The study examines how board diversity and CSR committee composition affect corporate governance and financial performance.
problem The relationship between corporate social responsibility (CSR) and corporate governance.
method Theoretical model development based on management and corporate governance theories, focusing on board diversity and CSR committee composition.
result Cognitive and demographic characteristics of board members provide more insights into the link between corporate governance and CSR.
Study improves model robustness in noisy datasets.
problem Instance-specific label noise in robust classification tasks.
method Coordinated Sparse Recovery (CSR) method introduces a collaboration matrix and confidence weights to reduce generalization error.
result CSR and CSR+ significantly reduce generalization error compared to existing methods.
In this paper we demonstrate end-to-end continuous speech recognition (CSR) using electroencephalography (EEG) signals with no speech signal as input. An attention model based automatic speech recognition (ASR) and connectionist temporal classification (CTC) based ASR systems were implemented for performing recognition…
Improves MRI-based brain surface reconstruction with minimal deformation energy loss.
problem Ensuring optimal deformation energy and consistency in learning-based cortical surface reconstruction.
method Design and implementation of a Minimal Energy Deformation (MED) loss in the V2C-Flow model.
result Significant improvements in training consistency and reproducibility without sacrificing reconstruction accuracy and topological correctness.
New voting strategies show committee-based consensus can scale efficiently.
problem Ensuring honest committees in committee-based consensus protocols.
method Empirical analysis of simpler voting strategies and their convergence to optimality.
result Simpler voting strategies converge to optimality exponentially quickly, ensuring robustness and efficiency.
Committee neural network models improve accuracy and enable active learning for interatomic potentials.
problem Improving accuracy and generalization error in interatomic potentials.
method Adapting committee models to neural networks, using multiple models with shared descriptors, and applying active learning to select configurations.
result Committee disagreement provides a measure of generalization error and guides active learning to minimize it.
Automatic continuous speech recognition (CSR) is sufficiently mature that a variety of real world applications are now possible including large vocabulary transcription and interactive spoken dialogues. This paper reviews the evolution of the statistical modelling techniques which underlie current-day systems, specific…
This research tackles image classification with noise, proposing committees of CNNs.
problem Image classification with concurrent feature and label noise.
method Committees of Convolutional Neural Networks (CNNs) for MNIST, CIFAR-10, and CIFAR-100 datasets.
result Committees outperform single models in noisy conditions, especially on difficult datasets.
This paper improves active learning by using robust divergences for committee disagreement.
problem Active learning with high measurement costs.
method Query by committee with Bregman divergence (including Kullback-Leibler divergence as a special case).
result The proposed method is more robust and performs as well as or better than conventional methods.
With the growth of renewable generation (RG) and the development of associated ride through curves serving as operating limits, during disturbances, on violation of these limits, the power system is at risk of losing large amounts of generation. In order to identify preventive control measures that avoid such scenarios…
In this work, we describe a framework that unifies many different interactive learning tasks. We present a generalization of the {\it query-by-committee} active learning algorithm for this setting, and we study its consistency and rate of convergence, both theoretically and empirically, with and without noise.
Active learning is a powerful approach to analyzing data effectively. We show that the feasibility of active learning depends crucially on the choice of measure with respect to which the query is being optimized. The standard information gain, for example, does not permit an accurate evaluation with a small committee, …
Active learning improves SR by proposing experiments in data-limited settings.
problem Efficiently gathering data for symbolic regression with physical constraints.
method Query by committee using the Pareto frontier of equations, with physical constraints.
result Reduces data required for SR and achieves state-of-the-art results.
Heuristic tools from statistical physics have been used in the past to locate the phase transitions and compute the optimal learning and generalization errors in the teacher-student scenario in multi-layer neural networks. In this contribution, we provide a rigorous justification of these approaches for a two-layers ne…
We derive generalized estimators for a number of spatial statistics that have been used in the analysis of spatially resolved omics data, such as Ripley's K, H and L functions, clustering index, and degree of clustering, which allow these statistics to be calculated on data modelled by arbitrary random measures (RMs). …
By mid 2004, the Basel Committee on Banking Supervision (BCBS) is epected to launch its final recommendations on minimum capital requirements in the banking industry. Although there is the intention to arrive at capital charges which concur with economic intuition, the risk weight formulas proposed by the committee wil…
In this paper we implement a Local Linear Regression Ensemble Committee (LOLREC) to predict 1-day-ahead returns of 453 assets form the S&P500. The estimates and the historical returns of the committees are used to compute the weights of the portfolio from the 453 stock. The proposed method outperforms benchmark portfol…
REALITrees uses a Rashomon ensemble approach for active learning in sparse decision trees.
problem Active learning reduces labeling costs by selecting informative samples, but current methods often sacrifice model diversity and direct characterization of the hypothesis space.
method REALITrees constructs a committee of all near-optimal sparse decision tree models using a Rashomon Set and a Gibbs posterior to weight them by empirical risk.
result REALITrees outperforms randomized ensembles, especially in noisy environments, by leveraging expanded model multiplicity.
This paper critiques the Standardized Measurement Approach (SMA) for operational risk and recommends maintaining Advanced Measurement Approach (AMA).
problem Weaknesses and failures of the Standardized Measurement Approach (SMA) in operational risk.
method Critical review and analysis of SMA and AMA approaches.
result SMA is unstable, insensitive to risk, and implicitly related to systemic risk in the banking sector.
Current Neural Architecture Search techniques can suffer from a few shortcomings, including high computational cost, excessive bias from the search space, conceptual complexity or uncertain empirical benefits over random search. In this paper, we present ImmuNeCS, an attempt at addressing these issues with a method tha…
This paper investigates and evaluates support vector machine active learning algorithms for use with imbalanced datasets, which commonly arise in many applications such as information extraction applications. Algorithms based on closest-to-hyperplane selection and query-by-committee selection are combined with methods …
In this paper we introduce various techniques to improve the performance of electroencephalography (EEG) features based continuous speech recognition (CSR) systems. A connectionist temporal classification (CTC) based automatic speech recognition (ASR) system was implemented for performing recognition. We introduce tech…
Proposes a new method for uncertainty estimation in neural networks.
problem Uncertainty quantification in neural networks for high-risk applications.
method Intuitive framework based on signal-to-noise ratio and variance-gated measure.
result Demonstrates a collapse in diversity of committee machines.
FedSight AI predicts federal funds rate using LLMs and multi-agent reasoning.
problem Predicting Federal Open Market Committee's decisions on federal funds rate.
method Multi-agent framework with large language models, structured and unstructured inputs, and CoD extension for efficient reasoning.
result Achieved 93.75% accuracy and 93.33% stability in predicting FOMC outcomes.
Recent studies have significantly improved the state-of-the-art on common-sense reasoning (CSR) benchmarks like the Winograd Schema Challenge (WSC) and SWAG. The question we ask in this paper is whether improved performance on these benchmarks represents genuine progress towards common-sense-enabled systems. We make ca…
Recently, Basel Committee for Banking Supervision proposed to replace all approaches, including Advanced Measurement Approach (AMA), for operational risk capital with a simple formula referred to as the Standardised Measurement Approach (SMA). This paper discusses and studies the weaknesses and pitfalls of SMA such as …
New method calibrates Gaussian product experts for better predictions.
problem Erratic predictions and uncalibrated uncertainty in Gaussian product experts.
method Calibration via tempered softmax and Wasserstein barycenter for predictions.
result Improved predictions with better mean and uncertainty quantification.
This paper considers a portfolio trading strategy formulated by algorithms in the field of machine learning. The profitability of the strategy is measured by the algorithm's capability to consistently and accurately identify stock indices with positive or negative returns, and to generate a preferred portfolio allocati…
Barcelona evaluates major events for economic and social impact.
problem Evaluating the economic and social impact of major events in Barcelona.
method Analyzes the economic and social dimensions of Barcelona's major events from 1888 to 2004.
result Develops a rational argument for communicating the economic benefits of major events.
A new methodology for incorporating LGD correlation effects into the Basel II risk weight functions is introduced. This methodology is based on modelling of LGD and default event with a single loss variable. The resulting formulas for capital charges are numerically compared to the current proposals by the Basel Commit…
Boosting is known to be sensitive to label noise. We studied two approaches to improve AdaBoost's robustness against labelling errors. One is to employ a label-noise robust classifier as a base learner, while the other is to modify the AdaBoost algorithm to be more robust. Empirical evaluation shows that a committee of…
DIAL learns embeddings to maximize recall and accuracy for entity resolution.
problem Low resource settings for entity resolution with large Cartesian product search space.
method DIAL uses an Index-By-Committee framework with pre-trained transformer language models to jointly learn embeddings for recall and accuracy.
result DIAL achieves high precision, recall, and efficiency on benchmark datasets.
New algorithm mitigates affinity bias in hiring feedback loops.
problem Mitigating affinity bias in hiring decisions to avoid unconscious favoritism.
method Introducing affinity bandits, a new bandit variant that accounts for evolving biased feedback.
result Elimination-style algorithm nearly matches the derived regret bound, outperforming classical algorithms.
We test the hypothesis that interconnections across financial institutions can be explained by a diversification motive. This idea stems from the empirical evidence of the existence of long-term exposures that cannot be explained by a liquidity motive (maturity or currency mismatch). We model endogenous interconnection…
We present a computational method for measuring financial risk by estimating the Value at Risk and Expected Shortfall from financial series. We have made two assumptions: First, that the predictive distributions of the values of an asset are conditioned by information on the way in which the variable evolves from simil…
Optimizes neural networks for solving problems with pruning and ensembles of minimal structures.
problem Improving neural network performance and interpretability.
method Pruning neural networks based on the principle of controlling training and pruning, using sensitivity indicators and logically transparent NN.
result Ensemble of minimal neural networks provides diverse forecasting algorithms and identifies areas for further data collection.
To scale Gaussian processes (GPs) to large data sets we introduce the robust Bayesian Committee Machine (rBCM), a practical and scalable product-of-experts model for large-scale distributed GP regression. Unlike state-of-the-art sparse GP approximations, the rBCM is conceptually simple and does not rely on inducing or …
In order to scale standard Gaussian process (GP) regression to large-scale datasets, aggregation models employ factorized training process and then combine predictions from distributed experts. The state-of-the-art aggregation models, however, either provide inconsistent predictions or require time-consuming aggregatio…
Firms disclosing positive earnings surprises are more likely to disclose ESG information.
problem Transparency vs. performance in financial markets.
method Empirical analysis of earnings surprises and ESG disclosures.
result Positive earnings firms disclose more ESG information than negative earnings firms.
Accounting frameworks follow stipulations of existing Accounting Theories. This exploratory research sets out to trace the evolution of accounting theories of Charge and Discharge Syndrome and the Corollary of Double Entry. Furthermore, it dives into the theories of Income Determination, garnishing it with areas of div…
Improving cancer treatment decisions requires considering causal effects, not just model accuracy.
problem Cancer outcome prediction models may cause harm when used for treatment decisions.
method Explains the importance of considering causal effects in model validation and provides guidelines.
result Building and validating models that are useful for decision making requires considering causal effects.
Machine learning (ML) techniques are enjoying rapidly increasing adoption. However, designing and implementing the systems that support ML models in real-world deployments remains a significant obstacle, in large part due to the radically different development and deployment profile of modern ML methods, and the range …
Study finds monetary policy uncertainty negatively impacts Bitcoin returns.
problem Impact of monetary policy and uncertainty on cryptocurrencies market.
method Markov Switching Means VAR (MSM-VAR) method.
result Monetary policy uncertainty leads to a decline in Bitcoin returns.
Large financial dataset tracks FOMC communications and their impact.
problem Understanding how FOMC communications influence financial markets.
method Constructed a large annotated dataset of FOMC speeches, minutes, and transcripts. Developed a hawk-dove classification task. Evaluated various models on the dataset and used RoBERTa-large for monetary policy stance measurement.
result Monetary policy stance measures derived from FOMC documents predict market performance.
We present the Bayesian Echo Chamber, a new Bayesian generative model for social interaction data. By modeling the evolution of people's language usage over time, this model discovers latent influence relationships between them. Unlike previous work on inferring influence, which has primarily focused on simple temporal…
Higher environmental performance linked to more tax avoidance, especially for financially constrained firms.
problem Tax avoidance practices in relation to environmental performance.
method Entropy balancing, propensity score matching, instrumental variable method, Heckman test.
result Higher environmental performance correlates with increased tax avoidance, particularly for financially constrained firms.
The paper presents a novel, principled approach to train recurrent neural networks from the Reservoir Computing family that are robust to missing part of the input features at prediction time. By building on the ensembling properties of Dropout regularization, we propose a methodology, named DropIn, which efficiently t…
This paper introduces the Behaviour Suite for Reinforcement Learning, or bsuite for short. bsuite is a collection of carefully-designed experiments that investigate core capabilities of reinforcement learning (RL) agents with two objectives. First, to collect clear, informative and scalable problems that capture key is…