Density Ratio Estimation

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

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Learning What to Forget: Distributional Unlearning for LLM Representation Spaces
Sep 30, 2026Pinaki Mohanty, Haoran Tang, Maggie Makar +1Density Ratio Estimation Machine Unlearning
Department of Computer Science, College of Science & College of Engineering, Purdue University, West Lafayette, IN, USA · Computer Science and Engineering Division, University of Michigan, Ann Arbor, MI, USA
Learning Conditional Expectation Operators via Functional Newton Updates
Sep 28, 2026Thiago Ramos, Alek Fröhlich, Daniel Perazzo +1Density Ratio Estimation Low-Rank Approximation
Federal University of São Carlos · CSML, Istituto Italiano di Tecnologia and University of Genoa · CSML, Istituto Italiano di Tecnologia and University College London
Density-Ratio Rescoring for Imbalanced Classification Using Raking Duals and Classifier Scores
Sep 20, 2026Dongha Kim, Seunghwan ParkClass-Imbalanced Learning Density Ratio Estimation
School of Mathematics, Statistics and Data Science, Sungshin Women’s University, 2, Bomun-ro 34da-gil, Seongbuk-gu, Seoul, 02844, Republic of Korea · Department of Information Statistics, Kangwon National University, 1, Kangwondaehak-gil, Chuncheon-si, Gangwon-do, 24341, Republic of Korea
Learning under Target Shift: Optimal Density Ratio Estimation and Importance-Weighted Regression
Sep 14, 2026Ren-Rui Liu, Zheng-Chu GuoKernel Regression Distribution Shift
School of Mathematical Sciences, Zhejiang University, Hangzhou 310058, China
Fitted Occupancy-Ratio Evaluation without Bellman Completeness
Jul 6, 2026Lars van der Laan, Nathan KallusOff-Policy Evaluation Offline RL
Stanford University · Netflix · Cornell University
Sampling Bias Compensation for Robust Evaluation of Audio Classification Systems with Partially Labeled Evaluation Datasets
Jul 5, 2026Javier Naranjo-Alcazar, Annamaria Mesaros, Tuomas Virtanen +1Importance Sampling Density Ratio Estimation
Instituto Tecnologico de Informatica (ITI), Valencia, Spain · Tampere University, Tampere, Finland
Regularized Variational and Spectral Log-Density-Ratio Estimation in the Gaussian Location Model
Jul 2, 2026Francis BachSpectral Regularization Density Ratio Estimation
SIERRA · Inria - Ecole Normale Sup´erieure PSL Research University
Causal Density Functions
May 30, 2026Sridhar MahadevanCausal Effect Estimation Density Ratio Estimation
Research Professor, University of Massachusetts, Amherst · Adobe Research San Jose, CA
Density-Ratio Losses for Post-Hoc Learning to Defer
May 19, 2026Alexander Soen, Ragnar Thobaben, Joakim Jaldén +1Learning to Defer Density Ratio Estimation
KTH⋄ · Google Research†
Anytime PAC-Bayes for Constrained Density-Ratio Networks under Covariate Shift
May 17, 2026Paulo Akira F. Enabe, Rodrigo ProvasiPAC-Bayesian Generalization Bounds Domain Adaptation
Escola Politénica University of São Paulo Department of Structural and Geotechnical Engineering
TokenRatio: Principled Token-Level Preference Optimization via Ratio Matching
May 12, 2026Truong Nguyen, Tien-Phat Nguyen, Linh Ngo Van +3Pairwise Preference Learning LLM Alignment
Hanoi University of Science and Technology · University of Stuttgart · German Research Center for Artificial Intelligence +3
Adaptive TD-Lambda for Cooperative Multi-agent Reinforcement Learning
May 12, 2026Yue Deng, Zirui Wang, Yin ZhangTemporal-Difference Learning Density Ratio Estimation
1Zhongguancun Academy, Beijing, China · College of Computer Science and Technology, Zhejiang University
A Spectral Framework for Closed-Form Relative Density Estimation
May 11, 2026Francis BachDensity Ratio Estimation Spectral Methods
SIERRA · Inria - Ecole Normale Supérieure · PSL Research University
Weight Clipping for Robust Conformal Inference under Unbounded Covariate Shifts
May 3, 2026James Wang, Surbhi GoelDistribution Shift Robustness Weighted Conformal Prediction
Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA 19104, USA.
LatentDiff: Scaling Semantic Dataset Comparison to Millions of Images
Apr 28, 2026James Flora, Kowshik Thopalli, Akshay R. Kulkarni +2Distribution Shift Distribution Shift Detection
School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, Oregon, USA · Lawrence Livermore National Laboratory, Livermore, California, USA · University of California San Diego, California, USA

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Learning What to Forget: Distributional Unlearning for LLM Representation Spaces

Pinaki Mohanty, Haoran Tang, Maggie Makar, Rajiv Khanna

Department of Computer Science, College of Science & College of Engineering, Purdue University, West Lafayette, IN, USA · Computer Science and Engineering Division, University of Michigan, Ann Arbor, MI, USA

Density Ratio Estimation Machine Unlearning LLM Unlearning

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Machine learning systems increasingly face the need to remove the influence of entire data domains, such as toxic language, harmful behavior, or topical content, rather than isolated records. Recent work formalizes this problem as \emph{distributional unlearning}: selecting a subset of a forget domain whose removal moves the training distribution away from an unwanted population while preserving proximity to the desired one. However, existing analyses often impose parametric assumptions to obtain tractable selection rules. These assumptions may be poorly suited to high-dimensional language-model representations. We introduce \textsc{Mamushi}, a framework for non-parametric distributional unlearning that ranks forget examples using a probabilistic classifier whose Bayes-optimal logit equals the forget-to-retain log-density ratio (up to an additive class-prior constant). We show that thresholding the population log-density ratio yields the optimal fixed-budget selection rule for our removal--preservation objective and establish a non-asymptotic transfer guarantee relating score-estimation and threshold-calibration errors to degradation from the population-optimal selection rule. Our empirical evaluation spans real-world datasets on toxic-language removal and topical-domain removal regimes using different representations, with \textsc{Mamushi} achieving a more favorable removal--preservation trade-off than other baselines. Our work shows that \textsc{Mamushi} can serve as an efficient selection approach for downstream machine unlearning procedures, reducing the number of forget examples required to reach a fixed forgetting target.

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