizing the cost…

Physics-informed learning framework for pH systems and EB-PBC control.

Paper introduces active and passive causal inference techniques.

Stabilizes online learning by using weighted reservoir sampling.

Actively sampled data can have very different characteristics than passively sampled data. Therefore, it's promising to investigate using different inference procedures during AL than are used during passive learning (PL). This general idea is explored in detail for the focused case of AL with cost-weighted SVMs for im…

This study proposes an equal-weight portfolio strategy to reduce risk compared to traditional ETFs.

Algometrics analyzes how predictive models affect their own forecasts in algorithmic markets.

FeDXL tackles federated learning for X-risk optimization.

Lin et al. (Reports, 7 September 2018, p. 1004) reported a remarkable proposal that employs a passive, strictly linear optical setup to perform pattern classifications. But interpreting the multilayer diffractive setup as a deep neural network and advocating it as an all-optical deep learning framework are not well jus…

There is a large body of work on convergence rates either in passive or active learning. Here we outline some of the results that have been obtained, more specifically in a nonparametric setting under assumptions about the smoothness and the margin noise. We also discuss the relative merits of these underlying assumpti…

New online learning algorithm combines PA and TER for binary classification.

Study stability of selective SSMs with discontinuous gating.

Optimal fees protect passive LPs in AMMs under varying market conditions.

Neural networks model COVID-19 spread with partial isolation data.

Automata learning techniques automatically generate system models from test observations. These techniques usually fall into two categories: passive and active. Passive learning uses a predetermined data set, e.g., system logs. In contrast, active learning actively queries the system under learning, which is considered…

Paper analyzes PSGLD for adaptive IRL with finite-sample bounds.

Recent studies using data on social media and stock markets have mainly focused on predicting stock returns. Instead of predicting stock price movements, we examine the relation between Facebook data and investors' decision making in stock markets with a unique data on investors' transactions on Nokia. We find that the…

Active data collection improves convergence rates in operator learning.

Deep learning uses WiFi CSI for reliable human presence detection.

Model A outperforms passive investment in stock index prediction with less exposure.

Improved k-NN active learning with local smoothness assumption.

In standard passive imitation learning, the goal is to learn a target policy by passively observing full execution trajectories of it. Unfortunately, generating such trajectories can require substantial expert effort and be impractical in some cases. In this paper, we consider active imitation learning with the goal of…

Batteryless or so called passive wearables are providing new and innovative methods for human activity recognition (HAR), especially in healthcare applications for older people. Passive sensors are low cost, lightweight, unobtrusive and desirably disposable; attractive attributes for healthcare applications in hospital…

LLMs add value in commodity portfolio construction when information set and implementation rules are held fixed.