ALT

ML/AI Core

450 papers · 2017–2026

ALT (International Conference on Algorithmic Learning Theory) focuses on the theoretical and algorithmic foundations of machine learning.

Papers per year

'20

'25

Top authors

Steve Hanneke 13 papers

Yishay Mansour 10 papers

Aryeh Kontorovich 8 papers

Emilie Kaufmann 8 papers

Gergely Neu 7 papers

Csaba Szepesvári 7 papers

Mehryar Mohri 6 papers

Sandra Zilles 6 papers

Robi Bhattacharjee 6 papers

Daniel Hsu 5 papers

Authors are matched by name, so researchers who share a name ("Yang Liu", "Wei Wang") appear as a single profile and their counts at this venue are combined. How identity is handled.

Newest papers

Efficient and Provable Algorithms for Covariate Shift 2026

Convex optimization with p-norm oracles 2026

Eventually LIL Regret: Almost Sure $\ln\ln T$ Regret for a sub-Gaussian Mixture on Unbounded Data 2026

Learning from Synthetic Data: Limitations of ERM 2026

Group-realizable multi-group learning by minimizing empirical risk 2026

Robust Online Learning 2026

On the Hardness of Learning Regular Expressions 2026

Reward Selection with Noisy Observations 2026

Discriminative Feature Feedback with General Teacher Classes 2026

Predictive inference for time series: why is split conformal effective despite temporal dependence? 2026

Beyond Discrepancy: A Closer Look at the Theory of Distribution Shift 2026

Sink equilibria and the attractors of learning in games 2026

Regularized Robustly Reliable Learners 2026

Enjoying Non-linearity in Multinomial Logistic Bandits: A Minimax-Optimal Algorithm 2026

Privately Learning Decision Lists and a Differentially Private Winnow 2026

Closeness testing from distributed measurements 2026

Pareto-optimal Non-uniform Language Generation 2026

A Martingale Kernel Two-Sample Test 2026

Talagrand Meets Talagrand: Upper and Lower Bounds on Expected Soft Maxima of Gaussian Processes with Finite Index Sets 2026

Sample-Near-Optimal Agnostic Boosting with Improved Running Time 2026

All ALT 2026 papers →