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Open problems in Classifier & causal discovery

Lack of unified benchmarks for agentic black-box optimization1 paper raises it · 1 addresses it
Makes it hard to compare methods and isolate the effects of design choices across domains.
- A Closer Look at Agentic BBO: Benchmarking LLM Agents for Black-Box Optimizationraises · addresses
Identifiability of individual world latents without reconstruction1 paper raises it · 1 addresses it
Current methods often rely on reconstruction or auxiliary supervision, limiting their applicability.
- DSReg: Provably Recovering Individual World Latents without Reconstructionraises · addresses
High-dimensional Bayesian optimization with limited budgets1 paper raises it · 1 addresses it
Latent spaces with thousands of dimensions exceed evaluation budgets, making optimization inefficient.
- SABLE: Scalable Affine Bayesian Latent-space Explorationraises · addresses
Causal identifiability in linear SDEs with latent variables and reciprocal interactions1 paper raises it · 1 addresses it
Existing results are limited to fully observed systems without directed 2-cycles, restricting real-world applicability.
- Identifiability from Stationary Covariance in Linear SDEs with Reciprocal Interactions and Latent Variablesraises · addresses
Uncertainty-aware reuse of prior knowledge in neural architecture search1 paper raises it · 1 addresses it
Starting from scratch wastes prior knowledge, and zero-cost proxies are unreliable across tasks.
- QuaNAS: Uncertainty-Aware Reuse of Earlier Searches for Query-Efficient Neural Architecture Searchraises · addresses

Named by an AI from these papers' abstracts.

Open problems

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Topics in this field

classifier · supervised · machine learning194 papers35 this week
causal discovery · causal inference · causal models130 papers14 this week
bayesian optimization · bayesian optimization bo · black box optimization101 papers13 this week
conformal prediction · split conformal prediction · aware conformal73 papers11 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
Henry MossSABLE: Scalable Affine Bayesian Latent-space Exploration3
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

Active contributors

Activity · 12 weeks

Activity

Recent papers

All papers in Research

arXiv cs.LGSep 29
Beyond Conditional Independence: Root Cause Analysis with Deep Causal Models
Md Musfiqur Rahman, Kenneth Lee, Ziwei Jiang, Padmaja Jonnalagedda, Ruocheng Guo +1
Root cause analysis (RCA) is a critical problem in many real-world scenarios. RCA enables the identification of faulty or failing mechanisms in a system by comparing anomalous observations with corresponding reference (i.e., regular) observations. However, existing approaches rely either on heuristic methods or on conditional independence tests with a strong unconfoundedness assumption, and thus fail to exploit other complicated distributional constraints in the presence of latent variables. To relax these assumptions, we model the underlying system as a causal model and the anomalous system as a change in the structural functions of the same causal model. Specifically, to handle unobserved confounders, we establish an implicit connection between distributional constraint testing and root cause analysis. To adapt our approach to data generated from arbitrary causal models, we employ the deep causal model (DCM) framework, in which we design the causal model using neural networks. Finally, we illustrate how our method, RCA-DCM, can utilize different levels of partial graphical knowledge to perform RCA. We evaluate RCA-DCM against state-of-the-art baselines on simulated datasets, a…
PDF arXiv
arXiv cs.LGSep 29
SIRNET: Causal Adaptation of Pretrained Embeddings for Semiparametric Treatment-Effect Estimation Under Surrogacy
Lei Shi, Qiuran Lyu
Modern experiments increasingly produce rich post-treatment objects---such as text, images, and video---that may provide early surrogate signals for outcomes that are delayed, expensive, or observed only for a subset of participants. A pretrained encoder can turn such an object into a strong generic embedding, but the embedding is not trained for the estimation tasks required by a causal estimator and may emphasize information irrelevant to the causal target. We propose SIRNET, a Surrogate-Informed Representation adaptation framework for neural-NETwork-based causal estimation in the presence of unstructured post-treatment surrogates. Starting from pretrained surrogate embeddings, SIRNET learns a shared adapter jointly for outcome prediction, treatment propensity, and arm-specific surrogate marginalization, beyond the generic pretraining objective. The resulting covariate and surrogate representations provide a common interface for these nuisance tasks. The final treatment-effect estimate and confidence interval are obtained from cross-fitted orthogonal scores that attain the semiparametric efficiency bound under the stated nuisance-rate conditions. We also validate SIRNET's…
PDF arXiv
arXiv cs.LGSep 29
CHAIN: Calibrated LLM Forecasting via Causal-Temporal Hypergraph Inference
Wenjin Liu, Chenxi Wang, Yue Lu, Zhe Cui, Haoran Luo
Large language models have achieved significant progress in event forecasting, yet their probability outputs exhibit systematic calibration bias that varies heterogeneously across different domains and question types, undermining the trustworthiness of probabilistic outputs for decision-making under uncertainty. However, existing calibration methods typically correct probability outputs after prediction is complete, without modeling the structural sources of bias within the prediction process itself. To address this challenge, we decompose probabilistic prediction over causal-temporal hypergraphs into three stages, evidence weighting, evidence aggregation, and source fusion, and propose CHAIN, which designs stage-specific mechanisms to mitigate bias at each stage: (i) modulating the temporal decay function by causal topological distance, (ii) aggregating approximately independent causal chains via Noisy-OR after direction-aware deduplication, and (iii) driving adaptive fusion by causal coverage and directional balance. Experimental results on cross-domain forecasting benchmarks show CHAIN outperforms existing methods in expected calibration error, Brier score, and accuracy. Our…
PDF arXiv
arXiv stat.MLSep 29
Hierarchical Utility Calibration for Structured Multiclass Decisions
Futoshi Futami, Jerry Huang, Ichiro Takeuchi
In multiclass probabilistic prediction, Utility Calibration (UC), which focuses auditing on specified utilities, has recently received attention as a way to guarantee downstream decisions while controlling computational and sample requirements. At the same time, some multiclass problems have meaningful label hierarchies that play important roles in medicine and image classification, yet how UC evaluates utility within a hierarchy remains insufficiently understood. We show that the difference between realized utility and predicted mean utility admits an exact decomposition into a sum of contributions from the internal nodes of the label tree. This decomposition shows that positive and negative contributions from different nodes can cancel, and that even when UC is small, the utility errors remaining in parts of the hierarchy need not be small. To address this problem, we propose Hierarchical Utility Calibration (HUC), which evaluates each node contribution before summation while retaining the same target utility, subgroup, and predicted-utility interval. We further provide finite-sample evaluation over all predicted-utility intervals and propose HUC-Boost, which updates only…
PDF arXiv
arXiv cs.LGSep 29
DisCoMBO: Steering Expert-in-the-Loop Black Box Optimization via Distributional Conformance
Jonas Seng, Bennet Wittelsbach, Kristian Kersting
Sequential Model-Based Optimization (SMBO) traditionally relies on Bayesian or ensembling surrogates for uncertainty quantification. While historically treated as fully data-driven, SMBO increasingly integrates external domain expertise to accelerate discovery. To overcome the opaque guidance and diminished integration fidelity of standard acquisition re-weighting, Probabilistic Circuits (PCs) have emerged as a generative surrogate alternative, enabling direct knowledge injection via conditional sampling. However, these generative routines lack the formal exploration-exploitation semantics required for rigorous optimization. We introduce the Distributional Conformance Score (DisCo), a novel metric that unifies the flexibility and efficiency of PCs with a formal uncertainty framework. DisCo provides a bounded, $[0, 1]$-normalized measure of model "surprise" that (1) recovers properties comparable to kernel-based uncertainty known from, e.g., Gaussian Processes, while maintaining linear-time inference, and (2) enables accurate assessment of conformance of external knowledge w.r.t. model evidence. We then present DisCoMBO, a framework leveraging these properties for robust,…
PDF arXiv
arXiv stat.MLSep 28
LOCO-AdaMP: Built-in LOCO Inference for Adaptive Minipatch Ensembles with Enhanced Prediction
Yinan Cheng, Lili Zheng
As black-box machine learning models become increasingly common, extracting interpretations with uncertainty quantification has become a critical challenge. One popular type of interpretation is leave-one-covariate-out (LOCO) feature importance, while prior LOCO inference methods often require data-splitting or model-refitting. A recent ensemble framework, LOCO-MP, addresses these challenges using minipatches that subsample both observations and features, but massive feature subsampling can hurt prediction in high-dimensional sparse settings. Motivated by this limitation, we consider minipatch ensembles with adaptive feature sampling guided by LOCO importance, and propose LOCO-AdaMP, which enables free LOCO inference for the resulting adaptive minipatch ensemble. We show that LOCO-AdaMP yields substantially improved predictive models while retaining asymptotically valid feature importance inference without data-splitting, despite the complex dependence between the adaptive sampling distribution and the LOCO importance statistics. Our analysis relies on a careful leave-two-out perturbation bound for the iteratively updated sampling probabilities together with the stability of LOCO…
PDF arXiv

Papers

Topics in this field

classifier · supervised · machine learning194 papers35 this week
causal discovery · causal inference · causal models130 papers14 this week
bayesian optimization · bayesian optimization bo · black box optimization101 papers13 this week
conformal prediction · split conformal prediction · aware conformal73 papers11 this week

Active contributors · 90 days

Yujia ZhengDSReg: Provably Recovering Individual World Latents without Reconstruction4
Gunwoong ParkLocal Fisher Information Enables Sparse Causal Discovery4
Henry MossSABLE: Scalable Affine Bayesian Latent-space Exploration3
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

Recent papers

All papers in Research

arXiv cs.LGSep 29
Beyond Conditional Independence: Root Cause Analysis with Deep Causal Models
Md Musfiqur Rahman, Kenneth Lee, Ziwei Jiang, Padmaja Jonnalagedda, Ruocheng Guo +1
Root cause analysis (RCA) is a critical problem in many real-world scenarios. RCA enables the identification of faulty or failing mechanisms in a system by comparing anomalous observations with corresponding reference (i.e., regular) observations. However, existing approaches rely either on heuristic methods or on conditional independence tests with a strong unconfoundedness assumption, and thus fail to exploit other complicated distributional constraints in the presence of latent variables. To relax these assumptions, we model the underlying system as a causal model and the anomalous system as a change in the structural functions of the same causal model. Specifically, to handle unobserved confounders, we establish an implicit connection between distributional constraint testing and root cause analysis. To adapt our approach to data generated from arbitrary causal models, we employ the deep causal model (DCM) framework, in which we design the causal model using neural networks. Finally, we illustrate how our method, RCA-DCM, can utilize different levels of partial graphical knowledge to perform RCA. We evaluate RCA-DCM against state-of-the-art baselines on simulated datasets, a…
PDF arXiv
arXiv cs.LGSep 29
SIRNET: Causal Adaptation of Pretrained Embeddings for Semiparametric Treatment-Effect Estimation Under Surrogacy
Lei Shi, Qiuran Lyu
Modern experiments increasingly produce rich post-treatment objects---such as text, images, and video---that may provide early surrogate signals for outcomes that are delayed, expensive, or observed only for a subset of participants. A pretrained encoder can turn such an object into a strong generic embedding, but the embedding is not trained for the estimation tasks required by a causal estimator and may emphasize information irrelevant to the causal target. We propose SIRNET, a Surrogate-Informed Representation adaptation framework for neural-NETwork-based causal estimation in the presence of unstructured post-treatment surrogates. Starting from pretrained surrogate embeddings, SIRNET learns a shared adapter jointly for outcome prediction, treatment propensity, and arm-specific surrogate marginalization, beyond the generic pretraining objective. The resulting covariate and surrogate representations provide a common interface for these nuisance tasks. The final treatment-effect estimate and confidence interval are obtained from cross-fitted orthogonal scores that attain the semiparametric efficiency bound under the stated nuisance-rate conditions. We also validate SIRNET's…
PDF arXiv
arXiv cs.LGSep 29
CHAIN: Calibrated LLM Forecasting via Causal-Temporal Hypergraph Inference
Wenjin Liu, Chenxi Wang, Yue Lu, Zhe Cui, Haoran Luo
Large language models have achieved significant progress in event forecasting, yet their probability outputs exhibit systematic calibration bias that varies heterogeneously across different domains and question types, undermining the trustworthiness of probabilistic outputs for decision-making under uncertainty. However, existing calibration methods typically correct probability outputs after prediction is complete, without modeling the structural sources of bias within the prediction process itself. To address this challenge, we decompose probabilistic prediction over causal-temporal hypergraphs into three stages, evidence weighting, evidence aggregation, and source fusion, and propose CHAIN, which designs stage-specific mechanisms to mitigate bias at each stage: (i) modulating the temporal decay function by causal topological distance, (ii) aggregating approximately independent causal chains via Noisy-OR after direction-aware deduplication, and (iii) driving adaptive fusion by causal coverage and directional balance. Experimental results on cross-domain forecasting benchmarks show CHAIN outperforms existing methods in expected calibration error, Brier score, and accuracy. Our…
PDF arXiv
arXiv stat.MLSep 29
Hierarchical Utility Calibration for Structured Multiclass Decisions
Futoshi Futami, Jerry Huang, Ichiro Takeuchi
In multiclass probabilistic prediction, Utility Calibration (UC), which focuses auditing on specified utilities, has recently received attention as a way to guarantee downstream decisions while controlling computational and sample requirements. At the same time, some multiclass problems have meaningful label hierarchies that play important roles in medicine and image classification, yet how UC evaluates utility within a hierarchy remains insufficiently understood. We show that the difference between realized utility and predicted mean utility admits an exact decomposition into a sum of contributions from the internal nodes of the label tree. This decomposition shows that positive and negative contributions from different nodes can cancel, and that even when UC is small, the utility errors remaining in parts of the hierarchy need not be small. To address this problem, we propose Hierarchical Utility Calibration (HUC), which evaluates each node contribution before summation while retaining the same target utility, subgroup, and predicted-utility interval. We further provide finite-sample evaluation over all predicted-utility intervals and propose HUC-Boost, which updates only…
PDF arXiv
arXiv cs.LGSep 29
DisCoMBO: Steering Expert-in-the-Loop Black Box Optimization via Distributional Conformance
Jonas Seng, Bennet Wittelsbach, Kristian Kersting
Sequential Model-Based Optimization (SMBO) traditionally relies on Bayesian or ensembling surrogates for uncertainty quantification. While historically treated as fully data-driven, SMBO increasingly integrates external domain expertise to accelerate discovery. To overcome the opaque guidance and diminished integration fidelity of standard acquisition re-weighting, Probabilistic Circuits (PCs) have emerged as a generative surrogate alternative, enabling direct knowledge injection via conditional sampling. However, these generative routines lack the formal exploration-exploitation semantics required for rigorous optimization. We introduce the Distributional Conformance Score (DisCo), a novel metric that unifies the flexibility and efficiency of PCs with a formal uncertainty framework. DisCo provides a bounded, $[0, 1]$-normalized measure of model "surprise" that (1) recovers properties comparable to kernel-based uncertainty known from, e.g., Gaussian Processes, while maintaining linear-time inference, and (2) enables accurate assessment of conformance of external knowledge w.r.t. model evidence. We then present DisCoMBO, a framework leveraging these properties for robust,…
PDF arXiv
arXiv stat.MLSep 28
LOCO-AdaMP: Built-in LOCO Inference for Adaptive Minipatch Ensembles with Enhanced Prediction
Yinan Cheng, Lili Zheng
As black-box machine learning models become increasingly common, extracting interpretations with uncertainty quantification has become a critical challenge. One popular type of interpretation is leave-one-covariate-out (LOCO) feature importance, while prior LOCO inference methods often require data-splitting or model-refitting. A recent ensemble framework, LOCO-MP, addresses these challenges using minipatches that subsample both observations and features, but massive feature subsampling can hurt prediction in high-dimensional sparse settings. Motivated by this limitation, we consider minipatch ensembles with adaptive feature sampling guided by LOCO importance, and propose LOCO-AdaMP, which enables free LOCO inference for the resulting adaptive minipatch ensemble. We show that LOCO-AdaMP yields substantially improved predictive models while retaining asymptotically valid feature importance inference without data-splitting, despite the complex dependence between the adaptive sampling distribution and the LOCO importance statistics. Our analysis relies on a careful leave-two-out perturbation bound for the iteratively updated sampling probabilities together with the stability of LOCO…
PDF arXiv

Activity · 12 weeks

Fields · AI · updated 32m ago

Classifier & causal discovery

Found by the topic model: classifier, causal discovery, bayesian optimization.

73papers this week−28.4% vs last week

All papers · 498 With code · 2

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430 papers this month

Open problems in Classifier & causal discovery

Lack of unified benchmarks for agentic black-box optimization1 paper raises it · 1 addresses it
Makes it hard to compare methods and isolate the effects of design choices across domains.
- A Closer Look at Agentic BBO: Benchmarking LLM Agents for Black-Box Optimizationraises · addresses
Identifiability of individual world latents without reconstruction1 paper raises it · 1 addresses it
Current methods often rely on reconstruction or auxiliary supervision, limiting their applicability.
- DSReg: Provably Recovering Individual World Latents without Reconstructionraises · addresses
High-dimensional Bayesian optimization with limited budgets1 paper raises it · 1 addresses it
Latent spaces with thousands of dimensions exceed evaluation budgets, making optimization inefficient.
- SABLE: Scalable Affine Bayesian Latent-space Explorationraises · addresses
Causal identifiability in linear SDEs with latent variables and reciprocal interactions1 paper raises it · 1 addresses it
Existing results are limited to fully observed systems without directed 2-cycles, restricting real-world applicability.
- Identifiability from Stationary Covariance in Linear SDEs with Reciprocal Interactions and Latent Variablesraises · addresses
Uncertainty-aware reuse of prior knowledge in neural architecture search1 paper raises it · 1 addresses it
Starting from scratch wastes prior knowledge, and zero-cost proxies are unreliable across tasks.
- QuaNAS: Uncertainty-Aware Reuse of Earlier Searches for Query-Efficient Neural Architecture Searchraises · addresses

Named by an AI from these papers' abstracts.