The paper examines how timing of observations affects causal discovery methods.
problem The sensitivity of causal discovery methods to mismatched observation timing.
method Empirical and theoretical analysis of classical and recent causal discovery methods.
result Causal discovery methods are sensitive to sampling rate and window length.
AI methods broaden signal discovery in scientific data.
problem Limited coverage of possible signals in model-dependent searches.
method Model-agnostic AI strategies for broad exploration.
result Enhanced discovery potential in experimental science.
This paper investigates the impact of dark pools on price discovery (the efficiency of prices on stock exchanges to aggregate information). Assets are traded in either an exchange or a dark pool, with the dark pool offering better prices but lower execution rates. Informed traders receive noisy and heterogeneous signal…
KEEL improves causal discovery with fuzzy knowledge and complex data.
problem Challenges in causal discovery due to prior knowledge, domain inconsistencies, and small sample sizes.
method Weakly-supervised fuzzy knowledge and data co-driven causal discovery method (KEEL).
result KEEL outperforms state-of-the-art methods in accuracy, robustness, and computational efficiency.
Scientific discovery is limited by hypothesis redundancy, and hybrid methods can exploit non-local exploration.
problem Limitation of scientific discovery due to hypothesis redundancy.
method Hybrid discovery systems combining structured local search with LLM-generated non-local proposals.
result Hybrid methods can exploit non-local exploration when three geometric conditions co-occur.
A new method enhances signal recovery with FDR control.
problem Challenging signal recovery in compressive sensing.
method Knockoff-guided compressive sensing framework with FDR control.
result Guaranteed FDR control leads to more accurate signal reconstruction.
Convolutional dictionary learning (CDL) estimates shift invariant basis adapted to multidimensional data. CDL has proven useful for image denoising or inpainting, as well as for pattern discovery on multivariate signals. As estimated patterns can be positioned anywhere in signals or images, optimization techniques face…
We release the largest public ECG dataset of continuous raw signals for representation learning containing 11 thousand patients and 2 billion labelled beats. Our goal is to enable semi-supervised ECG models to be made as well as to discover unknown subtypes of arrhythmia and anomalous ECG signal events. To this end, we…
A method uses non-autonomous equations to classify time signals efficiently.
problem Time signal classification with minimal parameters and high accuracy.
method Develops a framework using non-autonomous dynamical equations to classify time signals.
result The method achieves comparable accuracy with fewer parameters than existing methods.
Causal discovery algorithms infer causal relations from data based on several assumptions, including notably the absence of measurement error. However, this assumption is most likely violated in practical applications, which may result in erroneous, irreproducible results. In this work we show how to obtain an upper bo…
Study explores DNNs' reliance on existing vs. new features in physiological signals.
problem Understanding how deep neural networks discover new features in physiological signals.
method Proposes a method to remove hand-engineered features and force DNNs to learn new representations.
result DNNs often rediscover known features, but can also learn new ones.
Temporal Causal Prior-Data Fitted Networks (TCPFN) for industrial time series causal discovery
problem Estimating causal effects in industrial time series
method Temporal Causal Prior-Data Fitted Networks
result Zero-shot causal discovery with explicit reliability signals
These notes review six lectures given by Prof. Andrea Montanari on the topic of statistical estimation for linear models. The first two lectures cover the principles of signal recovery from linear measurements in terms of minimax risk. Subsequent lectures demonstrate the application of these principles to several pract…
GIT uses gradient estimators to target interventions for causal discovery.
problem Challenges in inferring causal structure from observational data.
method GIT uses gradient estimators to target interventions for causal discovery.
result GIT performs on par with competitive baselines, surpassing them in low-data regimes.
Generative AI improves stock selection by synthesizing features from diverse data sources.
problem Automating feature discovery in stock market data.
method Used large language models with retrieval-augmented generation and structured prompting to synthesize features from various data sources.
result AI-generated features consistently outperform baselines, with Sharpe improvements ranging from 14% to 91%.
New weight initialisation for ICNNs accelerates learning and improves generalization.
problem Lack of effective initialisation strategies for ICNNs due to their unique weight and activation properties.
method Derived a principled weight initialisation by generalizing signal propagation theory for ICNNs with non-negative weights.
result Principled initialisation effectively accelerates learning and leads to better generalization in ICNNs.
Machine learning detects foreign stock market signals for U.S. companies.
problem Detecting value-relevant foreign information for U.S. companies.
method Training over 100,000 models to capture stock-specific relationships.
result Foreign signals predict U.S. stock returns, especially in emerging markets.
RECLAIM discovers causal graphs in cyclic, noisy systems.
problem Discovering causal relationships in cyclic, noisy systems.
method RECLAIM uses EM with residual normalizing flows to handle cycles and noise.
result RECLAIM effectively discovers causal graphs in both synthetic and real-world datasets.
The false discovery rate (FDR)---the expected fraction of spurious discoveries among all the discoveries---provides a popular statistical assessment of the reproducibility of scientific studies in various disciplines. In this work, we introduce a new method for controlling the FDR in meta-analysis of many decentralized…
Detecting weak clustered signal in spatial data is important but challenging in applications such as medical image and epidemiology. A more efficient detection algorithm can provide more precise early warning, and effectively reduce the decision risk and cost. To date, many methods have been developed to detect signals…
We introduce an interactive market setup with sequential auctions where agents receive variegated signals with a known deadline. The effects of differential information and mutual learning on the allocation of overall profit \& loss (P\&L) and the pace of price discovery are analysed. We characterise the signal-based e…
Study reveals how investor flows impact stock prices, especially during herding episodes.
problem Understanding how information transmits through prices and why it breaks down.
method Combining regularized deconvolution with Hawkes process analysis.
result Institutional price impact deteriorates sharply during herding episodes in small-cap stocks, while large-cap stocks maintain resilience.
CauScale efficiently discovers causal relationships in large graphs.
problem Efficiency bottlenecks in causal discovery for large graphs.
method Neural architecture with reduction unit and tied attention weights.
result Achieves 99.6% mAP on in-distribution data and 84.4% on out-of-distribution data.
FoundCause: Causal Discovery with Latent Confounders from Observational Data
problem Causal discovery from observational data
method FoundCause, an amortized causal discovery model trained on synthetic data
result FoundCause outperforms classical and amortized methods on real-world datasets
Binary encoding enables neural networks to extrapolate periodic functions.
problem Extrapolating periodic functions without prior knowledge of their form.
method Normalized Base-2 Encoding (NB2E) for continuous numerical values.
result MLPs using NB2E can successfully extrapolate diverse periodic signals.
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…
Robust CD method for real-world time series with power-law distributions.
problem Challenges in causal discovery due to noise sensitivity.
method Power-law spectral feature extraction for robust CD.
result Consistently outperforms state-of-the-art alternatives on real-world datasets.
We describe a strategy for constructing a neural network jet substructure tagger which powerfully discriminates boosted decay signals while remaining largely uncorrelated with the jet mass. This reduces the impact of systematic uncertainties in background modeling while enhancing signal purity, resulting in improved di…
High-performing equity factor with Sharpe ratio above 13 out-of-sample.
problem Hidden cross-sectional predictability in stock returns.
method Regime-conditional signal activation combining value and short-term reversal signals.
result Annualized returns of 158.6% with 12.0% volatility, strong performance out-of-sample.
DeepSIBA predicts biological effects of chemical structures using graph neural networks.
problem Predicting biological effects of chemical structures for drug discovery.
method Siamese Graph Convolutional Neural Networks for structure-biological effect mapping.
result Highly accurate predictions of biological effects for structurally dissimilar compounds.
AI-driven investment strategies self-defeat at scale due to signal crowding and erosion.
problem Excess returns from AI-driven investment strategies diminish at scale due to signal crowding and erosion.
method Theoretical model and empirical validation using SEC Form 13F filings and hedge fund return dynamics.
result The alpha half-life of signals decreases significantly with AI adoption, leading to diminishing returns.
FactorMiner discovers financial alpha factors with low redundancy.
problem Finding novel financial alpha factors in a vast search space.
method Modular Skill Architecture and Experience Memory to distill and guide exploration.
result FactorMiner constructs a diverse library of high-quality factors with competitive performance.
A new method uses LLMs to discover causal pathways that affect fairness in machine learning.
problem Discovering fairness-relevant causal pathways in the presence of noise and confounding.
method Hybrid LLM-guided causal discovery framework combining active learning and dynamic scoring.
result LLM-guided methods, including the proposed active, dynamically scored variant, outperform baselines in recovering fairness-relevant structure under noisy conditions.
Study uses LLMs to categorize financial tweets, revealing useful sentiment signals.
problem Discovering meaningful sentiment signals from unstructured financial social media data.
method Leveraged LLMs to automatically label financial tweets with event categories and aligned with returns.
result Certain event labels consistently yield negative alpha, with statistically significant Sharpe ratios and information coefficients.
This paper establishes the existence of observable footprints that reveal the "causal dispositions" of the object categories appearing in collections of images. We achieve this goal in two steps. First, we take a learning approach to observational causal discovery, and build a classifier that achieves state-of-the-art …
Recently, deep learning approaches with various network architectures have achieved significant performance improvement over existing iterative reconstruction methods in various imaging problems. However, it is still unclear why these deep learning architectures work for specific inverse problems. To address these issu…
Alpha2 discovers logical formulaic alphas using deep reinforcement learning.
problem Discovering interpretable formulaic alphas for better trading strategies.
method Formulating alpha discovery as program construction, using deep reinforcement learning to navigate the search space.
result Empirical experiments show Alpha2 identifies diverse, logical, and effective alphas improving trading strategy performance.
Stein-Encoder isolates genetic signals in multi-modal biomedical data.
problem Integration of high-dimensional genomic data with clinical data obscures genetic predictive impact.
method White-box supervised framework using Stein's method and residualization.
result Stein-Encoder improves predictive accuracy and reveals specific biological mechanisms.
Unified method for simultaneous denoising and clustering.
problem Clustering noisy signals.
method Sparse convex wavelet clustering with fusion and group-sparse penalties.
result Unified approach that denoises and clusters simultaneously.
LANCA uses ANM to learn latent causal factors without supervision.
problem Learning latent causal factors without supervision.
method LANCA employs a deterministic Wasserstein Auto-Encoder coupled with a differentiable ANM Layer.
result LANCA outperforms baselines on physics and photorealistic environments.
New method uses DNN for genetic variant identification, controlling randomness and improving interpretability.
problem Challenges in interpreting deep neural networks for genetic variant identification.
method Interpretable neural network model with controlled variable selection using ensembling, knockoffs, and de-randomization.
result The proposed method leads to more discoveries compared to conventional methods.
GRIP2 improves deep learning feature selection robustness in correlated and noisy data.
problem Identifying predictive features in correlated and noisy data.
method Integrates first-layer feature activity over a two-dimensional regularization surface to control sparsity and geometry, using efficient block-stochastic sampling.
result Demonstrates improved robustness and power in high correlation and low signal-to-noise ratio regimes.
New method combines simulations and data for anomaly detection.
problem Detecting new particle signals without direct evidence.
method Hybrid approach using reweighting and interpolation.
result Improved background estimation and classification.
Non-negative matrix factorization (NMF) is a new knowledge discovery method that is used for text mining, signal processing, bioinformatics, and consumer analysis. However, its basic property as a learning machine is not yet clarified, as it is not a regular statistical model, resulting that theoretical optimization me…
Quantifying the value of data is a fundamental problem in machine learning. Data valuation has multiple important use cases: (1) building insights about the learning task, (2) domain adaptation, (3) corrupted sample discovery, and (4) robust learning. To adaptively learn data values jointly with the target task predict…
In unsupervised learning, dimensionality reduction is an important tool for data exploration and visualization. Because these aims are typically open-ended, it can be useful to frame the problem as looking for patterns that are enriched in one dataset relative to another. These pairs of datasets occur commonly, for ins…
Protein Thoughts interprets protein interactions with clear reasoning, improving prediction accuracy.
problem Lack of mechanistic justification in protein-protein interaction predictions.
method Interpretable search problem reformulation, hypothesis-guided entropy-regularized Tree-of-Thoughts search, embedding-space flow matching.
result Improves mean best-binder rank from 47.7 to 11.2 on SHS148k benchmark.
CDA framework infers channel influence from aggregated data without user identifiers.
problem Lack of user-level path data due to privacy regulations and platform restrictions.
method CDA integrates PCMCI for causal discovery and Structural Causal Model for effect estimation.
result CDA achieves strong accuracy in estimating channel influence, even under structural uncertainty.