AI Theory

This study presents CADIM, an adaptive algorithm using interventions to learn true causal relationships from mixtures of DAGs, achieving near-optimal intervention sizes and providing quantifiable opti…

Researchers uniquely estimate probabilities in Causal Bayesian Networks using simple independence assumptions, enabling analysis from observational data and simplifying counterfactual probability calc…

CMR: A novel Concept-Based Memory Reasoner delivers human-understandable, verifiable AI task predictions by using a neural selection mechanism over a set of human-understandable logic rules, achievin…

Problem (FAIR) framework and FORM algorithm achieve flexible multi-stakeholder fairness in online recommendation systems, balancing platform revenue with user and item fairness.

New algorithm improves differentially private estimations by adapting to dataset hardness, enhancing accuracy for variance, classification, and regression tasks.

Instance-optimal private density estimation algorithms, adapting to data characteristics for improved accuracy in the Wasserstein distance, are introduced.

Researchers developed a novel method to inject undetectable backdoors into obfuscated neural networks and language models, even with white-box access, posing significant security risks.

This paper unveils the information-theoretic limits of online classification with noisy labels, showing that the minimax risk is tightly characterized by the Hellinger gap of noisy label distributions…

New theoretical analysis reveals optimal binning strategies for minimizing bias in expected calibration error (ECE), improving machine learning model calibration evaluation.

Contrastive learning enables efficient probabilistic inference in high-dimensional time series by creating Gaussian representations that form a Gauss-Markov chain, allowing for closed-form solutions t…

This paper proves that inexact ALMs applied to SDPs achieve linear convergence for both primal and dual iterates, contingent solely on strict complementarity and a bounded solution set, thus resol…

IGNITE improves offline optimization by incorporating surrogate gradient norm to reduce model sharpness, boosting performance up to 9.6%

Data Debiasing with Datamodels (D3M) efficiently improves machine learning model robustness by identifying and removing specific training examples that disproportionately harm minority groups’ accurac…

Boosting machine learning model interpretability, this paper introduces cluster-based and tree-based Sparse Explanation Values (SEV) for generating more meaningful and credible explanations by optimiz…

AI systems are made safer by ‘circuit breakers’ that directly control harmful internal representations, significantly improving alignment and robustness against adversarial attacks with minimal impact…

Boosting adversarial robustness fairness in deep neural networks, Anti-Bias Soft Label Distillation (ABSLD) adaptively adjusts soft label smoothness to reduce error gap between classes.

Over-parameterized gradient descent dynamically adapts to signal structure, improving sequence model generalization and outperforming fixed-kernel methods.

A novel algorithm, D-FTBL, achieves improved regret bounds for bandit convex optimization with delayed feedback, tightly matching existing lower bounds in worst-case scenarios.

A novel fully dynamic algorithm achieves a (4+ε)-approximate solution for the k-center clustering problem with outliers in general metric spaces, boasting an efficient update time.

A new algorithm, AOGS, achieves significantly lower regret in two-sided matching markets by cleverly integrating exploration and exploitation, thus removing the dependence on the number of arms (K) in…