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2020 – today
- 2024
- [c68]Guy Blanc, Caleb Koch, Carmen Strassle, Li-Yang Tan:
A Strong Direct Sum Theorem for Distributional Query Complexity. CCC 2024: 16:1-16:30 - [c67]Caleb Koch, Carmen Strassle, Li-Yang Tan:
Superconstant Inapproximability of Decision Tree Learning. COLT 2024: 2979-3010 - [c66]Guy Blanc, Alexandre Hayderi, Caleb Koch, Li-Yang Tan:
The Sample Complexity of Smooth Boosting and the Tightness of the Hardcore Theorem. FOCS 2024: 1431-1450 - [c65]Caleb Koch, Carmen Strassle, Li-Yang Tan:
Fast Decision Tree Learning Solves Hard Coding-Theoretic Problems. FOCS 2024: 1893-1910 - [i71]Guy Blanc, Caleb Koch, Carmen Strassle, Li-Yang Tan:
A Strong Direct Sum Theorem for Distributional Query Complexity. CoRR abs/2405.16340 (2024) - [i70]Caleb Koch, Carmen Strassle, Li-Yang Tan:
Superconstant Inapproximability of Decision Tree Learning. CoRR abs/2407.01402 (2024) - [i69]Guy Blanc, Alexandre Hayderi, Caleb Koch, Li-Yang Tan:
The Sample Complexity of Smooth Boosting and the Tightness of the Hardcore Theorem. CoRR abs/2409.11597 (2024) - [i68]Caleb Koch, Carmen Strassle, Li-Yang Tan:
Fast decision tree learning solves hard coding-theoretic problems. CoRR abs/2409.13096 (2024) - 2023
- [c64]Konstantina Bairaktari, Guy Blanc, Li-Yang Tan, Jonathan R. Ullman, Lydia Zakynthinou
:
Multitask Learning via Shared Features: Algorithms and Hardness. COLT 2023: 747-772 - [c63]Guy Blanc, Caleb Koch, Carmen Strassle, Li-Yang Tan:
A strong composition theorem for junta complexity and the boosting of property testers. FOCS 2023: 1757-1777 - [c62]Caleb Koch, Carmen Strassle, Li-Yang Tan:
Properly learning decision trees with queries is NP-hard. FOCS 2023: 2383-2407 - [c61]Guy Blanc, Caleb Koch, Jane Lange, Carmen Strassle, Li-Yang Tan:
Certification with an NP Oracle. ITCS 2023: 18:1-18:22 - [c60]Guy Blanc, Jane Lange, Chirag Pabbaraju, Colin Sullivan, Li-Yang Tan, Mo Tiwari:
Harnessing the power of choices in decision tree learning. NeurIPS 2023 - [c59]Soheil Behnezhad, Moses Charikar, Weiyun Ma, Li-Yang Tan:
Single-Pass Streaming Algorithms for Correlation Clustering. SODA 2023: 819-849 - [c58]Caleb Koch, Carmen Strassle, Li-Yang Tan:
Superpolynomial lower bounds for decision tree learning and testing. SODA 2023: 1962-1994 - [c57]Guy Blanc, Jane Lange, Ali Malik, Li-Yang Tan:
Lifting Uniform Learners via Distributional Decomposition. STOC 2023: 1755-1767 - [i67]Guy Blanc, Jane Lange, Ali Malik, Li-Yang Tan:
Lifting uniform learners via distributional decomposition. CoRR abs/2303.16208 (2023) - [i66]Guy Blanc, Caleb Koch, Carmen Strassle, Li-Yang Tan:
A Strong Composition Theorem for Junta Complexity and the Boosting of Property Testers. CoRR abs/2307.04039 (2023) - [i65]Caleb Koch, Carmen Strassle, Li-Yang Tan:
Properly Learning Decision Trees with Queries Is NP-Hard. CoRR abs/2307.04093 (2023) - [i64]Guy Blanc, Jane Lange, Chirag Pabbaraju, Colin Sullivan, Li-Yang Tan, Mo Tiwari:
Harnessing the Power of Choices in Decision Tree Learning. CoRR abs/2310.01551 (2023) - 2022
- [j12]Ryan O'Donnell, Rocco A. Servedio
Fooling Polytopes. J. ACM 69(2): 9:1-9:37 (2022) - [j11]Guy Blanc
Properly Learning Decision Trees in almost Polynomial Time. J. ACM 69(6): 39:1-39:19 (2022) - [j10]Rocco A. Servedio, Li-Yang Tan:
Improved Pseudorandom Generators from Pseudorandom Multi-switching Lemmas. Adv. Math. Commun. 18: 1-46 (2022) - [c56]Victor Lecomte, Prasanna Ramakrishnan, Li-Yang Tan:
The Composition Complexity of Majority. CCC 2022: 19:1-19:26 - [c55]Guy Blanc, Jane Lange, Ali Malik, Li-Yang Tan:
On the power of adaptivity in statistical adversaries. COLT 2022: 5030-5061 - [c54]Soheil Behnezhad, Moses Charikar
Almost 3-Approximate Correlation Clustering in Constant Rounds. FOCS 2022: 720-731 - [c53]Guy Blanc, Jane Lange, Li-Yang Tan:
Reconstructing Decision Trees. ICALP 2022: 24:1-24:17 - [c52]Guy Blanc, Caleb Koch, Jane Lange, Li-Yang Tan:
A query-optimal algorithm for finding counterfactuals. ICML 2022: 2075-2090 - [c51]Guy Blanc, Jane Lange, Ali Malik, Li-Yang Tan:
Popular decision tree algorithms are provably noise tolerant. ICML 2022: 2091-2106 - [c50]Milan Mossé, Harry Sha, Li-Yang Tan:
A Generalization of the Satisfiability Coding Lemma and Its Applications. SAT 2022: 9:1-9:18 - [c49]Guy Blanc, Caleb Koch, Jane Lange, Li-Yang Tan:
The query complexity of certification. STOC 2022: 623-636 - [i63]Guy Blanc, Caleb Koch, Jane Lange, Li-Yang Tan:
The Query Complexity of Certification. CoRR abs/2201.07736 (2022) - [i62]Victor Lecomte, Prasanna Ramakrishnan, Li-Yang Tan:
The composition complexity of majority. CoRR abs/2205.02374 (2022) - [i61]Soheil Behnezhad, Moses Charikar
Almost 3-Approximate Correlation Clustering in Constant Rounds. CoRR abs/2205.03710 (2022) - [i60]Guy Blanc, Jane Lange, Ali Malik, Li-Yang Tan:
Popular decision tree algorithms are provably noise tolerant. CoRR abs/2206.08899 (2022) - [i59]Guy Blanc, Jane Lange, Mingda Qiao, Li-Yang Tan:
Open Problem: Properly learning decision trees in polynomial time? CoRR abs/2206.14431 (2022) - [i58]Guy Blanc, Caleb Koch, Jane Lange, Li-Yang Tan:
A Query-Optimal Algorithm for Finding Counterfactuals. CoRR abs/2207.07072 (2022) - [i57]Konstantina Bairaktari, Guy Blanc, Li-Yang Tan, Jonathan R. Ullman, Lydia Zakynthinou:
Multitask Learning via Shared Features: Algorithms and Hardness. CoRR abs/2209.03112 (2022) - [i56]Caleb Koch, Carmen Strassle, Li-Yang Tan:
Superpolynomial Lower Bounds for Decision Tree Learning and Testing. CoRR abs/2210.06375 (2022) - [i55]Guy Blanc, Caleb Koch, Jane Lange, Carmen Strassle, Li-Yang Tan:
Certification with an NP Oracle. CoRR abs/2211.02257 (2022) - 2021
- [c48]Rocco A. Servedio, Li-Yang Tan:
Deterministic Approximate Counting of Polynomial Threshold Functions via a Derandomized Regularity Lemma. APPROX-RANDOM 2021: 37:1-37:18 - [c47]Guy Blanc, Jane Lange, Mingda Qiao, Li-Yang Tan:
Decision Tree Heuristics Can Fail, Even in the Smoothed Setting. APPROX-RANDOM 2021: 45:1-45:16 - [c46]Noah Fleming, Mika Göös, Russell Impagliazzo
On the Power and Limitations of Branch and Cut. CCC 2021: 6:1-6:30 - [c45]Toniann Pitassi, Prasanna Ramakrishnan, Li-Yang Tan:
Tradeoffs for small-depth Frege proofs. FOCS 2021: 445-456 - [c44]Guy Blanc, Jane Lange, Mingda Qiao
Properly learning decision trees in almost polynomial time. FOCS 2021: 920-929 - [c43]Victor Lecomte, Li-Yang Tan:
Sharper bounds on the Fourier concentration of DNFs. FOCS 2021: 930-941 - [c42]Guy Blanc, Jane Lange, Li-Yang Tan:
Learning Stochastic Decision Trees. ICALP 2021: 30:1-30:16 - [c41]Guy Blanc, Jane Lange, Li-Yang Tan:
Provably efficient, succinct, and precise explanations. NeurIPS 2021: 6129-6141 - [c40]Moses Charikar
Brief Announcement: A Randomness-efficient Massively Parallel Algorithm for Connectivity. PODC 2021: 431-433 - [c39]Guy Blanc, Jane Lange, Li-Yang Tan:
Query strategies for priced information, revisited. SODA 2021: 1638-1650 - [i54]Noah Fleming, Mika Göös, Russell Impagliazzo, Toniann Pitassi, Robert Robere, Li-Yang Tan, Avi Wigderson:
On the Power and Limitations of Branch and Cut. CoRR abs/2102.05019 (2021) - [i53]Ryan O'Donnell, Rocco A. Servedio, Li-Yang Tan, Daniel Kane:
Fooling Gaussian PTFs via Local Hyperconcentration. CoRR abs/2103.07809 (2021) - [i52]Guy Blanc, Jane Lange, Li-Yang Tan:
Learning stochastic decision trees. CoRR abs/2105.03594 (2021) - [i51]Guy Blanc, Jane Lange, Mingda Qiao, Li-Yang Tan:
Decision tree heuristics can fail, even in the smoothed setting. CoRR abs/2107.00819 (2021) - [i50]Guy Blanc, Jane Lange, Mingda Qiao, Li-Yang Tan:
Properly learning decision trees in almost polynomial time. CoRR abs/2109.00637 (2021) - [i49]Victor Lecomte, Li-Yang Tan:
Sharper bounds on the Fourier concentration of DNFs. CoRR abs/2109.04525 (2021) - [i48]Guy Blanc, Jane Lange, Li-Yang Tan:
Provably efficient, succinct, and precise explanations. CoRR abs/2111.01576 (2021) - [i47]Toniann Pitassi, Prasanna Ramakrishnan, Li-Yang Tan:
Tradeoffs for small-depth Frege proofs. CoRR abs/2111.07483 (2021) - [i46]Guy Blanc, Jane Lange, Ali Malik, Li-Yang Tan:
On the power of adaptivity in statistical adversaries. CoRR abs/2111.10352 (2021) - [i45]Noah Fleming, Mika Göös, Russell Impagliazzo, Toniann Pitassi, Robert Robere, Li-Yang Tan, Avi Wigderson:
On the Power and Limitations of Branch and Cut. Electron. Colloquium Comput. Complex. TR21 (2021) - 2020
- [c38]Marshall Ball, Eshan Chattopadhyay, Jyun-Jie Liao, Tal Malkin, Li-Yang Tan:
Non-malleability Against Polynomial Tampering. CRYPTO (3) 2020: 97-126 - [c37]Andrew Bassilakis, Andrew Drucker, Mika Göös, Lunjia Hu
The Power of Many Samples in Query Complexity. ICALP 2020: 9:1-9:18 - [c36]Guy Blanc, Jane Lange, Li-Yang Tan:
Provable guarantees for decision tree induction: the agnostic setting. ICML 2020: 941-949 - [c35]Guy Blanc, Jane Lange, Li-Yang Tan:
Top-Down Induction of Decision Trees: Rigorous Guarantees and Inherent Limitations. ITCS 2020: 44:1-44:44 - [c34]Guy Blanc, Neha Gupta, Jane Lange, Li-Yang Tan:
Estimating decision tree learnability with polylogarithmic sample complexity. NeurIPS 2020 - [c33]Guy Blanc, Neha Gupta, Jane Lange, Li-Yang Tan:
Universal guarantees for decision tree induction via a higher-order splitting criterion. NeurIPS 2020 - [c32]Moses Charikar
Unconditional Lower Bounds for Adaptive Massively Parallel Computation. SPAA 2020: 141-151 - [c31]Ryan O'Donnell, Rocco A. Servedio
Fooling Gaussian PTFs via local hyperconcentration. STOC 2020: 1170-1183 - [i44]Moses Charikar, Weiyun Ma, Li-Yang Tan:
New lower bounds for Massively Parallel Computation from query complexity. CoRR abs/2001.01146 (2020) - [i43]Andrew Bassilakis, Andrew Drucker, Mika Göös, Lunjia Hu, Weiyun Ma, Li-Yang Tan:
The Power of Many Samples in Query Complexity. CoRR abs/2002.10654 (2020) - [i42]Guy Blanc, Jane Lange, Li-Yang Tan:
Provable guarantees for decision tree induction: the agnostic setting. CoRR abs/2006.00743 (2020) - [i41]Guy Blanc, Neha Gupta, Jane Lange, Li-Yang Tan:
Universal guarantees for decision tree induction via a higher-order splitting criterion. CoRR abs/2010.08633 (2020) - [i40]Guy Blanc, Jane Lange, Li-Yang Tan:
Query strategies for priced information, revisited. CoRR abs/2010.11381 (2020) - [i39]Guy Blanc, Neha Gupta, Jane Lange, Li-Yang Tan:
Estimating decision tree learnability with polylogarithmic sample complexity. CoRR abs/2011.01584 (2020) - [i38]Guy Blanc, Jane Lange, Li-Yang Tan:
Testing and reconstruction via decision trees. CoRR abs/2012.08735 (2020) - [i37]Marshall Ball
Non-Malleability against Polynomial Tampering. Electron. Colloquium Comput. Complex. TR20 (2020) - [i36]Andrew Bassilakis, Andrew Drucker, Mika Göös, Lunjia Hu, Weiyun Ma, Li-Yang Tan:
The Power of Many Samples in Query Complexity. Electron. Colloquium Comput. Complex. TR20 (2020) - [i35]Marshall Ball
Non-Malleability against Polynomial Tampering. IACR Cryptol. ePrint Arch. 2020: 147 (2020)
2010 – 2019
- 2019
- [c30]Rocco A. Servedio
Improved Pseudorandom Generators from Pseudorandom Multi-Switching Lemmas. APPROX-RANDOM 2019: 45:1-45:23 - [c29]Rocco A. Servedio
Pseudorandomness for read-k DNF formulas. SODA 2019: 621-638 - [c28]Ryan O'Donnell, Rocco A. Servedio
Fooling polytopes. STOC 2019: 614-625 - [i34]Guy Blanc, Jane Lange, Li-Yang Tan:
Top-down induction of decision trees: rigorous guarantees and inherent limitations. CoRR abs/1911.07375 (2019) - [i33]Guy Blanc, Jane Lange, Li-Yang Tan:
Constructive derandomization of query algorithms. CoRR abs/1912.03042 (2019) - [i32]Guy Blanc, Jane Lange, Li-Yang Tan:
Top-down induction of decision trees: rigorous guarantees and inherent limitations. Electron. Colloquium Comput. Complex. TR19 (2019) - 2018
- [j9]Xi Chen, Rocco A. Servedio
Settling the Query Complexity of Non-adaptive Junta Testing. J. ACM 65(6): 40:1-40:18 (2018) - [c27]Rocco A. Servedio
Luby-Velickovic-Wigderson Revisited: Improved Correlation Bounds and Pseudorandom Generators for Depth-Two Circuits. APPROX-RANDOM 2018: 56:1-56:20 - [c26]Marshall Ball
Non-Malleable Codes for Small-Depth Circuits. FOCS 2018: 826-837 - [i31]Rocco A. Servedio, Li-Yang Tan:
Deterministic search for CNF satisfying assignments in almost polynomial time. CoRR abs/1801.03588 (2018) - [i30]Rocco A. Servedio, Li-Yang Tan:
Improved pseudorandom generators from pseudorandom multi-switching lemmas. CoRR abs/1801.03590 (2018) - [i29]Marshall Ball, Dana Dachman-Soled, Siyao Guo, Tal Malkin, Li-Yang Tan:
Non-Malleable Codes for Small-Depth Circuits. CoRR abs/1802.07673 (2018) - [i28]Rocco A. Servedio, Li-Yang Tan:
Luby-Veličković-Wigderson revisited: Improved correlation bounds and pseudorandom generators for depth-two circuits. CoRR abs/1803.04553 (2018) - [i27]Ryan O'Donnell, Rocco A. Servedio, Li-Yang Tan:
Fooling Polytopes. CoRR abs/1808.04035 (2018) - [i26]Marshall Ball
Non-Malleable Codes for Small-Depth Circuits. Electron. Colloquium Comput. Complex. TR18 (2018) - [i25]Ryan O'Donnell, Rocco A. Servedio, Li-Yang Tan:
Fooling Polytopes. Electron. Colloquium Comput. Complex. TR18 (2018) - [i24]Marshall Ball
Non-Malleable Codes for Small-Depth Circuits. IACR Cryptol. ePrint Arch. 2018: 207 (2018) - 2017
- [j8]Johan Håstad, Benjamin Rossman, Rocco A. Servedio
An Average-Case Depth Hierarchy Theorem for Boolean Circuits. J. ACM 64(5): 35:1-35:27 (2017) - [c25]Xi Chen, Rocco A. Servedio, Li-Yang Tan, Erik Waingarten:
Adaptivity Is Exponentially Powerful for Testing Monotonicity of Halfspaces. APPROX-RANDOM 2017: 38:1-38:21 - [c24]Xi Chen, Rocco A. Servedio
Settling the Query Complexity of Non-Adaptive Junta Testing. CCC 2017: 26:1-26:19 - [c23]Rocco A. Servedio
Deterministic Search for CNF Satisfying Assignments in Almost Polynomial Time. FOCS 2017: 813-823 - [c22]Rocco A. Servedio
Fooling Intersections of Low-Weight Halfspaces. FOCS 2017: 824-835 - [c21]Rocco A. Servedio
What Circuit Classes Can Be Learned with Non-Trivial Savings?. ITCS 2017: 30:1-30:21 - [i23]Rocco A. Servedio, Li-Yang Tan:
Fooling intersections of low-weight halfspaces. CoRR abs/1704.04855 (2017) - [i22]Xi Chen, Rocco A. Servedio, Li-Yang Tan, Erik Waingarten, Jinyu Xie:
Settling the query complexity of non-adaptive junta testing. CoRR abs/1704.06314 (2017) - [i21]Xi Chen, Rocco A. Servedio, Li-Yang Tan, Erik Waingarten:
Adaptivity is exponentially powerful for testing monotonicity of halfspaces. CoRR abs/1706.05556 (2017) - [i20]Xi Chen, Rocco A. Servedio, Li-Yang Tan, Erik Waingarten, Jinyu Xie:
Settling the query complexity of non-adaptive junta testing. Electron. Colloquium Comput. Complex. TR17 (2017) - 2016
- [c20]Xi Chen, Igor C. Oliveira, Rocco A. Servedio
Near-optimal small-depth lower bounds for small distance connectivity. STOC 2016: 612-625 - [c19]Toniann Pitassi, Benjamin Rossman, Rocco A. Servedio
Poly-logarithmic Frege depth lower bounds via an expander switching lemma. STOC 2016: 644-657 - 2015
- [j7]Ilias Diakonikolas, Ragesh Jaiswal, Rocco A. Servedio, Li-Yang Tan, Andrew Wan:
Noise Stable Halfspaces are Close to Very Small Juntas. Chic. J. Theor. Comput. Sci. 2015 (2015) - [j6]Eric Blais, Li-Yang Tan:
Approximating Boolean Functions with Depth-2 Circuits. SIAM J. Comput. 44(6): 1583-1600 (2015) - [j5]Benjamin Rossman, Rocco A. Servedio, Li-Yang Tan:
Complexity Theory Column 89: The Polynomial Hierarchy, Random Oracles, and Boolean Circuits. SIGACT News 46(4): 50-68 (2015) - [c18]Eric Blais, Clément L. Canonne, Igor C. Oliveira, Rocco A. Servedio
Learning Circuits with few Negations. APPROX-RANDOM 2015: 512-527 - [c17]Rocco A. Servedio
Adaptivity Helps for Testing Juntas. CCC 2015: 264-279 - [c16]Benjamin Rossman, Rocco A. Servedio
An Average-Case Depth Hierarchy Theorem for Boolean Circuits. FOCS 2015: 1030-1048 - [c15]Shaddin Dughmi, Nicole Immorlica, Ryan O'Donnell, Li-Yang Tan:
Algorithmic Signaling of Features in Auction Design. SAGT 2015: 150-162 - [c14]Dana Dachman-Soled, Vitaly Feldman, Li-Yang Tan, Andrew Wan, Karl Wimmer:
Approximate resilience, monotonicity, and the complexity of agnostic learning. SODA 2015: 498-511 - [c13]Xi Chen, Anindya De, Rocco A. Servedio
Boolean Function Monotonicity Testing Requires (Almost) n1/2 Non-adaptive Queries. STOC 2015: 519-528 - [i19]Benjamin Rossman, Rocco A. Servedio, Li-Yang Tan:
An average-case depth hierarchy theorem for Boolean circuits. CoRR abs/1504.03398 (2015) - [i18]Eric Blais, Li-Yang Tan, Andrew Wan:
An inequality for the Fourier spectrum of parity decision trees. CoRR abs/1506.01055 (2015) - [i17]Xi Chen, Igor C. Oliveira, Rocco A. Servedio, Li-Yang Tan:
Near-optimal small-depth lower bounds for small distance connectivity. CoRR abs/1509.07476 (2015) - [i16]Benjamin Rossman, Rocco A. Servedio, Li-Yang Tan:
An average-case depth hierarchy theorem for Boolean circuits. Electron. Colloquium Comput. Complex. TR15 (2015) - 2014
- [b1]Li-Yang Tan:
Analytic Methods in Concrete Complexity. Columbia University, USA, 2014 - [j4]Ilias Diakonikolas, Prasad Raghavendra, Rocco A. Servedio
Average Sensitivity and Noise Sensitivity of Polynomial Threshold Functions. SIAM J. Comput. 43(1): 231-253 (2014) - [j3]Ilias Diakonikolas, Rocco A. Servedio
A Regularity Lemma and Low-Weight Approximators for Low-Degree Polynomial Threshold Functions. Theory Comput. 10: 27-53 (2014) - [j2]Per Austrin, Ryan O'Donnell, Li-Yang Tan, John Wright:
New NP-Hardness Results for 3-Coloring and 2-to-1 Label Cover. ACM Trans. Comput. Theory 6(1): 2:1-2:20 (2014) - [c12]Ryan O'Donnell, John Wright, Yu Zhao, Xiaorui Sun, Li-Yang Tan:
A Composition Theorem for Parity Kill Number. CCC 2014: 144-154 - [c11]Xi Chen, Rocco A. Servedio
New Algorithms and Lower Bounds for Monotonicity Testing. FOCS 2014: 286-295 - [c10]Eric Blais, Johan Håstad
On DNF Approximators for Monotone Boolean Functions. ICALP (1) 2014: 235-246 - [c9]Manuel Kauers, Ryan O'Donnell, Li-Yang Tan, Yuan Zhou:
Hypercontractive inequalities via SOS, and the Frankl-Rödl graph. SODA 2014: 1644-1658 - [i15]Dana Dachman-Soled, Vitaly Feldman, Li-Yang Tan, Andrew Wan, Karl Wimmer:
Approximate resilience, monotonicity, and the complexity of agnostic learning. CoRR abs/1405.5268 (2014) - [i14]Eric Blais, Clément L. Canonne, Igor C. Oliveira, Rocco A. Servedio, Li-Yang Tan:
Learning circuits with few negations. CoRR abs/1410.8420 (2014) - [i13]Xi Chen, Rocco A. Servedio, Li-Yang Tan:
New algorithms and lower bounds for monotonicity testing. CoRR abs/1412.5655 (2014) - [i12]Xi Chen, Anindya De, Rocco A. Servedio, Li-Yang Tan:
Boolean function monotonicity testing requires (almost) n1/2 non-adaptive queries. CoRR abs/1412.5657 (2014) - [i11]Eric Blais, Clément L. Canonne, Igor Carboni Oliveira, Rocco A. Servedio, Li-Yang Tan:
Learning circuits with few negations. Electron. Colloquium Comput. Complex. TR14 (2014) - 2013
- [j1]Eric Blais, Li-Yang Tan:
Hypercontractivity Via the Entropy Method. Theory Comput. 9: 889-896 (2013) - [c8]Dominik Scheder
On the Average Sensitivity and Density of k-CNF Formulas. APPROX-RANDOM 2013: 683-698 - [c7]Eric Blais, Li-Yang Tan:
Approximating Boolean Functions with Depth-2 Circuits. CCC 2013: 74-85 - [c6]Constantinos Daskalakis, Ilias Diakonikolas, Ryan O'Donnell, Rocco A. Servedio
Learning Sums of Independent Integer Random Variables. FOCS 2013: 217-226 - [c5]Ryan O'Donnell, Li-Yang Tan:
A Composition Theorem for the Fourier Entropy-Influence Conjecture. ICALP (1) 2013: 780-791 - [i10]Ryan O'Donnell, Li-Yang Tan:
A composition theorem for the Fourier Entropy-Influence conjecture. CoRR abs/1304.1347 (2013) - [i9]Ryan O'Donnell, Xiaorui Sun, Li-Yang Tan, John Wright, Yu Zhao:
A composition theorem for parity kill number. CoRR abs/1312.2143 (2013) - [i8]Eric Blais, Li-Yang Tan:
Approximating Boolean functions with depth-2 circuits. Electron. Colloquium Comput. Complex. TR13 (2013) - 2012
- [c4]Rocco A. Servedio, Li-Yang Tan, Justin Thaler:
Attribute-Efficient Learning and Weight-Degree Tradeoffs for Polynomial Threshold Functions. COLT 2012: 14.1-14.19 - [i7]Ilias Diakonikolas, Ragesh Jaiswal, Rocco A. Servedio, Li-Yang Tan, Andrew Wan:
On the Distribution of the Fourier Spectrum of Halfspaces. CoRR abs/1202.6680 (2012) - [i6]Li-Yang Tan:
Analysis of Boolean Functions. CoRR abs/1205.0314 (2012) - [i5]Per Austrin, Ryan O'Donnell, Li-Yang Tan, John Wright:
New NP-hardness results for 3-Coloring and 2-to-1 Label Cover. CoRR abs/1210.5648 (2012) - [i4]Manuel Kauers, Ryan O'Donnell, Li-Yang Tan, Yuan Zhou:
Hypercontractive inequalities via SOS, with an application to Vertex-Cover. CoRR abs/1212.5324 (2012) - [i3]Rocco A. Servedio, Li-Yang Tan, Justin Thaler:
Attribute-Efficient Learning and Weight-Degree Tradeoffs for Polynomial Threshold Functions. Electron. Colloquium Comput. Complex. TR12 (2012) - 2010
- [c3]Ilias Diakonikolas, Rocco A. Servedio, Li-Yang Tan, Andrew Wan:
A Regularity Lemma, and Low-Weight Approximators, for Low-Degree Polynomial Threshold Functions. CCC 2010: 211-222 - [c2]Ilias Diakonikolas, Prahladh Harsha
Bounding the average sensitivity and noise sensitivity of polynomial threshold functions. STOC 2010: 533-542
2000 – 2009
- 2009
- [i2]Ilias Diakonikolas, Rocco A. Servedio, Li-Yang Tan, Andrew Wan:
A regularity lemma, and low-weight approximators, for low-degree polynomial threshold functions. CoRR abs/0909.4727 (2009) - [i1]Ilias Diakonikolas, Prasad Raghavendra, Rocco A. Servedio, Li-Yang Tan:
Average sensitivity and noise sensitivity of polynomial threshold functions. CoRR abs/0909.5011 (2009) - 2005
- [c1]Aaron Stump, Li-Yang Tan:
The Algebra of Equality Proofs. RTA 2005: 469-483
Coauthor Index
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