Decision-alignment evaluates uncertainty quantification for decision-relevant UQ
problem Evaluation of uncertainty quantification metrics
method Introduce decision-alignment
result Proper scoring rules align with decision utility
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48 results for “downstream utility”
This work proposes a new pre-processing method for supervised learning to improve fairness without sacrificing utility.
problem Improving fairness in supervised learning without compromising model performance.
method Task-tailored pre-processing approach that balances fairness and utility.
result The proposed method preserves consistent trade-offs among multiple downstream models and improves fairness in computer vision tasks.
Paper establishes utility theory for synthetic data generation.
problem Lack of theoretical understanding in synthetic data utility.
method Statistical learning framework with two utility metrics: generalization and model ranking.
result Theoretical bounds for synthetic data utility metrics ensure comparable generalization and consistent model comparison.
New framework for evaluating multiclass classifier calibration.
problem Ensuring classifiers are well-calibrated for trustworthy predictions.
method Utility Calibration framework that measures calibration error relative to a utility function.
result Unified and robust interpretation of existing calibration metrics.
Proposes FairRR to improve fairness in machine learning models through randomized response.
problem Achieving group fairness in machine learning models.
method Formulates group fairness as optimizing a design matrix in Randomized Response, proposing FairRR.
result Demonstrates FairRR yields excellent model utility and fairness.
The paper proposes using density ratio estimation to evaluate synthetic data quality.
problem Improving the quality and utility of synthetic data for analysis.
method Density ratio estimation to measure synthetic data quality.
result Density ratio estimation yields more accurate global utility estimates than existing methods.
Self-supervised metric learning boosts downstream tasks in multi-view data.
problem Improving distance-based downstream tasks without labeled data.
method Developed a statistical framework to study self-supervised metric learning in multi-view data.
result Self-supervised metric learning improves target distances for various downstream tasks.
A benchmark evaluates ioUS-to-MR synthesis methods for brain tumor surgery.
problem Difficult interpretation of ioUS images for brain tumor surgery.
method Six generators trained under four inference regimes and two targets on public data.
result SynDiff-2.5D best preserved downstream segmentation (U_Dice=0.55).
Enhances performance on downstream tasks using multi-domain data.
problem Improving performance on tasks with limited downstream data.
method Deep transfer learning framework leveraging shared and domain-specific features.
result Significantly improves convergence rate for learning Lipschitz functions.
A new framework designs experiments for better decision-making.
problem Suboptimal experimental designs for downstream decision-making.
method Amortized decision-aware Bayesian Experimental Design (BED) with Transformer Neural Decision Process (TNDP).
result TNDP effectively designs experiments and facilitates accurate decision-making.
The paper addresses privacy in rank aggregation using randomized responses.
problem Preserving privacy while aggregating pairwise rankings.
method Adaptive debiasing method for randomized response rankings.
result Established minimax rates for estimation errors and optimal privacy guarantees.
Fair Wasserstein Coresetsstat.ML
FWC creates fair synthetic samples for machine learning tasks.
problem Addressing biases in machine learning models for fair decision-making.
method FWC uses an efficient majority minimization algorithm to minimize Wasserstein distance while enforcing demographic parity.
result FWC achieves a competitive fairness-utility tradeoff and reduces biases in predictions from large language models.
Proposes a hybrid model for stock market report classification using graph neural networks.
problem Lack of unified node embeddings for heterogeneous graphs in text datasets.
method Transductive hybrid approach combining unsupervised node representation learning and supervised node classification/edge prediction.
result Demonstrates the model's ability to classify stock market technical analysis reports.
New method certifies individual fairness in representations.
problem Ensuring fairness in data representations without sacrificing utility.
method Mapping similar individuals to close latent representations to certify individual fairness.
result Certifies individual fairness for existing and new data points.
Develops optimal uncertainty quantification for risk-averse decision makers.
problem Quantifying prediction uncertainty for risk-sensitive domains.
method Decision-theoretic foundations connecting uncertainty quantification with risk-averse decision-making.
result Risk-Averse Calibration (RAC) algorithm provides optimal prediction sets for risk-averse decision makers.
This paper evaluates fairness in deep metric learning and proposes a method to reduce subgroup performance gaps.
problem The negative impact of deep metric learning representations on minority subgroup performance in downstream tasks.
method Definition of fairness in DML through inter-class, intra-class, and uniformity properties; finDML benchmark; Partial Attribute De-correlation (PARADE) method.
result Bias in DML representations propagates to downstream tasks, even with balanced training data.
We present a data-driven framework for learning fair universal representations (FUR) that guarantee statistical fairness for any learning task that may not be known a priori. Our framework leverages recent advances in adversarial learning to allow a data holder to learn representations in which a set of sensitive attri…
Principal component analysis balancing prediction and approximation accuracy for spatial datastat.ME
New algorithm balances spatial data approximation and prediction accuracy.
problem Lack of methods considering spatial correlation and downstream modeling in dimension reduction.
method Formalizes approximation and modeling utility as metrics, proposes a balanced algorithm.
result Optimal trade-off between approximation accuracy and downstream modeling utility.
Survey on uncertainty in GNNs for improved model reliability.
problem Inherent randomness and model training errors lead to unstable GNN predictions.
method Comprehensive overview of GNN uncertainty theory and methods.
result Integration of uncertainty in GNNs enhances model performance and reliability.
Self-supervised learning improves few-shot classification and segmentation on point clouds.
problem Efficiently learn from limited labeled data in point cloud applications.
method Hierarchical cover-tree partitioning for self-supervised pre-training; restricted to support set for few-shot learning.
result Self-supervised learning significantly improves downstream classification and segmentation accuracy.
New methods reduce bias in synthetic data for machine learning.
problem Statistical bias in synthetic data generated for privacy.
method Re-weighting strategies using privatised likelihood ratios.
result Private importance weighting enhances synthetic data utility.
Fine-tunes GNNs by preserving generative patterns to improve transferability.
problem Vanilla fine-tuning fails due to structural divergence between pre-training and downstream graphs.
method G-Tuning, which reconstructs the generative patterns of the downstream graph using graphon bases.
result G-Tuning achieves an average improvement of 0.5% and 2.6% on in-domain and out-of-domain transfer learning experiments.
Graph representation learning is to learn universal node representations that preserve both node attributes and structural information. The derived node representations can be used to serve various downstream tasks, such as node classification and node clustering. When a graph is heterogeneous, the problem becomes more…
SMOTE-DP enhances synthetic data privacy without sacrificing utility.
problem Balancing privacy and utility in synthetic data generation.
method Integrating SMOTE with differential privacy mechanisms.
result SMOTE-DP produces synthetic data that is both private and useful.
New method samples from LLM posterior for coherent, useful responses.
problem Hallucinations in large language models.
method Posterior sampling for conditional generation, with calibration.
result Achieves statistical guarantees with higher downstream utility.
New approach to fairness in machine learning models using conformal prediction.
problem Fairness in machine learning models' downstream decision-making.
method Theoretical derivation and empirical evaluation of label-clustered conformal prediction.
result Label-clustered conformal prediction often provides a favorable balance between utility and substantive fairness.
ReTabSyn synthesizes realistic tabular data efficiently by focusing on conditional distribution.
problem Synthesizing realistic tabular data in low-data, imbalanced settings.
method ReTabSyn uses reinforcement learning to prioritize feature correlation preservation during training.
result ReTabSyn consistently outperforms state-of-the-art baselines across various benchmarks.
We introduce Contrastive Multivariate Singular Spectrum Analysis, a novel unsupervised method for dimensionality reduction and signal decomposition of time series data. By utilizing an appropriate background dataset, the method transforms a target time series dataset in a way that evinces the sub-signals that are enhan…
TVineSynth generates synthetic data to balance privacy and utility.
problem Balancing privacy and utility in synthetic data generation.
method Uses vine copula with truncation to control privacy and utility trade-off.
result Achieves superior privacy-utility balance compared to competitors.
Representation learning on graphs has emerged as a powerful mechanism to automate feature vector generation for downstream machine learning tasks. The advances in representation on graphs have centered on both homogeneous and heterogeneous graphs, where the latter presenting the challenges associated with multi-typed n…
Adversarially trained transformers can learn robustly across tasks with minimal tuning.
problem Adversarial attacks and the high cost of adversarial training.
method Adversarial pretraining followed by in-context learning.
result Single-layer linear transformers can generalize robustly to unseen tasks.
Method learns audio embeddings with contextualized tags.
problem Align audio and tags for cross-modal tasks.
method Audio autoencoder, word embeddings, multi-head self-attention, contrastive loss.
result Multi-head self-attention improves audio representations.
Proposes -balancing for more balanced expert utilization in MoE models.
problem Balanced expert utilization in MoE models to avoid bias.
method Directly targets population-level balance by minimizing a convex potential function.
result Consistently outperforms prior methods in stability and effectiveness.
New approach preserves privacy in high-dimensional data using representation learning.
problem Preserving privacy in high-dimensional data collection.
method Adapting representation learning techniques to add noise to low-dimensional data representations.
result Significantly outperforms current LDP mechanisms in downstream model learning.
CROP verifies clean prefixes in reasoning traces, improving downstream repair accuracy.
problem Uncertainty in reasoning traces prevents full certification of entire responses.
method CROP selects a calibrated threshold to certify the longest prefix with low risk proxies.
result CROP improves downstream repair accuracy by preserving valid reasoning and discarding misleading suffixes.
This paper improves model generalization by integrating diverse pretrained models.
problem Leveraging diverse pretrained models for robust out-of-distribution generalization.
method Characterize and integrate diverse pretrained models based on diversity and correlation shifts.
result Demonstrates state-of-the-art out-of-distribution generalization performance.
The ever-growing volume of data of user-generated content on social media provides a nearly unlimited corpus of unlabeled data even in languages where resources are scarce. In this paper, we demonstrate that state-of-the-art results on two Thai social text categorization tasks can be realized by pretraining a language …
MBExplainer provides explanations for models combining graph embeddings and tabular features.
problem Explaining models using a mix of graph embeddings and tabular features.
method Model-agnostic approach using Shapley values and Monte Carlo Tree Search.
result MBExplainer efficiently finds human-readable explanations for model predictions.
Complex networks are often either too large for full exploration, partially accessible, or partially observed. Downstream learning tasks on these incomplete networks can produce low quality results. In addition, reducing the incompleteness of the network can be costly and nontrivial. As a result, network discovery algo…
Proposes a new query autocompletion method that maximizes retrieval performance.
problem Users often select suboptimal queries due to unknown best retrieval performance.
method Formulates query autocompletion as ranking item rankings, uses counterfactual learning.
result Empirical results show improved query suggestions for better retrieval performance.
A new metric FRD improves comparing medical images.
problem Comparing medical images for distribution or domain differences.
method Developed a new metric FRD using standardized radiomic features.
result FRD outperforms other metrics in various medical imaging applications.
Improved RL for knowledge graph reasoning with entity types.
problem Challenges in path-based relational reasoning over knowledge graphs.
method Type-enhanced RL agent using GNN for neighborhood information.
result Outperforms state-of-the-art RL methods and discovers novel paths.
Can Pretext-Based Self-Supervised Learning Be Boosted by Downstream Data? A Theoretical Analysiscs.LG
Theoretical analysis shows pretext-based self-supervised learning can be boosted by downstream data under certain conditions.
problem Theoretical analysis of pretext-based self-supervised learning and downstream data refinement.
method Theoretical analysis and experiments on synthetic and real-world datasets.
result Theoretical lower bounds and experiments show that downstream data refinement can boost or hurt performance depending on conditions.
A novel framework extracts essential factors from order flow data for high-frequency trading.
problem Challenges in extracting and utilizing order flow data due to its large volume and limitations of traditional techniques.
method Proposes a Context Encoder and Factor Extractor for unsupervised learning of important signals from order flow data.
result Extracts superior factors from order flow data, improving stock trend prediction and order execution tasks.
This study examines how the size and alignment of pretraining data affect the performance of large language models on downstream tasks.
problem Understanding how the size and alignment of pretraining data impact the performance of large language models on downstream tasks.
method Investigated the scaling behavior of large language models in a transfer learning setting, focusing on machine translation tasks.
result The size of the finetuning dataset and the distribution alignment between pretraining and downstream data significantly influence the scaling behavior of downstream performance.
Assemblies of modular subsystems are being pressed into service to perform sensing, reasoning, and decision making in high-stakes, time-critical tasks in such areas as transportation, healthcare, and industrial automation. We address the opportunity to maximize the utility of an overall computing system by employing re…
A new method for fair classification using characteristic function distance.
problem Fairness in high-stakes decision-making with sensitive groups.
method Proposes a novel approach based on characteristic function distance to ensure minimal sensitive information in learned representations.
result Consistently matches or achieves better fairness and predictive accuracy than existing methods.
Paper defines and solves a problem in representation learning to ensure fairness with high confidence.
problem Learning fair representations with high confidence guarantees for all downstream tasks.
method Formally defines the problem, introduces FRG framework, proves high probability fairness, and demonstrates effectiveness empirically.
result FRG framework provides high-confidence guarantees for limiting unfairness across all downstream models and tasks.