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.
Develops a method to estimate treatment effects using noisy proxies over time.
problem Estimating individualized treatment effects from noisy proxies of confounders.
method Deconfounding Temporal Autoencoder (DTA) combining autoencoder and causal regularization.
result Improves treatment effect estimates by leveraging noisy proxies and learning hidden confounders.
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.
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.
The study examines fairness metrics in noisy covariate settings, providing theoretical guarantees.
problem Fairness measurement under noisy covariate information.
method Theoretical analysis of group fairness metrics using proxies for covariates.
result Characterization of weaker conditions for accurate fairness evaluation.
Estimating the individual treatment effect (ITE) from observational data is essential in medicine. A central challenge in estimating the ITE is handling confounders, which are factors that affect both an intervention and its outcome. Most previous work relies on the unconfoundedness assumption, which posits that all th…
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.
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.
Bayesian method mitigates negative transfer in unknown source data.
problem Negative transfer in transfer learning where target performance worsens after source data consideration.
method Proxy-informed robust method for probabilistic transfer learning (PROMPT).
result Negative transfer can be mitigated without prior knowledge of source data.
Paper tackles noise-robust domain adaptation in noisy environments.
problem Learning machines struggle with domain adaptation in noisy environments.
method The paper proposes offline curriculum learning and proxy distribution based margin discrepancy to mitigate label and feature noise.
result The proposed algorithm significantly outperforms state-of-the-art methods in noisy environments.
Learning individual-level causal effects from observational data, such as inferring the most effective medication for a specific patient, is a problem of growing importance for policy makers. The most important aspect of inferring causal effects from observational data is the handling of confounders, factors that affec…
Forecast reconciliation improves portfolio risk forecasts, especially when true covariance is known.
problem Improving portfolio risk forecasts using multivariate GARCH models.
method Combining univariate and multivariate forecasts with forecast reconciliation techniques.
result Forecast reconciliation improves over standard multivariate approaches, especially when true covariance is known.
GATs improve node regression on noisy graphs with provable advantage.
problem Improving node regression on graphs with noisy covariates and edges.
method Proposes a GAT designed for denoising proxy features in node regression.
result GAT achieves lower error in estimating regression coefficient and predicting responses.
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.
Study shows noisy data collection in ImageNet leads to biased model performance.
problem Noisy data collection in ImageNet datasets causes biased model performance.
method Human studies on ImageNet dataset to investigate design choices and their impact.
result Systematic misalignment between ImageNet benchmark and real-world task.
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.
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.
Causal Imitation Learning handles noisy measurements and distribution shifts.
problem Learning from noisy state observations and distributional shifts.
method Causal inference framework and adversarial RKHS learning.
result Improved robustness to distribution shifts compared to standard methods.
Local graph clustering improves with noisy labels, enhancing accuracy and performance.
problem Local graph clustering with noisy labels for node information.
method Constructing a weighted graph with noisy labels and using diffusion-based clustering.
result Diffusion in the weighted graph yields more accurate recovery of target clusters.
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.
Study finds on-chain data can proxy off-chain cryptocurrency pricing.
problem Develop methods to proxy off-chain cryptocurrency pricing using on-chain data.
method Graphical models, mutual information, and ensemble machine learning.
result A significant amount of pricing information is contained in on-chain data, but precise prices are hard to recover except on short time scales.
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.
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.
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.
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.
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…
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 …
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 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 study the flow of information and the evolution of internal representations during deep neural network (DNN) training, aiming to demystify the compression aspect of the information bottleneck theory. The theory suggests that DNN training comprises a rapid fitting phase followed by a slower compression phase, in whic…
Embodied cognition states that semantics is encoded in the brain as firing patterns of neural circuits, which are learned according to the statistical structure of human multimodal experience. However, each human brain is idiosyncratically biased, according to its subjective experience history, making this biological s…
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.
One of the biggest bottlenecks in a machine learning workflow is waiting for models to train. Depending on the available computing resources, it can take days to weeks to train a neural network on a large dataset with many classes such as ImageNet. For researchers experimenting with new algorithmic approaches, this is …
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.
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.
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 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.
Estimates causal effect using proxies in multi-domain settings.
problem Estimating causal effect in settings with unobserved confounders across domains.
method Proposes estimation techniques using proxy variables for discrete or categorical data.
result Proves identifiability and consistency of causal effect estimation.
Q-NETs use neural networks to estimate integrals of low-dimensional functions efficiently.
problem Estimating integrals of multidimensional functions with costly evaluations.
method Fixed neural networks (Q-NETs) that operate on proxy function parameters to calculate exact integrals over subsets of dimensions.
result Q-NETs can calculate integrals over any subset of dimensions without resampling or retraining the proxy.
PROBE optimizes best-arm identification with cheap proxies, improving sample complexity.
problem Fixed-confidence best-arm identification with costly rewards and correlated cheap proxies.
method PROBE uses control-variate adjustment and phase elimination to learn residual variance online.
result PROBE achieves oracle sample complexity up to a constant factor and additive calibration cost.
Two proxy methods for causal identification are compared.
problem Identifying causal effects in the presence of unmeasured variables.
method Bridge equation methods vs. array decomposition methods.
result Model restrictions and implications of assumptions differ between methods.
New method identifies latent treatment effects from proxy models.
problem Identifying heterogeneous treatment effects under unobserved confounding.
method Compressed observable operator and spectral analysis of treatment effects.
result Eigenvalues of the operator represent latent treatment effects.
Research suggests using deep learning for better recommendation systems.
problem Recommender systems rely on proxies for A/B testing, leading to random success.
method Advocates for using deep learning to improve recommendation performance.
result Deep learning can potentially optimize reward in recommendation systems.
New algorithms for hypothesis testing in high-dimensional data are shown to be effective under various noisy conditions.
problem Testing high-dimensional probability measures under noisy conditions.
method Low coordinate degree functions (LCDF) using Efron-Stein decomposition.
result LCDF can effectively test high-dimensional probability measures under noisy channels, with efficacy depending on scalar Fisher information.
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 …
Study identifies conditions for proxy adjustment in confounded binary treatment outcomes.
problem Average causal effect estimation with a non-differentially mismeasured binary confounder.
method Identifies conditions for proxy adjustment in the presence of a non-differentially mismeasured binary confounder.
result Adjusting for a non-differentially mismeasured binary proxy can improve estimation of the average causal effect.
Regulators require financial institutions to estimate counterparty default risks from liquid CDS quotes for the valuation and risk management of OTC derivatives. However, the vast majority of counterparties do not have liquid CDS quotes and need proxy CDS rates. Existing methods cannot account for counterparty-specific…