Papers

Field

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.

Open problems

Recently funded

Topics in this field

classifier · supervised · machine learning183 papers42 this week
causal discovery · causal inference · causal models123 papers20 this week
conformal prediction · split conformal prediction · aware conformal71 papers14 this week
bayesian optimization · bayesian optimization bo · black box optimization93 papers13 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

Activity

Recent papers

All in Research →

arXiv cs.LGOct 13
Agentic-MatriBO: Capability-Compiled Agentic Bayesian Optimization for Materials and Chemical Design
Yu Zeng, Yuchuan Chen, Ziyue Zhang, Zijian Wei, Yanyi Su +3
Materials and chemical optimization must choose the next synthesis, characterization, or high-fidelity simulation from sequential observations under a limited budget. Large language models can use task knowledge to propose adjustments, but composing surrogates, acquisition functions, and search strategies is constrained by compatibility, parameter dependencies, and evidence requirements. We present Agentic-MatriBO, a capability-compiled agentic Bayesian optimization (BO) framework that represents BO components and selection conditions as composable capabilities. A language policy proposes BO recipes and candidate plans; a compiler checks proposals against the registered capability catalog; and a deterministic execution layer validates and commits candidates. Oracle observations are stored in research memory and inform later rounds. We compare Agentic-MatriBO with eight baselines on four offline finite candidate pools---COF, PCE10, Buchwald--Hartwig (BH), and Suzuki--Miyaura---using five seeds per condition; results are reported descriptively. Across the four tasks, Agentic-MatriBO has the best mean endpoint quality after orienting each objective so that higher is better. On COF,…
PDF arXiv
arXiv cs.LGOct 13
Gaussian Process Bandits for AI System Optimization with Selectively Verified Feedback
Shuang Wu
Gaussian process bandits exploit similarities among configurations to optimize prompts, retrieval systems, and agent workflows. In these applications, however, inexpensive model-based evaluations can disagree with trusted task outcomes. We study selective verification when an action is chosen before a diagnostic is observed and trusted feedback is informative on an unknown set of diagnostic states. A finite reliability class and a finite RKHS signal dictionary describe these two sources of uncertainty. We construct a fixed audit design that combines reliability coverage with reward separation and establish a finite-time regret bound that accounts for a hard audit budget and the rounds needed to obtain informative observations. A safe adaptive extension mixes this robust design with predictable Chernoff allocations while preserving a uniform identification floor and exact expected acquisition-cost accounting. For signed-coordinate kernels, structured and diffuse reliability classes require different audit scales despite sharing the same reward geometry. Synthetic experiments distinguish these certified designs from an adaptive posterior heuristic and GP controls. Safe adaptive…
PDF arXiv
arXiv cs.LGOct 13
Train for Many, Update with One: Improving Multi-Sample Reasoning via Fenchel-Bregman Posterior Learning
Sungjun Lim, Adrian Robert Minut, Nico Carlsen Daheim, Mohammad Emtiyaz Khan, Kyungwoo Song +1
Multi-sample reasoning measures candidate coverage, the probability that at least one of $K$ candidates is correct. However, minimizing the average loss of individual attempts does not directly optimize candidate coverage. We therefore formulate finite-$K$ posterior learning, which trains a distribution over solver parameters to reduce the probability that all $K$ independently generated candidates fail. To optimize this objective efficiently, we propose a Fenchel-Bregman (FB) approach that requires only one posterior draw per update, rather than the $K$ used by direct Monte Carlo optimization. FB first uses an exact Fenchel reformulation to separate problem-specific weights from the posterior update, enabling one-draw posterior updates. It then applies a closed-form Bregman update to track these weights through an exponential running average of observed failures, reusing the same draw. We instantiate FB with IVON, a variational optimizer for Gaussian posteriors over solver parameters, yielding Fenchel-Bregman IVON (FBI). Across diverse recursive and autoregressive reasoning tasks, FBI improves candidate coverage. In a matched comparison, it achieves comparable Pass@10 using…
PDF arXiv
arXiv cs.LGOct 13
Calibrated Disambiguation for Partial Multi-label Learning
Zhuoming Li, Yuheng Jia, Mi Yu, Zicong Miao
Partial multi-label learning (PML) aims to train a classifier on datasets whose instances are over-annotated with not only relevant labels but also irrelevant labels, which is common when datasets are collected from crowd-sourcing platform. Existing works primarily approach it from a curriculum learning perspective, leveraging the memorization effect to disambiguate noisy labels and produce robust predictions. However, these methods are based on non-adaptive weighting functions and lack theoretical guidance for optimal weighting. To overcome these issues, a calibrated disambiguation model named PML-CD is proposed. We first formulate the optimal weighting function for curriculum-based disambiguation, which is equivalent to the calibration of the model's predicted confidences, thus providing guidance for curriculum designing. To obtain the optimal weighting function from PML dataset during the training, a transferable calibrator is designed, which takes the histogram of positive samples' confidences as input, and outputs the optimal curriculum weighting for training. Prototype alignment regularization is also proposed to promote the model's performance. Experiments conducted on…
PDF arXiv
arXiv cs.LGOct 13
CÆNL: Collapse-Based Active Neural Learning with Objective-Aligned Representation Control
Saad Alqithami
Active learning changes the representations used to acquire subsequent labels. We study this interaction through Collapse-Based Active Neural Learning (CÆNL), which separates acquisition, Dynamic Collapse Regularization (DCR), and its coefficient policy. DCR penalizes a normalized within-class to between-class scatter residual above a frozen reference, with a separate class-separation safeguard. Fixed, scheduled, deterministic-feedback, and learned-feedback policies apply this objective. The analysis establishes scatter invariances, conditional local descent, and coefficient bounds, and shows why clean geometry alone cannot guarantee input robustness. Six-seed classification comparisons are complemented by a 104-run study with separate control selection and ten-seed evaluations on each of two backbones. At 60% annotations on ImageNet-100, entropy acquisition with DCR and MACC-Lite reaches 73.420 +/- 0.512% accuracy on ResNet-50 and 73.086 +/- 0.397% on ResNet-18 (mean +/- seed SD), exceeding entropy alone by 1.742 and 1.834 percentage points. Both gains survive Holm correction under paired-t and exact sign-flip tests, with unchanged feedback settings across backbones.…
PDF arXiv
arXiv cs.AROct 12
Formal-Verification-Guided Bayesian Optimization for Non-Intrusive Last-Mile Coverage Closure
Chengjia Liu, Priyadarsi Mishra, David Kebo Houngninou, Michael Quinn, Aakash Tyagi +1
Last-mile functional coverage closure for modern chip designs remains costly because a mature UVM environment exposes many controls while direct hits on the remaining bins are sparse. We present a non-intrusive workflow that closes pre-defined bins in an existing, human-authored functional coverage model; it does not synthesize verification objectives from the design specification. The workflow first uses abstract-syntax-tree (AST) parsing and cone-of-influence (COI) tracing to map each residual target to existing testbench knobs. Under a matched 20-simulation budget, Bayesian optimization (BO) is compared with continued constrained-random verification (CRV) and random search over the same AST/COI-reduced space. Formal reachability then removes only targets supported by complete unreachability proofs, after which retained targets are grouped by formal difficulty and processed by group-specific BO under a second matched 20-simulation budget. Cleanup and grouping never add numerator bins; only simulator coverage artifacts do. On OpenTitan AES, first-pass reduced-space BO reaches 241/271 bins; formal cleanup removes two denominator bins; the integrated post-cleanup stage -…
PDF arXiv

Papers

Topics in this field

classifier · supervised · machine learning183 papers42 this week
causal discovery · causal inference · causal models123 papers20 this week
conformal prediction · split conformal prediction · aware conformal71 papers14 this week
bayesian optimization · bayesian optimization bo · black box optimization93 papers13 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 13
Agentic-MatriBO: Capability-Compiled Agentic Bayesian Optimization for Materials and Chemical Design
Yu Zeng, Yuchuan Chen, Ziyue Zhang, Zijian Wei, Yanyi Su +3
Materials and chemical optimization must choose the next synthesis, characterization, or high-fidelity simulation from sequential observations under a limited budget. Large language models can use task knowledge to propose adjustments, but composing surrogates, acquisition functions, and search strategies is constrained by compatibility, parameter dependencies, and evidence requirements. We present Agentic-MatriBO, a capability-compiled agentic Bayesian optimization (BO) framework that represents BO components and selection conditions as composable capabilities. A language policy proposes BO recipes and candidate plans; a compiler checks proposals against the registered capability catalog; and a deterministic execution layer validates and commits candidates. Oracle observations are stored in research memory and inform later rounds. We compare Agentic-MatriBO with eight baselines on four offline finite candidate pools---COF, PCE10, Buchwald--Hartwig (BH), and Suzuki--Miyaura---using five seeds per condition; results are reported descriptively. Across the four tasks, Agentic-MatriBO has the best mean endpoint quality after orienting each objective so that higher is better. On COF,…
PDF arXiv
arXiv cs.LGOct 13
Gaussian Process Bandits for AI System Optimization with Selectively Verified Feedback
Shuang Wu
Gaussian process bandits exploit similarities among configurations to optimize prompts, retrieval systems, and agent workflows. In these applications, however, inexpensive model-based evaluations can disagree with trusted task outcomes. We study selective verification when an action is chosen before a diagnostic is observed and trusted feedback is informative on an unknown set of diagnostic states. A finite reliability class and a finite RKHS signal dictionary describe these two sources of uncertainty. We construct a fixed audit design that combines reliability coverage with reward separation and establish a finite-time regret bound that accounts for a hard audit budget and the rounds needed to obtain informative observations. A safe adaptive extension mixes this robust design with predictable Chernoff allocations while preserving a uniform identification floor and exact expected acquisition-cost accounting. For signed-coordinate kernels, structured and diffuse reliability classes require different audit scales despite sharing the same reward geometry. Synthetic experiments distinguish these certified designs from an adaptive posterior heuristic and GP controls. Safe adaptive…
PDF arXiv
arXiv cs.LGOct 13
Train for Many, Update with One: Improving Multi-Sample Reasoning via Fenchel-Bregman Posterior Learning
Sungjun Lim, Adrian Robert Minut, Nico Carlsen Daheim, Mohammad Emtiyaz Khan, Kyungwoo Song +1
Multi-sample reasoning measures candidate coverage, the probability that at least one of $K$ candidates is correct. However, minimizing the average loss of individual attempts does not directly optimize candidate coverage. We therefore formulate finite-$K$ posterior learning, which trains a distribution over solver parameters to reduce the probability that all $K$ independently generated candidates fail. To optimize this objective efficiently, we propose a Fenchel-Bregman (FB) approach that requires only one posterior draw per update, rather than the $K$ used by direct Monte Carlo optimization. FB first uses an exact Fenchel reformulation to separate problem-specific weights from the posterior update, enabling one-draw posterior updates. It then applies a closed-form Bregman update to track these weights through an exponential running average of observed failures, reusing the same draw. We instantiate FB with IVON, a variational optimizer for Gaussian posteriors over solver parameters, yielding Fenchel-Bregman IVON (FBI). Across diverse recursive and autoregressive reasoning tasks, FBI improves candidate coverage. In a matched comparison, it achieves comparable Pass@10 using…
PDF arXiv
arXiv cs.LGOct 13
Calibrated Disambiguation for Partial Multi-label Learning
Zhuoming Li, Yuheng Jia, Mi Yu, Zicong Miao
Partial multi-label learning (PML) aims to train a classifier on datasets whose instances are over-annotated with not only relevant labels but also irrelevant labels, which is common when datasets are collected from crowd-sourcing platform. Existing works primarily approach it from a curriculum learning perspective, leveraging the memorization effect to disambiguate noisy labels and produce robust predictions. However, these methods are based on non-adaptive weighting functions and lack theoretical guidance for optimal weighting. To overcome these issues, a calibrated disambiguation model named PML-CD is proposed. We first formulate the optimal weighting function for curriculum-based disambiguation, which is equivalent to the calibration of the model's predicted confidences, thus providing guidance for curriculum designing. To obtain the optimal weighting function from PML dataset during the training, a transferable calibrator is designed, which takes the histogram of positive samples' confidences as input, and outputs the optimal curriculum weighting for training. Prototype alignment regularization is also proposed to promote the model's performance. Experiments conducted on…
PDF arXiv
arXiv cs.LGOct 13
CÆNL: Collapse-Based Active Neural Learning with Objective-Aligned Representation Control
Saad Alqithami
Active learning changes the representations used to acquire subsequent labels. We study this interaction through Collapse-Based Active Neural Learning (CÆNL), which separates acquisition, Dynamic Collapse Regularization (DCR), and its coefficient policy. DCR penalizes a normalized within-class to between-class scatter residual above a frozen reference, with a separate class-separation safeguard. Fixed, scheduled, deterministic-feedback, and learned-feedback policies apply this objective. The analysis establishes scatter invariances, conditional local descent, and coefficient bounds, and shows why clean geometry alone cannot guarantee input robustness. Six-seed classification comparisons are complemented by a 104-run study with separate control selection and ten-seed evaluations on each of two backbones. At 60% annotations on ImageNet-100, entropy acquisition with DCR and MACC-Lite reaches 73.420 +/- 0.512% accuracy on ResNet-50 and 73.086 +/- 0.397% on ResNet-18 (mean +/- seed SD), exceeding entropy alone by 1.742 and 1.834 percentage points. Both gains survive Holm correction under paired-t and exact sign-flip tests, with unchanged feedback settings across backbones.…
PDF arXiv
arXiv cs.AROct 12
Formal-Verification-Guided Bayesian Optimization for Non-Intrusive Last-Mile Coverage Closure
Chengjia Liu, Priyadarsi Mishra, David Kebo Houngninou, Michael Quinn, Aakash Tyagi +1
Last-mile functional coverage closure for modern chip designs remains costly because a mature UVM environment exposes many controls while direct hits on the remaining bins are sparse. We present a non-intrusive workflow that closes pre-defined bins in an existing, human-authored functional coverage model; it does not synthesize verification objectives from the design specification. The workflow first uses abstract-syntax-tree (AST) parsing and cone-of-influence (COI) tracing to map each residual target to existing testbench knobs. Under a matched 20-simulation budget, Bayesian optimization (BO) is compared with continued constrained-random verification (CRV) and random search over the same AST/COI-reduced space. Formal reachability then removes only targets supported by complete unreachability proofs, after which retained targets are grouped by formal difficulty and processed by group-specific BO under a second matched 20-simulation budget. Cleanup and grouping never add numerator bins; only simulator coverage artifacts do. On OpenTitan AES, first-pass reduced-space BO reaches 241/271 bins; formal cleanup removes two denominator bins; the integrated post-cleanup stage -…
PDF arXiv

Activity · 12 weeks

Coming with more sources: repos

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.

Fields · AI · updated 42m ago

Classifier & causal discovery

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

89papers this week−17.6% vs last week

All papers · 470 With code · 2

Newest Most relevant

439 papers this month