Scattering transform improves note onset detection and instrument recognition in music transcription.
problem Note onset detection and instrument recognition in music transcription.
method Multiscale scattering operators applied to MIDI-driven datasets and real musical pieces.
result Scattering transform outperforms other sound representations for note onset detection and instrument recognition.
DeepPhase uses deep learning to recognize surgical phases in cataract surgery videos.
problem Automating surgical workflow analysis for better standardization and post-surgical assessment.
method Deep learning for instrument detection and phase classification in cataract surgery videos.
result DeepPhase models achieve 99% accuracy in instrument detection and 78% in phase recognition.
Study uses neural networks to classify musical instruments from audio samples.
problem Classifying musical instruments from audio samples.
method Trained an Artificial Neural Network on audio samples transformed to frequency domain. Analyzed features in time and frequency domains.
result Accuracy decreased when using only attack features or initial 100 Hz.
SVM classifier outperforms other models in classifying musical instruments from IRMAS data.
problem Musical instrument classification from audio signals.
method Implemented supervised (SVM) and unsupervised (Hierarchical Clustering) learning algorithms.
result SVM classifier achieved 79% accuracy on IRMAS data.
This paper proposes a method for instrument classification in polyphonic music using monophonic data.
problem Instrument classification in polyphonic music from monophonic data.
method Data augmentation techniques including overlaying audio segments of the same genre, pitch, and tempo synchronization. Convolutional Neural Networks used for classification.
result An ensemble of VGG-like classifiers trained on non-augmented, pitch-synchronized, tempo-synchronized and genre-similar excerpts achieved above 80% LRAP.
Proposes Moment Exchange to use moments in image recognition models, improving generalization.
problem Discarding moments in image recognition models reduces stability and training time.
method Moment Exchange: replaces moments of learned features with another image's moments and interpolates labels.
result Improves generalization of recognition models across multiple datasets.
Estimates long-term effects using past experiments as instruments with many weak instruments.
problem Estimating long-term causal effects with limited short-term outcomes and many weak instruments.
method Nonparametric instrumental variable inference with many weak instruments, using past experiments as instruments.
result Automatic debiased machine learning estimators for linear functionals of the structural function and its minimum-norm projection are efficient in the many-weak-instruments regime.
Proposes COALA method for learning audio representations aligned with tags.
problem Lack of annotated data for high-performance audio representation learning.
method Aligns latent representations of audio and tags using a contrastive loss.
result Audio embedding model captures both acoustic and semantic characteristics.
ZNet learns instrumental representations from covariates for causal inference.
problem Lack of valid instruments in observational studies.
method Representation learning approach that constructs instrumental representations from observed covariates.
result ZNet enables IV-based estimation without explicit instruments.
TSCI estimates treatment effects using machine learning and data-adaptive methods for invalid instruments.
problem Estimating treatment effects with invalid instruments.
method Two-stage algorithm: first stage uses machine learning for nonlinearities, second stage selects and projects out instrument violations.
result Effective treatment effect estimation even with invalid instruments.
Researchers develop methods for causal inference with imperfect instrumental variables.
problem Quantifying cause and effect relationships with imperfect instrumental variables.
method Established a quantitative relationship between violations of instrumental inequalities and minimal measurement dependence, providing adapted inequalities valid in the presence of relaxed measurement dependence.
result Adapted inequalities for average causal effect in instrumental scenarios with binary outcomes, addressing violations of instrumental inequalities.
Paper proposes CIV estimator for categorical instruments in small sample settings.
problem Estimation with categorical instruments in settings with few observations per category.
method CIV estimator leveraging regularization assumption for latent categorical variable.
result CIV estimator is asymptotically normal, efficient, and semiparametrically efficient under homoskedasticity.
New method uses few instruments to estimate complex causal effects.
problem Estimating causal effects with limited instruments in high-dimensional settings.
method Sequentially selects and combines instruments to estimate the treatment effect.
result Can reliably recover the treatment effect's projection onto the instrumented subspace.
Model disentangles timbre and pitch for musical instruments.
problem Learning disentangled representations of musical instrument sounds.
method Gaussian mixture variational autoencoders with two separate encoders for timbre and pitch.
result Model successfully disentangles timbre and pitch, enabling controllable synthesis and transfer.
Machine learning extracts features from illiquid corporate bond yields.
problem Extracting features from illiquid corporate bond yields.
method Applied Denoising Autoencoder algorithm to learn features from liquid market data.
result Trained machine learning algorithm's results compared with 2D interpolation.
Paper develops a new estimator for panel data with endogenous treatments, improving causal inference.
problem Challenges in causal inference for static panel data with endogenous treatments and confounding variables.
method Develops Double Machine Learning (DML) estimator for static panel models with endogenous treatments (panel IV DML). Introduces weak-identification diagnostics.
result Panel IV DML estimator improves estimation accuracy and delivers more reliable inference under weak identification.
DFIV uses deep neural nets to learn nonlinear features in IV regression.
problem Learning causal relationships from observational data with nonlinear interactions.
method DFIV trains deep neural nets to define nonlinear features on instruments and treatments, alternating training to compose stages 1 and 2.
result DFIV outperforms state-of-the-art methods on IV benchmarks and off-policy policy evaluation.
L2Boosting improves treatment effect and IV estimation in high-dimensional data.
problem High-dimensional data with many parameters compared to observations.
method L2Boosting with componentwise least squares algorithm for regression problems.
result L2Boosting is a competitive method for inference in high-dimensional settings.
Learning a causal effect from observational data is not straightforward, as this is not possible without further assumptions. If hidden common causes between treatment X and outcome Y cannot be blocked by other measurements, one possibility is to use an instrumental variable. In principle, it is possible under some…
Valid causal inference with invalid instruments using majority or modal valid relationships.
problem Estimating causal effects in the presence of unobserved confounding and invalid instruments.
method Ensemble of instrumental variable estimators to estimate the modal prediction, achieving accurate estimates of conditional average treatment effects.
result Valid causal inference can be achieved with a majority or modal valid instrument-response relationship.
Deep transfer learning identifies and removes glitches in LIGO data.
problem Identifying and removing glitches in LIGO data to detect gravitational waves.
method Deep transfer learning combined with unsupervised clustering.
result Truncated deep networks achieve state-of-the-art accuracy in glitch classification.
Bayesian nonparametric machine learning improves instrumental variable inference.
problem Estimating causal effects with nonlinear relationships.
method Bayesian Additive Regression Trees (BART) for estimating functions and Dirichlet Process mixtures for error terms.
result Dramatic improvements in inference with nonlinear data, no manual tuning required.
Proposes new financial instruments for better market hedging.
problem Market crashes detection and hedging.
method Introduces perpetual derivatives for hedging jump-diffusion and stochastic volatility models.
result New financial instruments can serve as an early warning system for market crashes.
The paper uses graph learning to detect valid instruments in high-dimensional data for house pricing.
problem Endogeneity bias and invalid instrument validation in high-dimensional data.
method Merge variable selection algorithms and probabilistic graphs to estimate house prices and causal structure.
result Efficient data-driven instrument selection and invalid instrument purge in high-dimensional data.
Proposes TSCI method to infer causal effects with weak or invalid instruments using machine learning.
problem Causal inference with weak or invalid instrumental variables.
method Two-stage curvature identification (TSCI) using machine learning.
result Asymptotically unbiased and Gaussian estimator for causal effects.
Simplifies IV regression for high-dimensional instruments.
problem Nonlinear instrumental variable regression with high-dimensional instruments.
method Combines kernelized IV methods with an adaptive regression algorithm.
result Faster convergence and adaptability to feature dimensionality.
The study examines fairness issues in recidivism prediction instruments, showing how disparate impact can occur.
problem Discriminatory bias in recidivism prediction instruments.
method Analysis of fairness criteria and demonstration of disparate impact.
result Disparate impact can arise when instruments fail to satisfy error rate balance criteria.
New method for personalized pricing using invalid instrumental variables.
problem Personalized pricing under endogeneity with limited standard methods.
method PRINT method for continuous treatment, solving conditional moment restrictions.
result Established optimal pricing strategy under endogeneity with invalid instrumental variables.
GIV methodology extends instrumental variable estimation for high-dimensional data.
problem Estimating structural parameters in high-dimensional models with endogeneity and latent factors.
method Extends GIV methodology to large N and T, treats factors and loadings as unknown, and uses additional instruments for efficiency.
result Efficiency gains and negligible sampling errors in estimated instrument and factors.
Semi-supervised singing voice separation using synthetic mixtures.
problem Singing voice separation with limited labeled data.
method Trains a single mapping function g on synthetic mixtures of singing and instrumental music.
result Performance comparable to fully supervised methods, better than semi-supervised alternatives.
Study shows annotation instrument design affects model performance in hate speech detection.
problem Impact of annotation instrument design on model performance in hate speech detection.
method Collected annotations from five experimental conditions of an annotation instrument, fine-tuned BERT models on each dataset, evaluated performance on holdout portion.
result Significant differences in model performance and annotations across conditions.
Estimates linear model from noisy covariates and instruments using spectral regularization.
problem Estimating a linear model from many noisy covariates and instruments.
method Two-stage least squares with spectral regularization of canonical correlations.
result Upper and lower bounds on estimation error, proving optimality of the method with noisy data.
New method improves IV estimation with many weak and invalid instruments.
problem Identification in linear IV models with unknown validity.
method Non-convex penalized approaches, surrogate sparsest penalty.
result Advantages over other IV estimators in selection consistency and weak IV strength conditions.
New method exploits independence in instrumental variable models for better causal inference.
problem Identify causal functions in the presence of unobserved confounders.
method HSIC-X method that exploits independence between response, hidden confounders, and instruments.
result The method provides better finite sample results and is invariant to distributional shifts.
Develops methods to identify and estimate causal effects with instrumental variables.
problem Causal inference with confounded treatment assignment and unobserved variables.
method General nonparametric causal framework, debiased machine learning, semiparametric theory.
result Consistent and asymptotically normal estimators for average treatment effect.
Kernel method improves instrumental variable regression rates.
problem Nonparametric instrumental variable regression with weak instruments.
method Kernel-based two-stage least-squares method, strong L2 convergence analysis. result Minimax optimal rates for instrumental regression under standard assumptions.
A semiparametric test evaluates instrument validity and complier characteristics.
problem Evaluating the validity of instruments and complier characteristics.
method Semiparametric test, doubly robust moment, machine learning update.
result Validates instrument validity and complier characteristics.
This paper theoretically explains and validates a deep neural network approach to IV estimation.
problem Endogeneity issues in empirical applications, especially in the presence of omitted variables, measurement error, or simultaneous causality.
method A two-stage estimator using deep neural networks in a linear instrumental variables model, with a latent structural assumption on the reduced form equation.
result The second-stage estimator achieves the semiparametric efficiency bound, with a smaller estimation error and requiring weaker conditions on the smoothness of optimal instruments.
Long-term debt instruments can't be deposit substitutes due to mismatched features.
problem Long-term debt instruments cannot function as deposit substitutes due to their maturity and capital preservation.
method Applied fundamental theory of bond values to 'PEACe Bonds' to show incompatibility.
result Long-term debt instruments cannot be deposit substitutes due to their mismatched features.
The paper addresses statistical estimation in MDPs with confounders using instrumental variables.
problem Statistical estimation of value functions in MDPs with unobservable confounders.
method Two-stage estimator based on instrumental variables for confounded linear MDPs.
result Established statistical properties of the two-stage estimator, including error bounds and asymptotic normality.
New algorithms for IV regression with streaming data, avoiding matrix inversions.
problem Instrumental variable regression with streaming data.
method Viewing IV regression as a stochastic optimization problem, developing algorithms that avoid matrix inversions and mini-batches.
result Rates of convergence of order O(logT/T) and O(1/T1−ι) for linear models. Proposes a method to estimate treatment effects using instruments.
problem Estimating treatment effects from observational data is challenging when unconfoundedness is violated.
method Leverages instruments to estimate bounds on conditional average treatment effect (CATE) through a mapping to a discrete representation space and a two-step procedure.
result Demonstrates theoretical validity and reduced estimation variance in finite-sample settings.
Improves financial instrument pricing using neural networks.
problem Financial instrument pricing within Black-Karasinski model.
method Enhances path-integral approximation with neural networks.
result Demonstrates superior outcomes for multiple calibrations.
Recidivism prediction instruments provide decision makers with an assessment of the likelihood that a criminal defendant will reoffend at a future point in time. While such instruments are gaining increasing popularity across the country, their use is attracting tremendous controversy. Much of the controversy concerns …
Deep learning for integrating diverse clinical measurements.
problem Combining data from different measurement instruments in longitudinal clinical registries.
method Domain adaptation using deep learning for mapping items from different instruments.
result Domain adaptation can recover latent trajectories even with limited data and misalignment.
Transformer adapts to multi-instrumental music generation using NES and Lakh MIDI datasets.
problem Generating multi-instrumental music scores with Transformers.
method Adapted Transformer architecture to multi-instrumental setting, used NES-MDB and Lakh MIDI datasets for pre-training.
result Pre-training with Lakh MIDI dataset improves performance for multi-instrumental music generation.
Modeling trading costs for correlated instruments to improve execution strategies.
problem Incorrect estimation of liquidity and suboptimal execution strategies due to neglecting cross-impact effects.
method Extending the linear propagator model to the multivariate case for correlated instruments, calibrating a cost model free of arbitrage and manipulation.
result Synchronizing the execution of correlated contracts is crucial for accurate liquidity estimation and optimal execution strategies.
Bayesian method for robust causal inference using many-dimensional instrumental variables.
problem Intractable model space and uncertainty in selecting valid instrumental variables.
method Bayesian model averaging over promising instrumental variable models with weaker assumptions.
result Efficient and robust causal effect estimation in many-dimensional data.