The study compares M6 competitors' performance to industry benchmarks and discusses incentives for investment managers.
problem Investors seek to understand the performance and skill of M6 competitors beyond the competition's metrics.
method Comparative analysis using financial metrics, factor models, and new strategies.
result Most competitors do not generate significant out-performance compared to industry benchmarks, but some show skill in recent performance.
Enhances portfolio optimization by considering competitor benchmarks and regret.
problem Optimizing portfolios relative to specified competitors and measuring performance accurately.
method Extends Relative Robust Portfolio Optimisation to include competitor benchmarks and introduces a new way to measure regret.
result Improved portfolio optimization with competitor benchmarks and a new method for measuring regret.
Game theory models incentivizes honesty in collaborative learning among competitors.
problem Incentivizing honest updates among competitors in collaborative learning schemes.
method Formulated a game to model interactions, studied two learning tasks, proposed mechanisms to incentivize honest communication.
result Rational clients are incentivized to manipulate their updates, preventing learning; proposed mechanisms ensure comparable learning quality to full cooperation.
This paper analyzes Thompson Sampling in restless bandits with unknown parameters.
problem Analyzing performance of Thompson Sampling in restless bandits with unknown parameters.
method Proved a Bayesian regret bound of i l d e O ( T ) ilde{\mathcal{O}}(\sqrt{T}) i l d e O ( T ) for Thompson Sampling in episodic restless bandits with unknown parameters. result Proved a regret bound of i l d e O ( T ) ilde{\mathcal{O}}(\sqrt{T}) i l d e O ( T ) for Thompson Sampling in restless bandits with unknown parameters. Efficiently predict LLM benchmarks using feature selection and regression.
problem Predicting full benchmark scores with minimal question subsets.
method Multiple regression with feature selection, using kernel ridge regression and mRMR.
result Improved prediction accuracy and ranking correlation across various benchmarks.
Develops a new method for quantizing rough volatility for volatility derivatives pricing.
problem Pricing volatility derivatives in rough volatility models.
method Functional quantization of rough volatility using offline computable quantizers.
result Pricing VIX Futures in the rough Bergomi model shows competitive results.
MuyGPs efficiently estimates GP hyperparameters using local cross-validation.
problem Efficiently estimating GP hyperparameters for large datasets.
method Uses nearest neighbors structure and leave-one-out cross-validation.
result Outperforms state-of-the-art competitors in time and prediction accuracy.
VSC uses subsampling and locality to improve binary classification performance.
problem Improving binary classification accuracy using novel feature extraction methods.
method VSC employs subsampling and locality to define features for a perceptron, using max-margin classifiers on randomly selected pairs of samples.
result VSC outperforms 9 competitors on 22 benchmark datasets, including outperforming Multi Layer Perceptron (MLP).
Auto-PyTorch automates deep learning by optimizing neural architectures and hyperparameters.
problem Automated deep learning for tabular data with robust and efficient optimization.
method Combines multi-fidelity optimization, portfolio construction, and warmstarting with ensembling.
result Achieves state-of-the-art performance on tabular benchmarks.
Graph Convolutional Networks improve performance on complex data.
problem Processing high-dimensional, graph-based data for automation.
method Enhanced existing Graph Convolutional Network models with four improvements.
result Significant performance improvements on four benchmark datasets.
MALC combines interpretable linear models with black-box models for better predictions and transparency.
problem Combining interpretability with black-box models for better predictions.
method Formulates MALC as a convex optimization problem and uses accelerated proximal gradient method for training.
result MALC provides an efficient frontier balancing prediction accuracy and transparency.
We propose RoBiRank, a ranking algorithm that is motivated by observing a close connection between evaluation metrics for learning to rank and loss functions for robust classification. The algorithm shows a very competitive performance on standard benchmark datasets against other representative algorithms in the litera…
Paper presents a new method for better financial market forecasting.
problem Traditional investment strategies fail to capture market nuances and risks.
method Combines deep learning, factor integration, and correlated stock analysis.
result Enhanced diversification and performance capture in financial markets.
We propose a novel algorithm which allows to sample paths from an underlying price process in a local volatility model and to achieve a substantial variance reduction when pricing exotic options. The new algorithm relies on the construction of a discrete multinomial tree. The crucial feature of our approach is that -- …
Study evaluates deep RL methods for robotic grasping, focusing on off-policy learning.
problem Identify the best deep RL methods for vision-based robotic grasping.
method Proposed a simulated benchmark for grasping tasks, evaluating Q-function estimation, Monte Carlo return, and off-policy correction methods.
result Several simple methods outperform popular algorithms like double Q-learning.
Improves sample efficiency in evolutionary policy search methods.
problem Worse sample efficiency in evolutionary policy search methods.
method Importance mixing to reuse more samples.
result Improves sample efficiency but still less than deep reinforcement learning.
New method ranks competitors from multiple types of comparisons.
problem Ranking individuals or teams from multiple conflicting comparison types.
method Combination of expectation-maximization algorithm and modified Bradley-Terry model.
result A ranking can be computed from multiple conflicting comparison types.
Improved YouTube-8M classification accuracy using ensemble methods.
problem Improving video classification accuracy on YouTube-8M dataset.
method Ensemble methods to improve baseline predictions.
result Global prediction accuracy (GAP) increased from 77% to 80.7%.
A new adaptive kNN classifier outperforms Random Forests.
problem Improving classification accuracy using nearest neighbors.
method Finding discriminant subspaces for efficient nearest neighbor classification, leveraging bagging for diversity.
result The proposed method outperforms Random Forests and other nearest neighbors ensembles.
Broker uses multi-task dynamic pricing to learn competitive prices in credit markets.
problem Lack of data and infrequent trading in credit markets.
method Two-Stage Multi-Task (TSMT) algorithm that leverages shared structure across securities.
result TSMT algorithm achieves a regret bound of O ( T M d + M d ) O(\sqrt{T M d} + M d) O ( T M d + M d ) , outperforming baselines. Paper introduces a new method to improve learning on imbalanced regression problems.
problem Imbalanced distribution learning in predictive modeling reduces standard algorithms' performance.
method The paper proposes a novel method using disentangled VAEs and Smoothed Bootstrap in the latent space.
result The method improves learning on tabular data within the Imbalanced Regression framework.
A novel approach to improve knowledge base completion using tensor decomposition.
problem Knowledge Base Completion (KBC) as a tensor completion problem.
method Canonical Tensor Decomposition (CP) with novel regularizers and reformulation.
result Improved KBC results using CP decomposition and ComplEx model.
WAR method improves classifier robustness in noisy label datasets.
problem Learning robust classifiers in presence of noisy labels.
method Adversarial regularization based on Wasserstein distance.
result WAR method outperforms state-of-the-art competitors on noisy label datasets.
A simple baseline for extreme multi-label classification using random projections.
problem Automatically annotating data points with relevant labels from a large label vocabulary.
method On-the-fly global embedding using random projections, with an ensemble of learners.
result Competitive accuracy compared to existing methods, with significant speed-up and model-size reduction.
MMD-Flagger detects hallucinations in LLMs by tracking MMD between outputs and temperature-generated counterparts.
problem Detecting hallucinations in large language models.
method Maximum Mean Discrepancy (MMD) to track the difference between model outputs and temperature-generated counterparts.
result MMD-Flagger detects most hallucinations by analyzing the shape of the MMD trajectory.
DECS tool assesses swap rates of DEXes and Fusion outperforms competitors.
problem Lack of unbiased swap rate comparisons in decentralized finance.
method Swap transaction monitoring and simulation techniques.
result 1inch Classic and Fusion consistently outperform competitors in swap rates.
Bitcoins have emerged as a possible competitor to usual currencies, but other crypto-currencies have likewise appeared as competitors to the Bitcoin currency. The expanding market of crypto-currencies now involves capital equivalent to 10 10 10^{10} 1 0 10 US Dollars, providing academia with an unusual opportunity to study the em…
Two strategic agents track their portfolios, influencing each other's trading targets.
problem Strategic competition in portfolio tracking with price impact.
method Stochastic linear quadratic differential game with terminal state constraints.
result Unique open-loop Nash equilibrium strategies emerge based on price impact types.
Paper recovers multi-subspace matrices from permuted data.
problem Recovering a multi-subspace matrix from permuted data with corrupted columns.
method Four-stage algorithm pipeline: outlier identification, subspace reconstruction, outlier classification, unsupervised sensing.
result The pipeline provides theoretical guarantees for reliable multi-subspace matrix recovery.
TSGAN models time series data with limited data, outperforming competitors.
problem Lack of quality time series data for machine learning.
method Proposes Time Series GAN (TSGAN) for synthetic time series generation.
result TSGAN outperforms competitors in FID metric and qualitative classification.
A clustering algorithm uses the left Gram matrix for high dimensional data.
problem Clustering high dimensional data with many features and few objects.
method The algorithm uses the normalized left Gram matrix G = XX'/P to cluster objects based on row means.
result The algorithm provides the most accurate cluster configuration more than twice as often as competitors.
SKADA-bench evaluates unsupervised DA methods across diverse modalities.
problem Evaluating unsupervised DA methods on diverse modalities with realistic validation.
method Nested cross-validation and unsupervised model selection scores.
result Highlights the importance of realistic validation and provides practical guidance.
SU improves exploration in reinforcement learning, surpassing human performance on Atari games.
problem Challenges in scaling PSRL for reinforcement learning with neural networks.
method Design and implementation of Successor Uncertainties (SU) algorithm.
result SU outperforms human performance on Atari games and surpasses RVF competitor Bootstrapped DQN.
A nonparametric two-sample test using a parametric integral probability metric
problem Detecting distributional differences between two independent samples
method Propose a new two-sample test statistic based on a newly introduced integral probability metric (IPM)
result Establish theoretical guarantees for the associated two-sample testing procedure
The paper analyzes how automated market makers can retain trading fees.
problem How automated market makers can sustainably retain a portion of trading fees.
method Modeling to determine the optimal take rate for AMMs to maximize their revenue.
result AMMs can sustainably set a non-zero take rate if they have loyal trade volume.
The paper solves portfolio optimization problems with risk constraints.
problem Maximizing utility while ensuring a certain wealth threshold with risk constraints.
method Derives Nash equilibria for two agents and characterizes them for more than two agents.
result Characterizes Nash equilibria for different cases of competition probabilities.
Paper proposes a new model to measure common risk factors using quantile regression.
problem Measuring common market risk factors among financial assets.
method Panel Quantile Regression Model for Returns with penalized fixed effects estimator.
result The proposed model outperforms other models in Value-at-Risk forecasting, especially in the 5% and 10% quantiles.
Cellina uses supervised disentanglement to predict cell behavior in tissues.
problem Querying counterfactuals on tissue graphs
method Cellina framework using supervised disentanglement
result Outperforms spatially-informed and non-spatial competitors
This paper tackles generating manifold-valued images using WGAN.
problem Generating manifold-valued images over natural images.
method Formulated a theorem of optimal transport for Wasserstein distance on manifolds, introduced a new WGAN framework.
result Proposed model generates more plausible manifold-valued images than competitors.
AutoGluon-Tabular automates tabular data ML with single line Python.
problem Training accurate ML models on unprocessed tabular data.
method Ensembling multiple models in multiple layers.
result Outperforms competitors in accuracy and robustness.
CRPS improves GP-based sequential design for chemical space.
problem Finding molecules with specific properties in synthetic chemistry.
method Threshold-weighted CRPS as acquisition function for GP models in sequential design.
result Improved performance in molecule research with CRPS-based strategies.
System constructs public competitor graph from financial reports.
problem Time-consuming and expert-laden manual extraction of corporate relationships.
method Financial report processing to generate reliable knowledge graph of corporate relationships.
result More than 83% of S\&P 500 companies' competition relationships retrieved.
Unified framework for critical scaling of inverse temperature in self-attention.
problem Conflicting inverse-temperature laws for long-context self-attention.
method Counting gaps and defining an upper-tail accumulation scale.
result Critical inverse-temperature scale determined by gap-counting function.
Framework for generating multiple clusterings from multi-view data.
problem Challenges in finding optimal clustering criteria and handling incomplete multi-view data.
method DiMVMC framework that optimizes multiple decoder deep networks to complete data views and generate shared representations.
result DiMVMC outperforms state-of-the-art competitors in generating multiple clusterings with high diversity and quality.
Study uses Open Banking data to estimate customer value, showing potential 21% increase.
problem Limited CLV estimation using single-entity data.
method Introduces PCLV framework using Open Banking data for comprehensive customer value estimation.
result Open Banking data can estimate PCLV per competitor, showing a 21.06% increase over Actual CLV.
Paper uses transfer learning and Bayesian optimization to reduce DNA sequence design experiments.
problem Designing many similar DNA sequences for specific applications is expensive and time-consuming.
method Combines transfer learning with Bayesian optimization to reduce experiment count.
result Total number of experiments can be significantly reduced by sharing information between tasks.
Challenge aims to develop automated meningioma MRI segmentation models.
problem Lack of automated, objective tools for meningioma assessment.
method Develop and evaluate models on largest annotated dataset.
result Improved care of patients with meningioma through automated segmentation.
A contaminated mixture model detects outliers in multivariate functional data.
problem Detecting abnormal sensor measurements in multivariate functional data.
method A contaminated mixture model that clusters and detects outliers without specifying outlier proportion.
result The model outperforms competitors and correctly detects abnormal behaviors in real data.