| New algorithms and lower bounds for monotonicity testing X Chen, RA Servedio, LY Tan FOCS 2014, 2014 | 86 | 2014 |
| Boolean function monotonicity testing requires (almost) n^{1/2} non-adaptive queries X Chen, A De, RA Servedio, LY Tan STOC 2015, 2015 | 74 | 2015 |
| Convergence, unanimity and disagreement in majority dynamics on unimodular graphs and random graphs I Benjamini, SO Chan, R O’Donnell, O Tamuz, LY Tan Stochastic Processes and their Applications, 2016 | 61 | 2016 |
| An average-case depth hierarchy theorem for boolean circuits B Rossman, RA Servedio, LY Tan FOCS 2015, 2015 | 60 | 2015 |
| Learning sums of independent integer random variables C Daskalakis, I Diakonikolas, R ODonnell, RA Servedio, LY Tan FOCS 2013, 2013 | 58 | 2013 |
| Bounding the average sensitivity and noise sensitivity of polynomial threshold functions I Diakonikolas, P Harsha, A Klivans, R Meka, P Raghavendra, ... STOC 2010, 2010 | 52 | 2010 |
| A regularity lemma, and low-weight approximators, for low-degree polynomial threshold functions I Diakonikolas, RA Servedio, LY Tan, A Wan CCC 2010, 2010 | 49 | 2010 |
| Learning circuits with few negations E Blais, CL Canonne, IC Oliveira, RA Servedio, LY Tan RANDOM 2015, 2014 | 48 | 2014 |
| Non-malleable codes for small-depth circuits M Ball, D Dachman-Soled, S Guo, T Malkin, LY Tan FOCS 2018, 2018 | 47 | 2018 |
| Average sensitivity and noise sensitivity of polynomial threshold functions I Diakonikolas, P Raghavendra, RA Servedio, LY Tan SIAM Journal on Computing, 2014 | 46* | 2014 |
| Provably efficient, succinct, and precise explanations G Blanc, J Lange, LY Tan Advances in Neural Information Processing Systems 34, 6129-6141, 2021 | 39 | 2021 |
| Settling the query complexity of non-adaptive junta testing X Chen, RA Servedio, LY Tan, E Waingarten, J Xie Journal of the ACM, 2018 | 37 | 2018 |
| Poly-logarithmic frege depth lower bounds via an expander switching lemma T Pitassi, B Rossman, RA Servedio, LY Tan STOC 2016, 2016 | 37 | 2016 |
| Hypercontractive inequalities via SOS, and the Frankl–Rödl graph M Kauers, R O'Donnell, LY Tan, Y Zhou SODA 2014, 2014 | 37 | 2014 |
| Attribute-efficient learning and weight-degree tradeoffs for polynomial threshold functions R Servedio, LY Tan, J Thaler COLT 2012, 2012 | 36 | 2012 |
| An average-case depth hierarchy theorem for boolean circuits J Håstad, B Rossman, RA Servedio, LY Tan Journal of the ACM, 2017 | 35 | 2017 |
| Approximate resilience, monotonicity, and the complexity of agnostic learning D Dachman-Soled, V Feldman, LY Tan, A Wan, K Wimmer SODA 2015, 2014 | 31 | 2014 |
| Near-optimal small-depth lower bounds for small distance connectivity X Chen, IC Oliveira, RA Servedio, LY Tan STOC 2016, 2016 | 28 | 2016 |
| Adaptivity Helps for Testing Juntas RA Servedio, LY Tan, J Wright CCC 2015, 2015 | 28 | 2015 |
| Almost 3-approximate correlation clustering in constant rounds S Behnezhad, M Charikar, W Ma, LY Tan 2022 IEEE 63rd Annual Symposium on Foundations of Computer Science (FOCS …, 2022 | 26 | 2022 |