We use smaller proxy datasets to speed up hyperparameter search.
problem Time-consuming and costly training of neural networks on large datasets.
method Generate smaller 'proxy datasets' by sampling, training on easy/hard examples, or generating synthetic data. Compare proxy quality using variance measures.
result Proxy datasets can explain up to 95% of the variance in full dataset results, significantly faster.
Study accelerates NAS research with a large dataset of ZC proxies.
problem Speeding up neural architecture search with ZC proxies.
method Created NAS-Bench-Suite, evaluated 13 ZC proxies across 28 tasks, and provided a unified codebase.
result ZC proxies capture substantial complementary information and can improve NAS algorithm performance.
A data proxy reduces NAS computation cost by preserving key examples.
problem High computation cost in Neural Architecture Search (NAS).
method Measuring example importance based on relative accuracy ranking and creating a data proxy.
result Preserves almost same relative accuracy ranking with 10-20x smaller data proxy.
We consider the problem of improving fairness when one lacks access to a dataset labeled with protected groups, making it difficult to take advantage of strategies that can improve fairness but require protected group labels, either at training or runtime. To address this, we investigate improving fairness metrics for …
Method for creating synthetic multi-fidelity data sets.
problem Lack of representative synthetic datasets for multifidelity optimisation benchmarks.
method Systematic generation of synthetic fidelities from preexisting datasets.
result Allows systematic investigation of lower fidelity proxies' influence.
Predicting delayed outcomes is an important problem in recommender systems (e.g., if customers will finish reading an ebook). We formalize the problem as an adversarial, delayed online learning problem and consider how a proxy for the delayed outcome (e.g., if customers read a third of the book in 24 hours) can help mi…
Efficiently selects data points for deep learning using smaller proxy models.
problem Expensive data selection methods in deep learning.
method Uses a small proxy model to perform data selection.
result Improves data selection runtime by an order of magnitude without significant accuracy loss.
New method recovers latent confounders from high-dimensional proxy variables.
problem Detecting latent confounders from high-dimensional proxy variables.
method Proxy Confounder Factorization (PCF) framework using ICA-PCF and GD-PCF.
result ICA-PCF recovers confounders with high correlation and low error in synthetic and real-world data.
A machine learning model may exhibit discrimination when used to make decisions involving people. One potential cause for such outcomes is that the model uses a statistical proxy for a protected demographic attribute. In this paper we formulate a definition of proxy use for the setting of linear regression and present …
SAGE generates subsurface velocity models from sparse well logs and seismic images.
problem Lack of high-quality subsurface velocity models due to limited data availability.
method Subsurface AI-driven geostatistical extraction using proxy posterior.
result SAGE produces geologically plausible and statistically accurate velocity realizations.
Estimates Granger causality with unobserved confounders using deep latent-variable recurrent neural networks.
problem Non-linear Granger causality with unobserved confounders in observational studies.
method Generative model with latent variable, variational autoencoder, recurrent neural network.
result Estimated confounders improve performance in non-linear Granger causality with multiple proxies.
Predictive analytics is increasingly used to guide decision-making in many applications. However, in practice, we often have limited data on the true predictive task of interest, and must instead rely on more abundant data on a closely-related proxy predictive task. For example, e-commerce platforms use abundant custom…
FairJob dataset for job recommendations in advertising, preserving fairness and utility.
problem Fairness in online job recommendations for advertising.
method Anonymized dataset with proxy for sensitive attributes, fairness metric computation.
result Demonstrated potential improvements in fairness with trade-offs in utility.
Estimates proxy-based inference adjustments for distribution shifts.
problem Imperfect proxy data leads to biased inference.
method Empirical calibration of proxy-primary metric discrepancy as a random effect.
result Empowers inference without individual-level response data.
We often desire our models to be interpretable as well as accurate. Prior work on optimizing models for interpretability has relied on easy-to-quantify proxies for interpretability, such as sparsity or the number of operations required. In this work, we optimize for interpretability by directly including humans in the …
Ward2ICU dataset protects patient privacy while generating synthetic ICU transitions data.
problem Protecting patient privacy while creating synthetic ICU transition data.
method Wasserstein Generative Adversarial Network (GAN) to generate synthetic data, class label balancing.
result Quality of synthetic data generation assessed through binary classification task.
Algorithm optimizes biological sequences using bootstrapped training with a score-conditioned generator.
problem Optimizing biological sequences for a black-box score function.
method Bootstrapped training of score-conditioned generator (BootGen) algorithm.
result Our method outperforms competitive baselines on biological sequential design tasks.
Deep learning has thrived by training on large-scale datasets. However, in robotics applications sample efficiency is critical. We propose a novel adaptive masked proxies method that constructs the final segmentation layer weights from few labelled samples. It utilizes multi-resolution average pooling on base embedding…
The paper shows how to use proxy attributes for fairness in machine learning models with missing sensitive group data.
problem Measuring and enforcing fairness in machine learning models with incomplete sensitive group data.
method Using proxy-sensitive attributes to derive upper bounds on multiaccuracy and multicalibration violations and adjust models to satisfy these fairness notions.
result Provable upper bounds on multiaccuracy and multicalibration violations can be derived using proxy-sensitive attributes in the absence of sensitive group data.
Develops a method to audit indirect feature influence in complex models.
problem Auditing indirect feature influence in complex, black-box models.
method Disentangled influence audits using disentangled representations.
result Can detect proxy features and show which ones affect model outcomes most.
A proxy network for few-shot learning improves model generalization.
problem Training models with limited data for new classes.
method Meta-learning architecture with a proxy network that learns class representatives and distance metrics.
result The proxy network outperforms state-of-the-art methods in few-shot learning tasks.
New method robust to random distributional shifts in prediction.
problem Random distributional shifts in real-world settings.
method Hybrid approach combining long-term and proxy outcomes.
result Hybrid approach yields lower mean-squared error than current methods.
A new framework improves VaR recalibration by balancing reliance on imperfect volatility proxies.
problem How to balance reliance on imperfect volatility proxies in one-sided VaR recalibration.
method Proxy-reliance control framework that interpolates between constant-shift and proxy-scaled corrections.
result Lower or intermediate proxy reliance can outperform fully proxy-scaled recalibration in stressed left-tail VaR control.
Method estimates dataset utility via minimal program length proxy.
problem Determining if data labels are generated by useful subroutines.
method Rissanen Data Analysis (RDA) estimates minimum description length (MDL) as a proxy.
result Method reveals dataset characteristics and utility in various NLP settings.
PPI uses proxy data to improve inference from limited labels across related tasks.
problem Statistical inference with limited labels across multiple related tasks.
method Prediction-powered inference framework that uses cross-task recalibration to improve power and accuracy.
result Cross-task recalibration can substantially reduce confidence interval widths when labels are scarce.
Improved 3D MRI classification using contrastive learning with continuous proxy metadata.
problem Insufficient labelled data for 3D medical image classification.
method Proposed a new loss function (y-Aware InfoNCE) to leverage continuous proxy metadata in contrastive learning.
result 3D CNN model pre-trained on 10^4 multi-site healthy brain MRI scans outperforms fully-supervised methods.
Unified framework for analyzing neural networks trained by gradient descent.
problem Lack of generalizable guarantees for neural networks trained by gradient descent.
method Proxy convexity and proxy Polyak-Lojasiewicz inequalities.
result Unified guarantees for neural networks trained by gradient descent.
Study semi-supervised learning with noisy proxy covariates, deriving bounds and showing gains.
problem Learning from noisy proxy covariates with scarce labels.
method Two-stage estimator learning kernel eigenfeatures from all proxy covariates and fitting a ridge predictor on labeled data.
result Finite sample bounds show fast labeled sample rates and consistent gains over supervised and semi-supervised baselines.
DFPV improves PCL for confounded bandit policy evaluation.
problem Estimating causal effects in confounded settings with high-dimensional data.
method Deep feature proxy variable method (DFPV) for high-dimensional, nonlinear relationships.
result DFPV outperforms state-of-the-art methods on synthetic benchmarks and confounded bandit problems.
Modern computer vision algorithms typically require expensive data acquisition and accurate manual labeling. In this work, we instead leverage the recent progress in computer graphics to generate fully labeled, dynamic, and photo-realistic proxy virtual worlds. We propose an efficient real-to-virtual world cloning meth…
A framework uses proxies to prioritize treatment without estimating causal effects.
problem Prioritizing treatment when causal effects are hard to estimate.
method Decision-focused framework identifying conditions for proxy usefulness.
result Proxies can recover correct effect ordering under specific conditions.
Single proxy variable helps estimate causal effects from confounders.
problem Estimating causal effects from treatment to outcome when unobserved confounders are present.
method Assumes a single, potentially multi-dimensional proxy variable of the unobserved confounder and a known mechanism generating the proxy from the confounder. Proves causal effects are identifiable under completeness assumption.
result Causal effects are identifiable under SPICE assumption.
A scalable method for deep metric learning using chance constraints.
problem Improving deep metric learning by addressing feasibility issues.
method Relating DML to chance constraints, reformulating as a feasibility problem, and iteratively training proxies.
result The method effectively improves deep metric learning performance across multiple benchmarks.
Study data biases to predict algorithmic discrimination, developing a Data Bias Profile.
problem Data biases contribute to algorithmic discrimination, but their impact is understudied.
method Analyzed three common data biases across various datasets and models, developing a Data Bias Profile.
result Combination of proxies and label bias can lead to more significant discrimination than underrepresentation alone.
Develops methods to improve demand counterfactuals from imperfect proxies.
problem Imperfect proxies in demand models lead to biased counterfactuals and invalid inference.
method Practical toolkit for market-level and individual data, requiring minimal computation.
result Improves substitution prediction and counterfactual performance.
New method handles many noisy proxy controls for causal inference.
problem Causal inference with many noisy proxy controls and unknown confounders.
method Linear models with rank-restricted and sparse nuisance parameters, penalization methods.
result Estimators achieve better performance in high dimensions, especially with many proxies.
proxymate diagnoses and corrects proxy estimates for reliable inference.
problem Systematic bias in proxy estimates can lead to invalid inference on primary outcomes.
method Four-level framework for proxy validation and adjustment, including diagnostic checks and targeted corrections.
result Facilitated quick decision making on thousands of experiments at Meta.
New conditions show proxy data can improve policy learning in sparse expert data contexts.
problem Improving sample complexity of policy learning with sparse expert data.
method Sufficient conditions for proxy data to improve sample complexity, adaptable to LLMs.
result Provable improvement in sample complexity for policy learning with proxy data.
A new probabilistic approach improves deep metric learning by considering image uncertainties and class-specific variances.
problem Proxy-based deep metric learning struggles with image uncertainties and class-specific structures.
method Introduces non-isotropic probabilistic proxy-based deep metric learning using directional von Mises-Fisher distributions.
result Improves generalization performance and competitive on standard benchmarks.
Proposes a method to create robust linear models with noisy proxies of unobserved variables.
problem Learning robust linear models to handle interventions on unobserved variables with noisy proxies.
method Regularization term that balances in-distribution performance and robustness to interventions.
result Single proxy can create prediction optimal estimators under interventions of bounded strength.
Bayesian method estimates causal effects with proxy networks.
problem Estimating causal effects with only proxy measurements of a latent interference network.
method Structural causal model with Block Gibbs sampler and Locally Informed Proposals.
result Accurately estimates causal effects even with noisy proxy networks.
Kernel methods estimate causal effects with a single proxy for deterministic confounders.
problem Estimating causal effects with a single proxy for an unobserved confounder.
method Two kernel-based methods: two-stage regression and maximum moment restriction.
result Both kernel methods can consistently estimate the causal effect.
Study shows how adjusting for a binary proxy can bound causal effects.
problem Bounding causal effects with a binary confounder and proxy.
method Monotonicity assumption applied to a binary confounder and observed proxy.
result Adjusting for a proxy produces a measure of the effect between unadjusted and true measures.
New framework for choosing optimal proxy metrics from past experiments.
problem Difficult to measure long-term treatment effects in experiments.
method Statistical framework to define and construct optimal proxy metrics.
result Optimal proxy metric depends on experiment's sample size.
Discovering causal relations is fundamental to reasoning and intelligence. In particular, observational causal discovery algorithms estimate the cause-effect relation between two random entities X and Y, given n samples from P(X,Y). In this paper, we develop a framework to estimate the cause-effect relation bet…
New method mitigates bias without sensitive data using causal graph and variational autoencoder.
problem Lack of fairness strategies when sensitive attributes are not collected.
method SRCVAE framework based on causal graph for inferring a proxy sensitive attribute.
result Significant improvements in fairness metrics over existing methods.
We consider causal inference in the presence of unobserved confounding. We study the case where a proxy is available for the unobserved confounding in the form of a network connecting the units. For example, the link structure of a social network carries information about its members. We show how to effectively use the…
New methods identify causal effects without needing complete proxy variables.
problem Identifying causal effects in the presence of unmeasured confounders.
method Partial identification methods that do not require completeness of proxy variables.
result Obtain bounds on causal effects using available proxy variables.