Paper proposes a new method to secure power system operation using machine learning.
problem Ensuring secure power system operation under high uncertainty.
method Embedding disjunctive rules from Decision Trees in an optimization framework using GDP and a two-step search method.
result The method achieves efficient system control at a marginal increase in system price compared to an oracle model.
This paper proposes a generic classification system designed to detect security threats based on the behavior of malware samples. The system relies on statistical features computed from proxy log fields to train detectors using a database of malware samples. The behavior detectors serve as basic reusable building block…
Cross-border equity and long-term debt securities portfolio investment networks are analysed from 2002 to 2012, covering the 2008 global financial crisis. They serve as network-proxies for measuring the robustness of the global financial system and the interdependence of financial markets, respectively. Two early-warni…
CUTS removes corruption from models without clean data, improving utility and security.
problem Removing corruption from models without access to clean training data.
method CUTS uses a proxy set to amplify corruption and subtract it from model weights.
result CUTS recovers a large fraction of lost utility and nearly eliminates attacks with minimal damage.
New snooping attacks exploit deep RL without access to environment.
problem Security vulnerabilities in deep reinforcement learning.
method Proposes snooping threat models and attacks on RL agents.
result Adversaries can launch attacks without interacting with the environment.
Fast, format-agnostic web content detection for security.
problem Detecting malicious web content efficiently and accurately.
method Deep learning on static HTML tokens, avoiding complex parsing.
result 97.5% detection rate at 0.1% false positive rate.
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 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.
Predicting delayed outcomes in recommender systems using proxies.
problem Predicting delayed outcomes in recommender systems.
method Formalized as adversarial, delayed online learning problem; proposed Factored Forecaster (FF) and Residual Factored Forecaster (RFF) neural network architectures.
result Residual Factored Forecaster (RFF) outperforms direct forecaster and Factored Forecaster (FF) in predicting human behavior.
Paper detects proxies in linear regression models causing discrimination.
problem Discrimination in machine learning models using proxies for protected attributes.
method Formulated a definition of proxy use, identified proxies via second-order cone program, and extended to justified business necessity.
result Proxies in linear regression models can be efficiently identified and removed to reduce discrimination.
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.
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.
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.
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.
Improves fairness without protected group labels by using proxy groups.
problem Lack of protected group labels makes it hard to improve fairness.
method Investigates improving fairness metrics for proxy groups and tests their effectiveness.
result Proxy fairness strategy works well in practice but depends on fairness metric choice.
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.
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.
Proxy measures in competitive systems lead to corruption and inefficiency.
problem Corruption of proxy measures in competitive systems.
method Agent-based model integrating insights from complex systems, contest theory, behavioral economics, and cultural evolution.
result Proxy measures lead to corruption and convergence towards an equilibrium level of corruption.
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.
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.
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.
Predictive analytics uses proxy data when true data is scarce, but this can lead to errors.
problem Bias in using proxy data for predictive analytics.
method Proposes a two-step estimator using high-dimensional statistics to combine proxy and true data.
result Achieves better predictive accuracy with less true data.
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.
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.
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.
Optimizes a portfolio for an investor preferring accepted securities over a reference security.
problem Investor preference for a set of securities over a reference security with constraints.
method Mean-variance optimization with Sharpe Ratio performance measurement.
result Derives an optimal portfolio that maximizes returns while minimizing risk.
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.
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.
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.
FRD protects privacy in distributed RL by sharing proxy experience memory.
problem Privacy violation in exchanging experience memory in distributed RL.
method Proposes FRD framework using proxy experience memory.
result Numerical evaluation shows FRD is effective and performance depends on proxy memory structure.
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…
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.
Study shows Bitcoin security tied to mining rewards and prices.
problem Understanding Bitcoin security's dependency on market outcomes.
method Used ARDL approach with daily blockchain and Bitcoin data from 2014-2019.
result Bitcoin security outcomes linked to Bitcoin price and mining rewards.
Algorithm identifies best arm with biased proxy and selective ground truth audits.
problem Fixed-confidence best-arm identification with biased proxy and selective ground truth.
method Propensity-weighted estimator and adaptive auditing algorithm.
result Plug-in Neyman rule achieves near-oracle audit efficiency.
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.