| 2026 | COLT | Boosting with List-Decodable Codes. | Addison Prairie, Li-Yang Tan |
| 2025 | COLT | A Distributional-Lifting Theorem for PAC Learning. | Guy Blanc, Jane Lange, Carmen Strassle, Li-Yang Tan |
| 2025 | FOCS | Computational-Statistical Tradeoffs from NP-hardness. | Guy Blanc, Caleb Koch, Carmen Strassle, Li-Yang Tan |
| 2024 | COLT | Superconstant Inapproximability of Decision Tree Learning. | Caleb Koch, Carmen Strassle, Li-Yang Tan |
| 2024 | FOCS | Fast Decision Tree Learning Solves Hard Coding-Theoretic Problems. | Caleb Koch, Carmen Strassle, Li-Yang Tan |
| 2024 | FOCS | The Sample Complexity of Smooth Boosting and the Tightness of the Hardcore Theorem. | Guy Blanc, Alexandre Hayderi, Caleb Koch, Li-Yang Tan |
| 2023 | COLT | Multitask Learning via Shared Features: Algorithms and Hardness. | Konstantina Bairaktari, Guy Blanc, Li-Yang Tan, Jonathan R. Ullman, Lydia Zakynthinou |
| 2023 | FOCS | A strong composition theorem for junta complexity and the boosting of property testers. | Guy Blanc, Caleb Koch, Carmen Strassle, Li-Yang Tan |
| 2023 | FOCS | Properly learning decision trees with queries is NP-hard. | Caleb Koch, Carmen Strassle, Li-Yang Tan |
| 2023 | SODA | Single-Pass Streaming Algorithms for Correlation Clustering. | Soheil Behnezhad, Moses Charikar, Weiyun Ma, Li-Yang Tan |
| 2023 | SODA | Superpolynomial lower bounds for decision tree learning and testing. | Caleb Koch, Carmen Strassle, Li-Yang Tan |
| 2023 | STOC | Lifting Uniform Learners via Distributional Decomposition. | Guy Blanc, Jane Lange, Ali Malik, Li-Yang Tan |
| 2022 | COLT | On the power of adaptivity in statistical adversaries. | Guy Blanc, Jane Lange, Ali Malik, Li-Yang Tan |
| 2022 | FOCS | Almost 3-Approximate Correlation Clustering in Constant Rounds. | Soheil Behnezhad, Moses Charikar, Weiyun Ma, Li-Yang Tan |
| 2022 | ICALP | Reconstructing Decision Trees. | Guy Blanc, Jane Lange, Li-Yang Tan |
| 2022 | ICML | A query-optimal algorithm for finding counterfactuals. | Guy Blanc, Caleb Koch, Jane Lange, Li-Yang Tan |
| 2022 | ICML | Popular decision tree algorithms are provably noise tolerant. | Guy Blanc, Jane Lange, Ali Malik, Li-Yang Tan |
| 2022 | STOC | The query complexity of certification. | Guy Blanc, Caleb Koch, Jane Lange, Li-Yang Tan |
| 2022 | SAT | A Generalization of the Satisfiability Coding Lemma and Its Applications. | Milan Moss, Harry Sha, Li-Yang Tan |
| 2021 | FOCS | Properly learning decision trees in almost polynomial time. | Guy Blanc, Jane Lange, Mingda Qiao, Li-Yang Tan |
| 2021 | FOCS | Sharper bounds on the Fourier concentration of DNFs. | Victor Lecomte, Li-Yang Tan |
| 2021 | FOCS | Tradeoffs for small-depth Frege proofs. | Toniann Pitassi, Prasanna Ramakrishnan, Li-Yang Tan |
| 2021 | ICALP | Learning Stochastic Decision Trees. | Guy Blanc, Jane Lange, Li-Yang Tan |
| 2021 | PODC | Brief Announcement: A Randomness-efficient Massively Parallel Algorithm for Connectivity. | Moses Charikar, Weiyun Ma, Li-Yang Tan |
| 2021 | SODA | Query strategies for priced information, revisited. | Guy Blanc, Jane Lange, Li-Yang Tan |
| 2020 | CRYPTO | Non-malleability Against Polynomial Tampering. | Marshall Ball, Eshan Chattopadhyay, Jyun-Jie Liao, Tal Malkin, Li-Yang Tan |
| 2020 | ICALP | The Power of Many Samples in Query Complexity. | Andrew Bassilakis, Andrew Drucker, Mika Gs, Lunjia Hu, Weiyun Ma, Li-Yang Tan |
| 2020 | ICML | Provable guarantees for decision tree induction: the agnostic setting. | Guy Blanc, Jane Lange, Li-Yang Tan |
| 2020 | STOC | Fooling Gaussian PTFs via local hyperconcentration. | Ryan O'Donnell, Rocco A. Servedio, Li-Yang Tan |
| 2020 | SPAA | Unconditional Lower Bounds for Adaptive Massively Parallel Computation. | Moses Charikar, Weiyun Ma, Li-Yang Tan |
| 2019 | SODA | Pseudorandomness for read-k DNF formulas. | Rocco A. Servedio, Li-Yang Tan |
| 2019 | STOC | Fooling polytopes. | Ryan O'Donnell, Rocco A. Servedio, Li-Yang Tan |
| 2018 | FOCS | Non-Malleable Codes for Small-Depth Circuits. | Marshall Ball, Dana Dachman-Soled, Siyao Guo, Tal Malkin, Li-Yang Tan |
| 2017 | FOCS | Deterministic Search for CNF Satisfying Assignments in Almost Polynomial Time. | Rocco A. Servedio, Li-Yang Tan |
| 2017 | FOCS | Fooling Intersections of Low-Weight Halfspaces. | Rocco A. Servedio, Li-Yang Tan |
| 2016 | STOC | Near-optimal small-depth lower bounds for small distance connectivity. | Xi Chen, Igor C. Oliveira, Rocco A. Servedio, Li-Yang Tan |
| 2016 | STOC | Poly-logarithmic Frege depth lower bounds via an expander switching lemma. | Toniann Pitassi, Benjamin Rossman, Rocco A. Servedio, Li-Yang Tan |
| 2015 | FOCS | An Average-Case Depth Hierarchy Theorem for Boolean Circuits. | Benjamin Rossman, Rocco A. Servedio, Li-Yang Tan |
| 2015 | SODA | Approximate resilience, monotonicity, and the complexity of agnostic learning. | Dana Dachman-Soled, Vitaly Feldman, Li-Yang Tan, Andrew Wan, Karl Wimmer |
| 2015 | STOC | Boolean Function Monotonicity Testing Requires (Almost) n | Xi Chen, Anindya De, Rocco A. Servedio, Li-Yang Tan |
| 2015 | SAGT | Algorithmic Signaling of Features in Auction Design. | Shaddin Dughmi, Nicole Immorlica, Ryan O'Donnell, Li-Yang Tan |
| 2014 | FOCS | New Algorithms and Lower Bounds for Monotonicity Testing. | Xi Chen, Rocco A. Servedio, Li-Yang Tan |
| 2014 | ICALP | On DNF Approximators for Monotone Boolean Functions. | Eric Blais, Johan Hstad, Rocco A. Servedio, Li-Yang Tan |
| 2014 | SODA | Hypercontractive inequalities via SOS, and the Frankl-Rdl graph. | Manuel Kauers, Ryan O'Donnell, Li-Yang Tan, Yuan Zhou |
| 2013 | FOCS | Learning Sums of Independent Integer Random Variables. | Constantinos Daskalakis, Ilias Diakonikolas, Ryan O'Donnell, Rocco A. Servedio, Li-Yang Tan |
| 2013 | ICALP | A Composition Theorem for the Fourier Entropy-Influence Conjecture. | Ryan O'Donnell, Li-Yang Tan |
| 2010 | STOC | Bounding the average sensitivity and noise sensitivity of polynomial threshold functions. | Ilias Diakonikolas, Prahladh Harsha, Adam R. Klivans, Raghu Meka, Prasad Raghavendra, Rocco A. Servedio, Li-Yang Tan |