Papers

Environment-Adaptive Covariate Selection: Learning When to Use Spurious Correlations for Out-of-Distribution Prediction

Discrete Causal Representation Learning

Problems with Chinchilla Approach 2: Systematic Biases in IsoFLOP Parabola Fits

Goal-Oriented Influence-Maximizing Data Acquisition for Learning and Optimization

Identification and Estimation of the Conditional Average Treatment Effect with Nonignorable Missing Covariates, Treatment, and Outcome

Causal Representation Meets Stochastic Modeling under Generic Geometry

On the Identifiability of Regime-Switching Models with Multi-Lag Dependencies

Adaptive Nonparametric Perturbations of Parametric Bayesian Models

Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfaces

Structured Flow Autoencoders: Learning Structured Probabilistic Representations with Flow Matching

Meta-probabilistic Modeling

Strategic Learning with Local Explanations as Feedback

Latency-Response Theory Model: Evaluating Large Language Models via Response Accuracy and Chain-of-Thought Length

Bayesian Invariance Modeling of Multi-Environment Data

A Bayesian Reinforcement Learning Framework for Optimizing the BCI-utility of P300 Brain-Computer Interfaces

Deep Generative Models: Complexity, Dimensionality, and Approximation

Permutative Preference Alignment from Listwise Ranking of Human Judgments

Identifying Neural Dynamics Using Interventional State Space Models

Finding Information Quality: Counterfactual Voting Adjustment for Quality Assessment and Fairer Voting in Online Platforms with Helpfulness Evaluation

Policy Learning with a Natural Language Action Space: A Causal Approach

Exponential Family Attention

Posterior Mean Matching: Generative Modeling through Online Bayesian Inference

Accuracy on the Wrong Line: On the Pitfalls of Noisy Data for Out-of-distribution Generalisation

Breaking Feedback Loops in Recommender Systems with Causal Inference

Addressing Discretization-Induced Bias in Demographic Prediction

Valid Inference after Causal Discovery

Doubly Robust Identification of Treatment Effects from Multiple Environments

Let Me Grok for You: Accelerating Grokking via Embedding Transfer from a Weaker Model

Probabilistic Machine Learning: New Frontiers for Modeling Consumers and their Choices

Deep Ensembles Secretly Perform Empirical Bayes

From Unstructured Data to In-Context Learning: Exploring What Tasks Can Be Learned and When

Desiderata for Representation Learning: A Causal Perspective

Spatiotemporal Clustering with Neyman-Scott Processes via Connections to Bayesian Nonparametric Mixture Models

Instrumental Variable Value Iteration for Causal Offline Reinforcement Learning

LCL: Contrastive Learning for Lineage Barcoded scRNA-seq Data

LLMs Are Prone to Fallacies in Causal Inference

Causal Inference for Human-Language Model Collaboration

Uncertainty Calibration for Tool-Using Language Agents

On the Identifiability of Switching Dynamical Systems

Conformal Sensitivity Analysis for Individual Treatment Effects

Multi-Domain Causal Representation Learning via Weak Distributional Invariances

Causal Fairness Assessment of Treatment Allocation with Electronic Health Records

Optimization-based Causal Estimation from Heterogeneous Environments

Offline Policy Evaluation and Optimization under Confounding

A Causality-inspired Plus-minus Model for Player Evaluation in Team Sports

Interventional Causal Representation Learning

Bidirectional Attention as a Mixture of Continuous Word Experts

Coherent Blending of Biophysics-Based Knowledge with Bayesian Neural Networks for Robust Protein Property Prediction

Learning Equilibria in Matching Markets from Bandit Feedback

The Sample Complexity of Online Contract Design

Delayed and Indirect Impacts of Link Recommendations

On Learning Necessary and Sufficient Causal Graphs

Recommendation Systems with Distribution-Free Reliability Guarantees

Detecting Incidental Correlation in Multimodal Learning via Latent Variable Modeling

Adjusting Machine Learning Decisions for Equal Opportunity and Counterfactual Fairness

Team Resilience under Shock: An Empirical Analysis of GitHub Repositories during Early COVID-19 Pandemic

Large Sample Properties of Matching for Balance

Clarifying Trust of Materials Property Predictions using Neural Networks with Distribution-Specific Uncertainty Quantification

Multi-Source Causal Inference Using Control Variates

Anticipating Performativity by Predicting from Predictions

Empirical Gateaux Derivatives for Causal Inference

Identifiable Variational Autoencoders via Sparse Decoding

Adjusting for Indirectly Measured Confounding Using Large-scale Propensity Score

Partial Identification with Noisy Covariates: A Robust Optimization Approach

Statistical and Computational Trade-offs in Variational Inference: A Case Study in Inferential Model Selection

Posterior Collapse and Latent Variable Non-identifiability

A Proxy Variable View of Shared Confounding

Double Empirical Bayes Testing

Point Process Models for Sequence Detection in High-dimensional Neural Spike Trains

Causal Inference for Recommender Systems

Minimal Dispersion Approximately Balancing Weights: Asymptotic Properties and Practical Considerations

Towards Clarifying the Theory of the Deconfounder

The Blessings of Multiple Causes

Frequentist Consistency of Variational Bayes

Variational Bayes under Model Misspecification

Using Embeddings to Correct for Unobserved Confounding in Networks

Comment: Variational Autoencoders as Empirical Bayes

The Medical Deconfounder: Assessing Treatment Effect with Electronic Health Records

Black Box FDR

Evaluating Bayesian Models with Posterior Dispersion Indices

Robust Probabilistic Modeling with Bayesian Data Reweighting

A Bayesian Hierarchical Model for Spatial Extremes with Multiple Durations