arXiv research

Enhances conformal prediction for better uncertainty estimates in armed conflict fatalities.

Paper proposes active learning for mining conflict dynamics from textual data.

We consider two multi-armed bandit problems with $n$ arms: (i) given an $ε> 0$, identify an arm with mean that is within $ε$ of the largest mean and (ii) given a threshold $μ_0$ and integer $k$, identify $k$ arms with means larger than $μ_0$. Existing lower bounds and algorithms for the PAC framework suggest that both …

Algorithm minimizes regret in multi-criteria bandits with constraints.

In this paper we propose the multi-objective contextual bandit problem with similarity information. This problem extends the classical contextual bandit problem with similarity information by introducing multiple and possibly conflicting objectives. Since the best arm in each objective can be different given the contex…

Armed conflict has led to an unprecedented number of internally displaced persons (IDPs) - individuals who are forced out of their homes but remain within their country. IDPs often urgently require shelter, food, and healthcare, yet prediction of when large fluxes of IDPs will cross into an area remains a major challen…

Novel approach uses Gaussian processes to estimate conflict trends.

The deterrent effect of military alliances is well documented and widely accepted. However, such work has typically assumed that alliances are exogenous. This is problematic as alliances may simultaneously influence the probability of conflict and be influenced by the probability of conflict. Failing to account for suc…

In all types of disasters, from earthquakes to armed conflicts, aid workers need accurate and timely data such as damage to buildings and population displacement to mount an effective response. Remote sensing provides this data at an unprecedented scale, but extracting operationalizable information from satellite image…

The paper shows conflict graphs of Petersen family graphs are mostly unbalanced.

Unified approach detects traffic conflicts across various interactions.

The study extends Tutte's conflict graph concept to nonplanar graphs.

A distinction has been drawn in fair machine learning research between `group' and `individual' fairness measures. Many technical research papers assume that both are important, but conflicting, and propose ways to minimise the trade-offs between these measures. This paper argues that this apparent conflict is based on…

Signed Evidence Flow (SEF) combines fitted prediction with signed feature attributions to measure evidence conflict and stability.

Russia-Ukraine conflict impacts global agricultural futures and spot markets' extreme risks.

The paper uses conformal prediction for forecasting conflict sequences in Markov processes.

Automatic conflict detection has grown in relevance with the advent of body-worn technology, but existing metrics such as turn-taking and overlap are poor indicators of conflict in police-public interactions. Moreover, standard techniques to compute them fall short when applied to such diversified and noisy contexts. W…

In this paper, we propose a test, called Flagged-1-Bit (F1B) test, to study the intrinsic capability of recurrent neural networks in sequence learning. Four different recurrent network models are studied both analytically and experimentally using this test. Our results suggest that in general there exists a conflict be…

This paper tackles multilingual speech processing by optimizing conflicting objectives hierarchically.

MOL-TS uses Thompson Sampling for multi-objective linear bandits with Pareto guarantees.

We first pursue the study of how hierarchy provides a well-adapted tool for the analysis of change. Then, using a time sequence-constrained hierarchical clustering, we develop the practical aspects of a new approach to wavelet regression. This provides a new way to link hierarchical relationships in a multivariate time…

We investigate the structure of global inter-firm linkages using a dataset that contains information on business partners for about 400,000 firms worldwide, including all the firms listed on the major stock exchanges. Among the firms, we examine three networks, which are based on customer-supplier, licensee-licensor, a…

Prediction problems often admit competing models that perform almost equally well. This effect challenges key assumptions in machine learning when competing models assign conflicting predictions. In this paper, we define predictive multiplicity as the ability of a prediction problem to admit competing models with confl…

Hybrid model improves geopolitical conflict forecasting.

We present CYCLADES, a general framework for parallelizing stochastic optimization algorithms in a shared memory setting. CYCLADES is asynchronous during shared model updates, and requires no memory locking mechanisms, similar to HOGWILD!-type algorithms. Unlike HOGWILD!, CYCLADES introduces no conflicts during the par…

Study examines grain futures connectedness during Russia-Ukraine conflict.

WISCA generates consensus explanations from conflicting model-agnostic interpretability methods.

This article reviews entity resolution methods and their applications.

Conflict sets are loci of intersecting wavefronts emanating from $l$ different surfaces. We show that generically conflict sets are Legendrian: locally they admit the structure of wavefronts. Simple stable singularities for this problem in $\R^n$ occur when $0 \leq n-l \leq 4$. Other related sets, such as kite curves a…

Researchers develop a neural network that learns like humans, overcoming forgetting and structure issues.

The study analyzes implicit biases in neural networks using backward error analysis.

A 2-complex requires at least 12 colours to avoid edge conflicts.

The study analyzes the conflict between group fairness and individual fairness in machine learning.

Study analyzes crude oil futures markets using visibility graphs to understand their structure and dynamics.

Time inconsistency leads to intra-personal conflict and reconciliation strategies.

We study a bad arm existing checking problem in which a player's task is to judge whether a positive arm exists or not among given K arms by drawing as small number of arms as possible. Here, an arm is positive if its expected loss suffered by drawing the arm is at least a given threshold. This problem is a formalizati…

New algorithms for streaming bandits with limited memory.

Study best arm identification in restless Markov multi-armed bandits with state-dependent transitions.

Paper tackles identifying an odd arm in a multi-armed bandit with restless Markov processes and trembling hand.

Improved theoretical guarantees for Top Two algorithms.

We address the M-best-arm identification problem in multi-armed bandits. A player has a limited budget to explore K arms (M<K), and once pulled, each arm yields a reward drawn (independently) from a fixed, unknown distribution. The goal is to find the top M arms in the sense of expected reward. We develop an algorithm …

Sequential screening and dynamic regret in multi-armed bandits with arriving arms

Introduces Rashomon Capacity to measure predictive multiplicity in probabilistic classifiers.

MAMBA learns policies competitive with multiple conflicting oracles.

Paper tackles good arm identification in stochastic bandits.

We consider the best-arm identification problem in multi-armed bandits, which focuses purely on exploration. A player is given a fixed budget to explore a finite set of arms, and the rewards of each arm are drawn independently from a fixed, unknown distribution. The player aims to identify the arm with the largest expe…

A novel algorithm reduces communication costs in federated best arm identification.

New algorithms identify best arm with less pulls, adapting to arm covariances.