arXiv research

Proposes a curriculum learning algorithm to maximize cumulative return in reinforcement learning.

NAK model predicts student grades by focusing on relevant past courses.

Method determines credit transition matrix from cumulative default probabilities.

Lifelong learning can be viewed as a continuous transfer learning procedure over consecutive tasks, where learning a given task depends on accumulated knowledge --- the so-called knowledge base. Most published work on lifelong learning makes a batch processing of each task, implying that a data collection step is requi…

New algorithm minimizes cumulative loss in dynamic linear bandits without prior knowledge of comparator switches.

New algorithms reduce regret in bandit problems with causal information.

New algorithms minimize regret in both adversarial and stochastic contexts.

GACBO optimizes unknown causal graphs with interventions.

Boost-R uses gradient boosted trees for analyzing recurrence data.

New method reduces dynamic regret for non-stationary bandits.

TVBO optimizes time-varying functions with asymptotically vanishing regret.

Over the past half-century, the empirical finance community has produced vast literature on the advantages of the equally weighted S\&P 500 portfolio as well as the often overlooked disadvantages of the market capitalization weighted Standard and Poor's (S\&P 500) portfolio (see \cite{Bloom}, \cite{Uppal}, \cite{Jacobs…

The Multi-Armed Bandits (MAB) framework highlights the tension between acquiring new knowledge (Exploration) and leveraging available knowledge (Exploitation). In the classical MAB problem, a decision maker must choose an arm at each time step, upon which she receives a reward. The decision maker's objective is to maxi…

We present an adaptive online gradient descent algorithm to solve online convex optimization problems with long-term constraints , which are constraints that need to be satisfied when accumulated over a finite number of rounds T , but can be violated in intermediate rounds. For some user-defined trade-off parameter $β$…

Deep Reinforcement Learning (DRL) has achieved impressive success in many applications. A key component of many DRL models is a neural network representing a Q function, to estimate the expected cumulative reward following a state-action pair. The Q function neural network contains a lot of implicit knowledge about the…

A new UU-test decides unimodality of datasets.

Study quantifies how LLMs capture higher-order statistical structure using cumulant expansion.

New algorithm for identifying optimal arms in stochastic bandit problems.

The CSA-ES is an Evolution Strategy with Cumulative Step size Adaptation, where the step size is adapted measuring the length of a so-called cumulative path. The cumulative path is a combination of the previous steps realized by the algorithm, where the importance of each step decreases with time. This article studies …

A new scoring function improves ordinal regression by combining binary predictions.

Using methods introduced by Scargle in 1978 we derive a cumulative version of the Lomb periodogram that exhibits frequency independent statistics when applied to cumulative noise. We show how this cumulative Lomb periodogram allows us to estimate the significance of log-periodic signatures in the S&P 500 anti-bubble th…

In this paper, we propose an effective THresholding method based on ORder Statistic, called THORS, to convert an arbitrary scoring-type classifier, which can induce a continuous cumulative distribution function of the score, into a cost-sensitive one. The procedure, uses order statistic to find an optimal threshold for…

Proposes new models to predict student grades more accurately.

Kernelized cumulants improve statistical analysis in high-dimensional spaces.

Neural networks can learn from higher-order cumulants efficiently, requiring quadratic samples.

A new GAN loss function based on cumulant generating functions improves stability and robustness.

The paper calculates bounds for risk metrics and entropies under partial information constraints.

A novel bandit problem with delayed arms, showing optimal strategies and lower bounds.

UCRL-CMDP algorithm optimizes RL with constraints on average costs.

Bayesian methods improve inference for cumulative probit models on large datasets.

The problem of an arbitrary truncated Levy flight description using the method of cumulant approach has been solved. The set of cumulants of the truncated Levy distribution given the assumption of arbitrary truncation has been found. The influence of truncation shape on the truncated Levy flight properties in the Gauss…

Learning from prior tasks and transferring that experience to improve future performance is critical for building lifelong learning agents. Although results in supervised and reinforcement learning show that transfer may significantly improve the learning performance, most of the literature on transfer is focused on ba…

CENNSurv models cumulative effects of time-dependent exposures on survival outcomes.

Paper tackles robust MDPs with sample complexity guarantees.

New algorithms minimize simple and cumulative regret in contextual bandits.

New method calibrates classifier probabilities with guaranteed coverage.

Paper converts quantiles to cumulative distribution functions to simplify risk measures.

We introduce a new type of graphical model called a "cumulative distribution network" (CDN), which expresses a joint cumulative distribution as a product of local functions. Each local function can be viewed as providing evidence about possible orderings, or rankings, of variables. Interestingly, we find that the condi…

Improved Thompson Sampling for high-dimensional sparse bandits.

Develops regression trees for estimating cumulative incidence curves in competing risks.

IPO optimizes reinforcement learning with constraints for better performance.

A new model relaxes constraints on exponential dispersion models.

Paper proposes a new method to identify causal graphs with latent variables using higher-order cumulants.

The paper identifies causal effects in latent variable models using higher-order cumulants.

Adapts Exp3 to adversarial bandits with delays and data.

We show power-scaling behaviors for fluctuations in share volume, which no other studies have so far done. After analyzing a database of the daily transactions for all securities listed on the Tokyo Stock Exchange, we selected 1050 large companies that each had an unbroken series of daily trading activity from January …

Proposes a new sampling method for online learning with cumulative oversampling.

The paper tackles non-cumulative objectives in reinforcement learning and proposes modifications to existing algorithms.