Selective Inference

Follow this topic Ask about this topic

Momentum

3 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Jul 13Week of Sep 28

Latest papers 12

All topics

Selective Inference for Deep Clustering in Latent Spaces
Sep 23, 2026Eina Mizui, Tomohiro Shiraishi, Shunichi Nishino +1Selective Inference Clustering
Nagoya University · RIKEN
How Sensitive Are LLM Leaderboard Claims to Hidden Model Selection?
Sep 23, 2026Chen Yang, Xianyang Zhang, Jun ChenLLM Evaluation Selective Inference
Department of Statistics, Texas A&M University · Division of Computational Biology, Department of Quantitative Health Sciences, Mayo Clinic
Encrypt What Matters: When Selective Homomorphic Inference Is Efficient
Sep 8, 2026Ali Backour, Juan Reyes, Jaime Punyed +1Efficient Inference Selective Inference
Massachusetts Institute of Technology Cambridge, MA 02139
Automatic Statistical Test for Rationally Expressible Algorithms by Selective Inference, with Applications to Feature Selection
Aug 5, 2026Teruyuki Katsuoka, Tomohiro Shiraishi, Shuichi Nishino +1Selective Inference Selection Bias
Nagoya University · RIKEN
CogEEGAgent: Toward Autonomous Cognitive EEG Analysis with Grounded Execution and Selection-Aware Verification
Jul 27, 2026Dengzhe Hou, Lingyu Jiang, Fangzhou Lin +1Electroencephalography Selective Inference
1Graduate School of Information Sciences, Tohoku University · 2Unprecedented-scale Data Analytics Center, Tohoku University · 3Texas A&M University +1
Measurement Under Selection: Decoy-Calibrated Failure Audits for Language Models
Jun 8, 2026Vyzantinos Repantis, Ameya Gawde, Harshvardhan SinghLLM Evaluation Language Model Error Detection
The Educational Approaches to Virtual Reality Technologies Lab, University of Ioannina, Greece · Meta Platforms
Trust but Verify: Prover-Verifier Deliberation for Selective LLM Prediction
May 24, 2026João Sedoc, Baotong Zhang, Dean FosterLLM Answer Verification Selective Prediction
New York University
Post-ADC Inference: Valid Inference After Active Data Collection
May 12, 2026Shuichi Nishino, Tomohiro Shiraishi, Teruyuki Katsuoka +1Selective Inference
Nagoya University · RIKEN
Towards Reliable LLM Evaluation: Correcting the Winner's Curse in Adaptive Benchmarking
May 7, 2026Yang Xu, Jiefu Zhang, Haixiang Sun +3LLM Evaluation Selective Inference
Purdue University · Johns Hopkins University · Purdue University, USA
CHASE: Competing Hypotheses for Ambiguity-Aware Selective Prediction
May 2, 2026Kartik Jhawar, Yuhao Geng, Atul N. Parikh +1Selective Prediction Selective Inference
Institute for Digital Molecular Analytics and Science, NTU, Singapore 636921 · School of Electrical and Electronic Engineering, NTU, Singapore 639798 · School of Materials Science and Engineering, NTU, Singapore 639798 +1
Partial Causal Structure Learning for Valid Selective Conformal Inference under Interventions
Mar 2, 2026Amir Asiaee, Kavey Aryan, James P. LongSelective Inference Causal Structure Learning
Department of Biostatistics, Vanderbilt University Medical Center, TN 37203, USA · Department of Informatics, King’s College London, London, UK · Department of Biostatistics, MD Anderson Cancer Center, Houston, TX, USA
Making Interpretable Discoveries from Unstructured Data: A High-Dimensional Multiple Hypothesis Testing Approach
Nov 3, 2025Jacob CarlsonSelective Inference Interpretable ML
Harvard University∗

Sep 23, 2026, stat.MLJ/K move · Enter open · S save

Selective Inference for Deep Clustering in Latent Spaces

Eina Mizui, Tomohiro Shiraishi, Shunichi Nishino, Ichiro Takeuchi

Nagoya University · RIKEN

Selective Inference Clustering Unsupervised Clustering Deep Clustering Statistical Hypothesis Testing

Open paper AskPDF arXiv

Deep clustering is a powerful approach for discovering meaningful structures in high-dimensional data by learning a low-dimensional latent representation prior to clustering. Despite its empirical success, assessing the statistical reliability of the resulting clusters remains challenging. Testing discovered clusters on the same data induces selection bias and invalidates classical

p

-values. Selective inference (SI) provides a principled framework for correcting this bias, but existing methods focus on clustering performed directly on the observed features. In this work, we develop an SI framework for deep clustering with a fixed pretrained encoder. The key challenge is that cluster assignments are determined through a nonlinear transformation from the original data space to the latent space, resulting in a substantially more complex selection process than in conventional clustering. Our method provides a computationally tractable way to account for this process and enables valid statistical testing of differences between clusters identified in the latent space. Synthetic experiments demonstrate that the proposed method controls the Type I error rate while achieving higher power than valid but conservative baselines, and genomic applications show that it can identify significant cluster differences while appropriately accounting for selection bias. Our framework provides a principled approach to quantifying the statistical reliability of structures discovered by deep clustering.

Momentum

Related topics

Latest papers 12