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Open problems in Classifier & causal discovery

Benchmarking agentic black-box optimization1 paper raises it · 1 addresses it
Lack of standardized cross-domain benchmarks makes it hard to compare methods and isolate design choices in agentic BBO.
- A Closer Look at Agentic BBO: Benchmarking LLM Agents for Black-Box Optimizationraises · addresses
Identifying individual world latents without reconstruction1 paper raises it · 1 addresses it
Existing methods fail to recover individual latents without reconstruction, decoders, or labels, limiting interpretability and causal analysis.
- DSReg: Provably Recovering Individual World Latents without Reconstructionraises · addresses
Long-tailed recognition with instance-wise confusion1 paper raises it · 2 address it
Class-wise logit adjustments ignore instance-level confusion, harming minority class performance in imbalanced datasets.
- CILA: Confusion-based Instance-wise Logit Adjustment for Long-Tailed Recognitionraises · addresses
- Using Set Learning to Improve Set-Valued Classifiers on Long-tailed Dataaddresses
Data augmentation under label noise1 paper raises it · 1 addresses it
Theoretical understanding of when augmentation helps or harms generalization in noisy-label settings is missing.
- Understanding Data Augmentation under Label Noiseraises · addresses
Causal attribution with scarce incident data1 paper raises it · 1 addresses it
Exact attribution of alarm-score changes in causal systems is combinatorial and requires incident data, limiting practicality.
- What Moved the Alarm Score? Causal Attribution with Exact Computation and Scarce-Incident Estimationraises · addresses

Named by an AI from these papers' abstracts.

Recently funded

Topics in this field

Active contributors

Activity · 12 weeks

Coming with more sources: repos

Top tools here

Open problems in Classifier & causal discovery

Benchmarking agentic black-box optimization1 paper raises it · 1 addresses it
Lack of standardized cross-domain benchmarks makes it hard to compare methods and isolate design choices in agentic BBO.
- A Closer Look at Agentic BBO: Benchmarking LLM Agents for Black-Box Optimizationraises · addresses
Identifying individual world latents without reconstruction1 paper raises it · 1 addresses it
Existing methods fail to recover individual latents without reconstruction, decoders, or labels, limiting interpretability and causal analysis.
- DSReg: Provably Recovering Individual World Latents without Reconstructionraises · addresses
Long-tailed recognition with instance-wise confusion1 paper raises it · 2 address it
Class-wise logit adjustments ignore instance-level confusion, harming minority class performance in imbalanced datasets.
- CILA: Confusion-based Instance-wise Logit Adjustment for Long-Tailed Recognitionraises · addresses
- Using Set Learning to Improve Set-Valued Classifiers on Long-tailed Dataaddresses
Data augmentation under label noise1 paper raises it · 1 addresses it
Theoretical understanding of when augmentation helps or harms generalization in noisy-label settings is missing.
- Understanding Data Augmentation under Label Noiseraises · addresses
Causal attribution with scarce incident data1 paper raises it · 1 addresses it
Exact attribution of alarm-score changes in causal systems is combinatorial and requires incident data, limiting practicality.
- What Moved the Alarm Score? Causal Attribution with Exact Computation and Scarce-Incident Estimationraises · addresses

Named by an AI from these papers' abstracts.

Recently funded

Topics in this field

classifier · supervised · machine learning183 papers42 this week
causal discovery · causal inference · causal models123 papers18 this week
conformal prediction · split conformal prediction · aware conformal71 papers14 this week
bayesian optimization · bayesian optimization bo · black box optimization93 papers12 this week

Topics in this field

Active contributors · 90 days

Yujia ZhengDSReg: Provably Recovering Individual World Latents without Reconstruction4
Gunwoong ParkLocal Fisher Information Enables Sparse Causal Discovery4
Aapo HyvärinenIdentifying Dependent Components from Multi-domain Linear Mixtures3
Sara MagliacaneIdentifying Dependent Components from Multi-domain Linear Mixtures3
Stefan FeuerriegelDebiased Prior-Data Fitted Networks for Amortized Bayesian Semi-Parametric Inference3
Isabel ValeraDeDiCa: A Scalable Deconfounding Discrete Causal Generative Model3

Active contributors

Activity · 12 weeks

Coming with more sources: repos

Recent papers

All in Research →

arXiv cs.LGOct 4
Measuring Learned Monotone Temporal Aggregation at Matched Admissibility
Yew Lee Tan
Risk regulation imposes directional constraints on scores; we adopt their strict per-input form -- the score monotone non-decreasing in every exposure input -- as a normative commitment. Deployed pipelines -- monotone hand-crafted aggregates feeding sign-constrained gradient boosting -- already satisfy it by composition, so constrained-versus-unconstrained comparisons price a guarantee the incumbent has for free. We instead hold admissibility fixed on both sides and measure what learning the aggregation is worth. Our instrument is a recurrent network whose state is classical risk statistics (an exponentially weighted moving average and a high-water mark with learned transforms), monotone by construction in every input and per MC-dropout sample. The central finding, by functional regression, is a subsumption boundary: a learned monotone channel reproduces the geometrically weighted separable family of hand-crafted statistics, one channel per member, to Spearman $ρ\ge 0.996$, approximates window statistics with measurable ceilings, and fails at consecutivity ($ρ= 0.924$) and time localization (0.628), both structural, and at the exposure floor (0.829), a learnability boundary. One…
PDF arXiv
arXiv cs.LGOct 4
An Information-Theoretic Evaluation Framework for Benchmark and Model Diagnosis in Knowledge Tracing
Houru Jiang, Zixi Wang, Tengteng Cheng, Xueyi Li, Mingliang Hou +4
Knowledge tracing (KT) models are predominantly evaluated using aggregate metrics such as area under the curve (AUC) and accuracy. However, these global scores obscure where the remaining errors originate and fail to indicate whether a benchmark is approaching saturation. While estimating a global theoretical performance limit is challenging in realistic KT settings, it is possible to quantify local predictability. To address this, we propose an information-theoretic evaluation framework for KT benchmark diagnosis. We use Context Tree Weighting (CTW) on item-response histories and current-item queries as an operational causal uncertainty coordinate, while distinguishing it from the unobserved Local Irreducible Uncertainty (LIU) under the full KT information set. By projecting predictions onto this shared uncertainty coordinate, we evaluate model performance gains across distinct entropy bands rather than only at the global level. Comprehensive evaluations on NIPS Task 3/4 and Algebra 2005 reveal that model improvements are highly non-uniform. Modern KT models show substantial gains in high-entropy regions, and additional item-aware references, log-loss, and equal-frequency…
PDF arXiv
arXiv cs.LGOct 4
Learning under Localized Minority Imbalance
Amin Hosseininasab, Steven M. Shugan
Class-imbalance methods implicitly assume that the minority class is uniformly undersampled relative to the majority class. However, in many real-world settings, minority instances may be disproportionately under-observed in certain regions of the feature space. For example, small businesses that go bankrupt may disappear from records, while those that survive remain visible, making bankruptcy appear less common among small firms than it actually is. This gives rise to localized minority imbalance (LMI), a challenge that is often overlooked and extends beyond general class-count imbalance. We show that under LMI, existing imbalance mitigation techniques can fit observation-induced biases in the training data and generalize poorly to under-observed regions of the true minority distribution. To address this, we propose a tree-based stratified approach that recursively partitions the feature space with the goal of reducing within-stratum LMI distortion. For each resulting stratum, we pair its majority instances with the full observed minority set and train a base classifier to create an ensemble. Extensive experiments over benchmark tabular datasets simulated with LMI show that our…
PDF arXiv
arXiv cs.LGOct 3
Uncertainty in Representation Learning on Knowledge Graphs
Yuqicheng Zhu
Knowledge graph embedding (KGE) methods represent entities and predicates in continuous vector spaces to infer missing knowledge. Despite strong benchmark performance, their predictions often lack principled reliability guarantees, limiting their use in high-stakes applications. Moreover, uncertainty arises throughout the KGE pipeline, from incomplete or probabilistic input knowledge to stochastic training and prediction. This thesis systematically investigates three sources of uncertainty in KGE: knowledge uncertainty, arising from incomplete, noisy, or probabilistic input knowledge; algorithmic uncertainty, induced by randomness in model training; and predictive uncertainty, concerning the reliability of model outputs. To address algorithmic uncertainty, the thesis demonstrates that models trained under identical settings can produce substantially different predictions and introduces a voting-based aggregation framework to mitigate this instability. To quantify predictive uncertainty, it adapts conformal prediction to KGE, constructing answer sets with distribution-free coverage guarantees and extending them to provide predicate-conditional reliability guarantees. To support…
PDF arXiv
arXiv cs.LGOct 3
EasyClassifier: Honest, Reproducible Machine-Learning Classification for Researchers Who Do Not Program
Ahmad B. A. Hassanat, Ghada A. Altarawneh
Machine-learning classification is now used across medicine, the social sciences, economics, education and engineering, very often by researchers who do not program. Two errors recur in such work: preprocessing learned on all rows before cross-validation (data leakage), and reporting the cross-validation score of the classifier that won a comparison (selection bias). Both make published scores optimistic. We present EasyClassifier, an open-source Python package that guides a user from a CSV or Excel file to a finished analysis through a sequence of plain-language, multiple-choice questions, with no code. It makes the correct procedure the default rather than an option: every preprocessing step is learned inside each training fold; the selected classifier receives a separate final score from nested cross-validation (up to 2,000 rows), or an untouched 20% test set; classifiers run with fixed default parameters and fixed random seeds; and every run writes a report with a ready-to-adapt Methods paragraph, the references to cite, and figures prepared for publication. On ten public datasets from seven fields and a random-label control, split in half so that one half served as an…
PDF arXiv
arXiv cs.LGOct 3
Coupling Noisy Pairwise Knowledge to the DAG Posterior for Causal Discovery
Guoliang Xu, James E Corter
External causal reports can improve structure learning from limited observations, but their reliability varies across sources and variable pairs. We introduce HB-NoisyKG, a Bayesian framework that combines observational data with repeated causal reports from sources such as large language models. Each report is a noisy observation of a direct pair state implied by one DAG. A feature-conditioned Beta prior pools information about pair reliability, and a shared error matrix captures systematic mistakes. Alternating inference uses the graph posterior to refine reliability estimates, which determine how reports influence subsequent graph updates. The report likelihood uses only graph pair-state marginals, so the same observation layer supports discrete and continuous likelihoods in graph-only and joint inference. Against an 80-restart no-KG baseline, HB uses at most 80 total restarts and lowers mean SHD from 22.39 to 16.06 on five discrete benchmarks. On a physical light tunnel with random variable IDs and retained descriptions, HB lowers SHD from 39.00 for no-KG to 27.30. On continuous Sachs, graph-only BGe raises AUROC by 0.121 over no-KG Top-K. In a controlled synthetic study,…
PDF arXiv

Papers

Topics in this field

classifier · supervised · machine learning183 papers42 this week
causal discovery · causal inference · causal models123 papers18 this week
conformal prediction · split conformal prediction · aware conformal71 papers14 this week
bayesian optimization · bayesian optimization bo · black box optimization93 papers12 this week

Active contributors · 90 days

Yujia ZhengDSReg: Provably Recovering Individual World Latents without Reconstruction4
Gunwoong ParkLocal Fisher Information Enables Sparse Causal Discovery4
Aapo HyvärinenIdentifying Dependent Components from Multi-domain Linear Mixtures3
Sara MagliacaneIdentifying Dependent Components from Multi-domain Linear Mixtures3
Stefan FeuerriegelDebiased Prior-Data Fitted Networks for Amortized Bayesian Semi-Parametric Inference3
Isabel ValeraDeDiCa: A Scalable Deconfounding Discrete Causal Generative Model3

Recent papers

All in Research →

arXiv cs.LGOct 4
Measuring Learned Monotone Temporal Aggregation at Matched Admissibility
Yew Lee Tan
Risk regulation imposes directional constraints on scores; we adopt their strict per-input form -- the score monotone non-decreasing in every exposure input -- as a normative commitment. Deployed pipelines -- monotone hand-crafted aggregates feeding sign-constrained gradient boosting -- already satisfy it by composition, so constrained-versus-unconstrained comparisons price a guarantee the incumbent has for free. We instead hold admissibility fixed on both sides and measure what learning the aggregation is worth. Our instrument is a recurrent network whose state is classical risk statistics (an exponentially weighted moving average and a high-water mark with learned transforms), monotone by construction in every input and per MC-dropout sample. The central finding, by functional regression, is a subsumption boundary: a learned monotone channel reproduces the geometrically weighted separable family of hand-crafted statistics, one channel per member, to Spearman $ρ\ge 0.996$, approximates window statistics with measurable ceilings, and fails at consecutivity ($ρ= 0.924$) and time localization (0.628), both structural, and at the exposure floor (0.829), a learnability boundary. One…
PDF arXiv
arXiv cs.LGOct 4
An Information-Theoretic Evaluation Framework for Benchmark and Model Diagnosis in Knowledge Tracing
Houru Jiang, Zixi Wang, Tengteng Cheng, Xueyi Li, Mingliang Hou +4
Knowledge tracing (KT) models are predominantly evaluated using aggregate metrics such as area under the curve (AUC) and accuracy. However, these global scores obscure where the remaining errors originate and fail to indicate whether a benchmark is approaching saturation. While estimating a global theoretical performance limit is challenging in realistic KT settings, it is possible to quantify local predictability. To address this, we propose an information-theoretic evaluation framework for KT benchmark diagnosis. We use Context Tree Weighting (CTW) on item-response histories and current-item queries as an operational causal uncertainty coordinate, while distinguishing it from the unobserved Local Irreducible Uncertainty (LIU) under the full KT information set. By projecting predictions onto this shared uncertainty coordinate, we evaluate model performance gains across distinct entropy bands rather than only at the global level. Comprehensive evaluations on NIPS Task 3/4 and Algebra 2005 reveal that model improvements are highly non-uniform. Modern KT models show substantial gains in high-entropy regions, and additional item-aware references, log-loss, and equal-frequency…
PDF arXiv
arXiv cs.LGOct 4
Learning under Localized Minority Imbalance
Amin Hosseininasab, Steven M. Shugan
Class-imbalance methods implicitly assume that the minority class is uniformly undersampled relative to the majority class. However, in many real-world settings, minority instances may be disproportionately under-observed in certain regions of the feature space. For example, small businesses that go bankrupt may disappear from records, while those that survive remain visible, making bankruptcy appear less common among small firms than it actually is. This gives rise to localized minority imbalance (LMI), a challenge that is often overlooked and extends beyond general class-count imbalance. We show that under LMI, existing imbalance mitigation techniques can fit observation-induced biases in the training data and generalize poorly to under-observed regions of the true minority distribution. To address this, we propose a tree-based stratified approach that recursively partitions the feature space with the goal of reducing within-stratum LMI distortion. For each resulting stratum, we pair its majority instances with the full observed minority set and train a base classifier to create an ensemble. Extensive experiments over benchmark tabular datasets simulated with LMI show that our…
PDF arXiv
arXiv cs.LGOct 3
Uncertainty in Representation Learning on Knowledge Graphs
Yuqicheng Zhu
Knowledge graph embedding (KGE) methods represent entities and predicates in continuous vector spaces to infer missing knowledge. Despite strong benchmark performance, their predictions often lack principled reliability guarantees, limiting their use in high-stakes applications. Moreover, uncertainty arises throughout the KGE pipeline, from incomplete or probabilistic input knowledge to stochastic training and prediction. This thesis systematically investigates three sources of uncertainty in KGE: knowledge uncertainty, arising from incomplete, noisy, or probabilistic input knowledge; algorithmic uncertainty, induced by randomness in model training; and predictive uncertainty, concerning the reliability of model outputs. To address algorithmic uncertainty, the thesis demonstrates that models trained under identical settings can produce substantially different predictions and introduces a voting-based aggregation framework to mitigate this instability. To quantify predictive uncertainty, it adapts conformal prediction to KGE, constructing answer sets with distribution-free coverage guarantees and extending them to provide predicate-conditional reliability guarantees. To support…
PDF arXiv
arXiv cs.LGOct 3
EasyClassifier: Honest, Reproducible Machine-Learning Classification for Researchers Who Do Not Program
Ahmad B. A. Hassanat, Ghada A. Altarawneh
Machine-learning classification is now used across medicine, the social sciences, economics, education and engineering, very often by researchers who do not program. Two errors recur in such work: preprocessing learned on all rows before cross-validation (data leakage), and reporting the cross-validation score of the classifier that won a comparison (selection bias). Both make published scores optimistic. We present EasyClassifier, an open-source Python package that guides a user from a CSV or Excel file to a finished analysis through a sequence of plain-language, multiple-choice questions, with no code. It makes the correct procedure the default rather than an option: every preprocessing step is learned inside each training fold; the selected classifier receives a separate final score from nested cross-validation (up to 2,000 rows), or an untouched 20% test set; classifiers run with fixed default parameters and fixed random seeds; and every run writes a report with a ready-to-adapt Methods paragraph, the references to cite, and figures prepared for publication. On ten public datasets from seven fields and a random-label control, split in half so that one half served as an…
PDF arXiv
arXiv cs.LGOct 3
Coupling Noisy Pairwise Knowledge to the DAG Posterior for Causal Discovery
Guoliang Xu, James E Corter
External causal reports can improve structure learning from limited observations, but their reliability varies across sources and variable pairs. We introduce HB-NoisyKG, a Bayesian framework that combines observational data with repeated causal reports from sources such as large language models. Each report is a noisy observation of a direct pair state implied by one DAG. A feature-conditioned Beta prior pools information about pair reliability, and a shared error matrix captures systematic mistakes. Alternating inference uses the graph posterior to refine reliability estimates, which determine how reports influence subsequent graph updates. The report likelihood uses only graph pair-state marginals, so the same observation layer supports discrete and continuous likelihoods in graph-only and joint inference. Against an 80-restart no-KG baseline, HB uses at most 80 total restarts and lowers mean SHD from 22.39 to 16.06 on five discrete benchmarks. On a physical light tunnel with random variable IDs and retained descriptions, HB lowers SHD from 39.00 for no-KG to 27.30. On continuous Sachs, graph-only BGe raises AUROC by 0.121 over no-KG Top-K. In a controlled synthetic study,…
PDF arXiv

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