A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
This paper tackles multilingual speech processing by optimizing conflicting objectives hierarchically.
A new Adamize method improves multi-objective recommender systems.
New method robustly discovers causal relationships from imperfect data.
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…
Convolutional Neural Networks (CNNs) are commonly thought to recognise objects by learning increasingly complex representations of object shapes. Some recent studies suggest a more important role of image textures. We here put these conflicting hypotheses to a quantitative test by evaluating CNNs and human observers on…
Pessimistic estimator improves multi-objective policy optimization.
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.
Bidders in day-ahead electricity markets want to sell/buy electricity when their bids generate positive surplus and not to take an action when the reverse holds. However, non-convexities in these markets cause conflicts between the actions that the bidders want to take and the actual market results. In this work, we in…
Multi-objective optimization aims at finding trade-off solutions to conflicting objectives. These constitute the Pareto optimal set. In the context of expensive-to-evaluate functions, it is impossible and often non-informative to look for the entire set. As an end-user would typically prefer a certain part of the objec…
LLMs show biases in investment analysis, leading to unreliable recommendations.
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.
Proposes a new framework for uncertainty-aware LLM post-training.
Russia-Ukraine conflict impacts global agricultural futures and spot markets' extreme risks.
New method optimizes multiple objectives using particle dynamics and gradient flow.
The use of machine learning (ML) is on the rise in many sectors of software development, and automotive software development is no different. In particular, Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS) are two areas where ML plays a significant role. In automotive development, safety is…
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…
MOL-TS uses Thompson Sampling for multi-objective linear bandits with Pareto guarantees.
Many real-world applications are characterized by a number of conflicting performance measures. As optimizing in a multi-objective setting leads to a set of non-dominated solutions, a preference function is required for selecting the solution with the appropriate trade-off between the objectives. The question is: how g…
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…
Many real-world decision problems are characterized by multiple conflicting objectives which must be balanced based on their relative importance. In the dynamic weights setting the relative importance changes over time and specialized algorithms that deal with such change, such as a tabular Reinforcement Learning (RL) …
Paper proposes privacy-preserving learning for images, making them imperceptible to humans but recognizable by machines.
GRAIN: Group Aggregation via Min-Norm Objective
Stochastic algorithm achieves sublinear convergence for bi-objective optimization.
Paper proposes active learning for mining conflict dynamics from textual data.
Proposes MOGFNs for generating diverse Pareto optimal solutions in multi-objective optimization.
A novel multi-objective optimization framework improves insurance pricing fairness.
NN-EVCLUS uses neural networks to cluster data with uncertainty.
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…
Paper proposes PSIPS for identifying Pareto set with correlated objectives.
PBO framework optimizes latent preferences over multiple objectives.
Autonomous agents must often deal with conflicting requirements, such as completing tasks using the least amount of time/energy, learning multiple tasks, or dealing with multiple opponents. In the context of reinforcement learning~(RL), these problems are addressed by (i)~designing a reward function that simultaneously…
Enhances conformal prediction for better uncertainty estimates in armed conflict fatalities.
This paper surveys gradient-based multi-objective deep learning methods.
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.
Multi-objective Neural Architecture Search (NAS) aims to discover novel architectures in the presence of multiple conflicting objectives. Despite recent progress, the problem of approximating the full Pareto front accurately and efficiently remains challenging. In this work, we explore the novel reinforcement learning …
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…
Real-world problems typically require the simultaneous optimization of several, often conflicting objectives. Many of these multi-objective optimization problems are characterized by wide ranges of uncertainties in their decision variables or objective functions, which further increases the complexity of optimization. …
Paper studies competitive networks where teams aim to minimize their own objectives, adapting to each other's strategies.
In multi-task learning, multiple tasks are solved jointly, sharing inductive bias between them. Multi-task learning is inherently a multi-objective problem because different tasks may conflict, necessitating a trade-off. A common compromise is to optimize a proxy objective that minimizes a weighted linear combination o…
PAIR optimizes machine learning models to generalize better to out-of-distribution data.
Study examines grain futures connectedness during Russia-Ukraine conflict.