Social networks may manipulate users without regulatory oversight.
problem Manipulation of users by sophisticated curator algorithms.
method Analysis of machine learning and reinforcement learning techniques.
result Curator algorithms can learn and implement manipulative strategies.
We explain SSL objectives as log-likelihoods in a data curation model.
problem Lack of understanding of SSL objectives as log-likelihoods.
method Formulate SSL objectives as a log-likelihood in a generative model of data curation.
result SSL methods can be understood as lower-bounds on a principled log-likelihood.
The paper examines how decentralized credit curators have taken over risk management from traditional protocols.
problem Risk management in decentralized credit has shifted from centralized protocols to decentralized curators.
method Analysis of ERC 4626 vaults and third-party curators, focusing on capital utilization, concentration, and fee margins.
result Curators have a significant impact on the risk profile of decentralized credit systems, with a small set of curators handling a disproportionate share of system TVL.
The paper resolves the paradox of using less data in machine learning.
problem The paradox of using less data in machine learning.
method Theoretical framework and data curation strategies.
result Small curated datasets can outperform full datasets under certain conditions.
OpenML provides standardized machine learning benchmarks.
problem Objective, comparable, reproducible benchmarks for machine learning.
method Curated benchmarking suites with standardized data formats and APIs.
result OpenML-CC18 benchmark suite for classification.
The paper shows how curated synthetic data can optimize human preferences in generative models.
problem Contamination of web-scale datasets by synthetic data affects future model training.
method Theoretical study of iterated retraining of generative models with curated synthetic data.
result Data curation can be seen as an implicit preference optimization mechanism, maximizing expected reward.
DataRater learns which data points are most valuable for training models.
problem Training model efficiency depends on high-quality training data.
method Meta-learning to estimate the value of data points for training.
result Meta-learning improves compute efficiency by filtering data effectively.
Method curates cost-effective, high-quality datasets using AI models.
problem Costly manual labeling of datasets.
method Probably Approximately Correct Labels (PACL) method.
result Curates high-quality datasets with low overall labeling error.
The paper compares aggregated data labels in curated and random bags for machine learning models.
problem Protecting user privacy in machine learning systems with aggregated data.
method Examined curated and random bags for training machine learning models and compared their performance.
result Gradient-based learning can be performed on aggregated data without performance degradation.
New insights into bias mitigation show DRO isn't a complete solution.
problem Bias in machine learning systems across different data subsets.
method Theoretical analysis of Distributionally Robust Optimization (DRO) and data curation.
result Neither DRO nor data curation alone can fully address bias issues.
Modeling incentives for content creators on algorithm-curated platforms.
problem Maximizing exposure for content creators on algorithmic platforms.
method Formalized exposure game model, proving effects of algorithmic choices on equilibria, proposing tools for finding equilibria.
result Algorithmic choices significantly affect content exposure and creator behavior.
This research creates and classifies datasets for Setswana and Sepedi news headlines.
problem Challenges in creating and preparing datasets for low-resourced languages.
method Investigates an approach for data augmentation tailored to low resource languages.
result Improves classification performance on news topic classification task.
Cold posteriors in BNNs harm performance, likely due to incorrect likelihood.
problem Cold posteriors in Bayesian neural networks degrade performance.
method Developed a generative model explaining cold posteriors and matched it to the tempered likelihoods.
result Cold posteriors are a result of using the wrong likelihood for image classification datasets.
Study on self-consuming generative models with diverse human curation, focusing on convergence and stability.
problem Analyzing self-consuming generative models with heterogeneous human curation.
method Investigates the asymptotic behavior of retraining dynamics using nonlinear Perron--Frobenius theory and Banach contraction mapping.
result Improves convergence results and provides stability and non-stability analyses for the model.
ConCuR generates high-quality CUDA kernels with concise reasoning traces.
problem Scarce high-quality data for kernel generation.
method Developed a pipeline to generate and curate high-quality CUDA kernels with reasoning traces.
result Our model achieves significant improvements in KernelBench setup.
Dynamic pricing improves DeFi lending efficiency by reducing regret to logarithmic levels.
problem Static pricing mechanisms in DeFi lending protocols lead to suboptimal welfare and revenue.
method Online learning model for static and dynamic pricing models in DeFi lending.
result Adaptive supply models achieve logarithmic regret, outperforming static models.
We develop a privacy-preserving method to summarize distributed datasets under covariate shift.
problem Constructing a training dataset from multiple private sources that matches a target task without privacy leakage.
method Introduced a novel protocol using hash functions and differential privacy mechanisms to minimize private information access.
result Achieved strong differential privacy guarantees while closely matching the performance of non-private greedy algorithms.
FinSphere improves stock analysis quality with AI and expert-curated data.
problem Lack of objective evaluation metrics and depth in stock analysis by FinLLMs.
method Developed AnalyScore, curated Stocksis dataset, and FinSphere AI agent.
result FinSphere outperforms general and domain-specific LLMs in generating high-quality stock analysis reports.
SubseasonalClimateUSA dataset improves subseasonal weather forecasting.
problem Challenges in subseasonal weather forecasting, especially skill of physics-based models and integration of local and global variables.
method Curated dataset for training and benchmarking subseasonal forecasting models, including various methods.
result Benchmarking suggests simple and effective ways to improve current operational models.
CrossFilter tackles noisy labels in audio tagging.
problem Noisy labels in large audio datasets.
method CrossFilter framework using multiple representations and multi-task learning.
result Improves audio tagging performance on FSDKaggle2018 and FSDKaggle2019 datasets.
Similarity encoding improves learning from messy categorical data.
problem Learning from categorical variables with high cardinality and redundancy.
method Similarity encoding, a generalization of one-hot encoding that uses similarities between categories.
result Similarity encoding significantly outperforms traditional encoding methods in prediction accuracy.
Dr.S recommends cancer drugs based on genomic data.
problem Personalizing cancer treatments using genomic information.
method Machine learning to identify optimal drug-gene associations.
result Developed a Drug Recommendation System (Dr.S) for cancer cell lines.
InvestLM is a financial domain LLM tuned on LLaMA-65B for investment advice.
problem Improving financial text understanding and advice generation for investment.
method Curated financial instruction dataset, LLaMA-65B, less-is-more-for-alignment approach.
result InvestLM provides comparable responses to state-of-the-art commercial models.
The paper investigates ethical issues in large image datasets, focusing on pornographic content.
problem Ethical issues in large-scale computer vision datasets, particularly concerning pornographic content.
method Cross-sectional model-based quantitative census covering various factors in the ImageNet-ILSVRC-2012 dataset.
result The dataset contains verifiably pornographic images, including non-consensual and voyeuristic content.
Proposes measuring fairness through multiple stakeholder-curated stress tests.
problem Limited power of rigid fairness metrics and lack of stakeholder involvement in fairness discussions.
method Shift focus from fairness metrics to stress tests curated by stakeholders.
result Machine's performance under multiple stress tests reflects fairness.
RoPAWS improves semi-supervised learning on uncurated data.
problem Efficiency of semi-supervised learning with real-world unlabeled data.
method Reinterprets PAWS as a generative classifier and calibrates predictions using densities of labeled and unlabeled data.
result Significant improvement in performance for uncurated data.
Paper tackles privacy issues in LDA model training.
problem Privacy leakage in LDA model training.
method Developed privacy monitoring and locally private LDA training algorithms.
result Demonstrated effectiveness of locally private LDA training on crowdsourced data.
DILP improves fraud detection explainability without significant performance boost.
problem Improving fraud detection explainability in machine learning.
method Differentiable Inductive Logic Programming (DILP) for fraud detection with data curation.
result DILP provides comparable results to traditional methods but lacks significant advantage.
FLAME improves privacy in federated learning without trusted parties.
problem Ensuring privacy in federated learning without trusted parties.
method FLAME uses the shuffle model of differential privacy to achieve better accuracy and privacy.
result FLAME protocols improve testing accuracy by 60.7% compared to local model FL.
SUMMA aggregates predictions without labeled data.
problem Lack of labeled data limits ensemble methods.
method Developed SUMMA framework for unlabeled data.
result Estimates base classifier performances and optimal ensemble strategy.
Bayesian method detects outliers and uncertain points in data.
problem Detecting outliers and uncertain points in data using Bayesian methods.
method Generative model of data curation for aleatoric uncertainty, combining with epistemic uncertainty and outlier exposure.
result Principled Bayesian approach outperforms methods using aleatoric or epistemic uncertainty alone.
TextWorld is a Python library for RL agents in text-based games.
problem Training RL agents on text-based games with varying challenges and sparse rewards.
method Developed a Python library with backend functions for state tracking and reward assignment. Enables users to create new games with precise control over difficulty and scope.
result Demonstrated the effectiveness of TextWorld in training RL agents on a curated list of games and generated sets of games.
A method for cold-start playlist recommendation using multitask learning.
problem Playlist recommendation for new users and songs.
method Flexible multitask learning method that minimizes a bipartite ranking loss and a classification loss.
result The method performs well in cold-start scenarios on real datasets.
ProductNet curates high-quality product datasets for better product understanding.
problem Lack of high-quality product datasets for product representation learning.
method Curated high-quality product datasets with a multi-modal deep neural network and active learning.
result Master model yields high categorization accuracy (94.7% top-1 accuracy for 1240 classes).
CAI automates extraction and validation of corporate GHG emission metrics.
problem Manual extraction of corporate GHG emission metrics is labor-intensive and error-prone.
method CAI uses LLMs to automate extraction and validation of metrics from corporate disclosures.
result CAI improves data collection efficiency and accuracy by automating the process.
DeepVir uses deep matrix factorization to predict antivirals for COVID-19.
problem Predicting effective antivirals for COVID-19 using known drug-virus associations.
method Graphical deep matrix factorization with HyPALM optimization.
result DeepVir outperforms state-of-the-art techniques in predicting antivirals for COVID-19.
MoleculeNet benchmarks molecular machine learning algorithms.
problem Lack of a standard benchmark for molecular machine learning.
method Curated multiple public datasets, established evaluation metrics, released open-source implementations.
result Learnable representations offer the best performance in molecular machine learning.
Adaptive financial dataflow system improves model robustness in dynamic markets.
problem Static historical data leads to poor performance in dynamic financial markets.
method Drift-aware dataflow system with adaptive control and optimization.
result Enhanced model robustness and improved risk-adjusted returns.
Analyzes factors affecting flow VI performance.
problem Consistent performance of flow VI across studies.
method Step-by-step analysis of capacity, objectives, batchsize, estimators, and step-sizes.
result Specific recommendations and a flow VI recipe.
Algorithm matches vertices of correlated Erdős-Rényi graphs efficiently.
problem Graph matching in correlated Erdős-Rényi graphs.
method Counting chandeliers to extract graph correlation.
result Correctly matches all but a vanishing fraction of vertices with high probability.
In-Run Data Shapley offers efficient data attribution for large-scale models.
problem Existing data attribution methods are computationally intensive and cannot target specific models.
method In-Run Data Shapley, which efficiently attributes data contributions to a specific model without re-training.
result In-Run Data Shapley achieves significant efficiency, enabling data attribution for pretraining models.
Unsupervised method detects earthquakes from raw waveforms, generalizing across datasets.
problem Lack of labeled data for earthquake detection.
method Uses deep autoencoders with cross-covariance triggering at bottleneck.
result Performance comparable to supervised methods, with strong cross-dataset generalization.
This work simplifies data valuation for LLMs using Shapley value computation.
problem How to fairly distribute benefits from training superior LLMs with multiple data owners' resources.
method We leverage the specific mathematical structure of DPO to enable scalable Shapley value computation for LLMs.
result We demonstrate that Shapley value computation for LLMs trained with DPO is significantly simplified.
Data-driven approach discovers molecular photoswitches with separated electronic absorption bands.
problem Engineering photoswitchable molecules with specific electronic absorption bands remains challenging.
method Data-driven discovery pipeline using Gaussian processes for multitask learning.
result Multioutput Gaussian process (MOGP) trained on four photoswitch transition wavelengths outperforms single-task models and TD-DFT.
User strategization undermines algorithmic trustworthiness.
problem User strategic behavior corrupts algorithmic data and trust.
method Modeling user-platform interactions as a game, analyzing strategic behavior's short-term benefits and long-term harms.
result User strategization can initially benefit platforms but ultimately harms their ability to make accurate decisions.
Infrastructure monitors AI/ML radiology models across multiple sites.
problem Monitoring and improving AI/ML radiology models across multiple sites.
method Interactive radiology reporting, centralized cloud system, post-marketing surveillance.
result Efficient monitoring and iterative development of AI/ML models without radiologist burden.
The paper tackles confidence calibration for exploratory machine learning problems.
problem Difficulty in curating datasets and confusion about category validity.
method Introduces four new algorithms for category-specific confidence estimation, including kernel density ratios.
result Kernel density ratios provide a novel approach to confidence calibration, especially for exploratory problems.
Cer-Eval saves LLM evaluation costs while maintaining accuracy.
problem Challenges in evaluating large language models due to large dataset requirements.
method Adapts to different evaluation objectives, uses test sample complexity, and develops a partition-based algorithm.
result Cer-Eval can save 20-40% test points with comparable accuracy and 95% confidence guarantee.