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Li-Yang Tan

14 papers · 2012–2026 · 3 venues · across top CS/AI venues

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

Achievements

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+8 more ↓ πŸ—ΊοΈ Taxonomy Completionist (17) πŸƒ Academic Marathon Β· Half 🌍 Conference Polyglot Β· Bronze πŸŒ‰ Interdisciplinary Bridge Β· Silver 🐣 Hot Topic Early Bird
πŸ”¬ Deep Specialist (12) 🀝 Dynamic Duo (11) πŸ† Keyword Champion (4) πŸ“ˆ Trend Setter ❓ The Questioner πŸ”₯ Unstoppable Β· Bronze πŸ—ƒοΈ Keyword Collector (67) πŸ’Ž Century Club Β· Bronze

Venues

COLT (7) NeurIPS (4) ICML (3)

Top co-authors

Guy Blanc (11) Jane Lange (10) Caleb Koch (2) Neha Gupta (2) Carmen Strassle (2) Ali Malik (2) Lydia Zakynthinou (1) Konstantina Bairaktari (1) Chirag Pabbaraju (1) Justin Thaler (1)

Research topics

Machine Learning (13) Optimization & Theory (12) Core Methods (8) Artificial Intelligence (4) Learning Types (4) Computer Science (3) Core AI (3) Foundations (3) Mathematics & Optimization (1) Learning Paradigms (1) Mathematics (1)

Keywords

decision tree learning (4) decision tree (4) sample complexity (3) boosting algorithm (2) pac learning (2) attribute-efficient learning (2) noise tolerance (2) statistical query (1) computational complexity (1) multi-task learning (1) weak learner (1) concept class (1) query complexity (1) additive noise (1) halfspace learning (1) greedy algorithm (1) mistake bound (1) polynomial time (1) agnostic learning (1) learning theory (1)

Papers

Boosting with List-Decodable Codes COLT 2026 A Distributional-Lifting Theorem for PAC Learning COLT 2025 Superconstant Inapproximability of Decision Tree Learning COLT 2024 Harnessing the power of choices in decision tree learning NeurIPS 2023 Multitask Learning via Shared Features: Algorithms and Hardness COLT 2023 A query-optimal algorithm for finding counterfactuals ICML 2022 On the power of adaptivity in statistical adversaries COLT 2022 Open Problem: Properly learning decision trees in polynomial time? COLT 2022 Popular decision tree algorithms are provably noise tolerant ICML 2022 Provably efficient, succinct, and precise explanations NeurIPS 2021 Estimating decision tree learnability with polylogarithmic sample complexity NeurIPS 2020 Provable guarantees for decision tree induction: the agnostic setting ICML 2020 Universal guarantees for decision tree induction via a higher-order splitting criterion NeurIPS 2020 Attribute-Efficient Learning andWeight-Degree Tradeoffs for Polynomial Threshold Functions COLT 2012