1;95;0c Possible Papers for Presentations

Presentation Dates

11/22/17: Kevin Vissuet (Ailon et al. '08), Joe Berner (Alon et al. '98)
11/27/17: Mao Li (Netrapalli et al. '14), Hongwi Jin (Lee '16)
11/29/17: Yingyi Ma (Zhang et al. '15), Darko Trifunovski (Balcan et al. '09)
12/1/17: Karly Brint (Chen et al. '99), Jonathan Wolf (Blum et al. '03)
12/4/17: Debjyoti Saharoy (Moitra '17), Shelby Heinecke (Lattanzi and Sivakumar '09)
12/6/17: Lujia Wang (Larsen and Nelson '17), Samuel Shideler (Ben-David et al. '06)
12/8/17: Matthew Kaplan (Arora et al '12), Mansavi Kumar (Balcan et al. '13)

Possible Papers

Papers that have already been selected by a student are crossed out from this list.

Random Graphs and Communities

Noga Alon, Michael Krivelevich, Benny Sudakov. Finding a large hidden clique in a random graph. SODA 1998: 594-598
Silvio Lattanzi, D. Sivakumar. Affiliation networks. STOC 2009: 427-434
Ravi Kumar, Prabhakar Raghavan, Sridhar Rajagopalan, D. Sivakumar, Andrew Tomkins, Eli Upfal. Stochastic models for the web graph. FOCS 2000: 57-65
Sam Cole, Shmuel Friedland, Lev Reyzin A Simple Spectral Algorithm for Recovering Planted Partitions Special Matrices 2017: 5:139-157
Sanjeev Arora, Rong Ge, Sushant Sachdeva, Grant Schoenebeck. Finding overlapping communities in social networks: toward a rigorous approach. EC 2012: 37-54
Maria-Florina Balcan, Christian Borgs, Mark Braverman, Jennifer T. Chayes, Shang-Hua Teng. Finding Endogenously Formed Communities. SODA 2013: 767-783 2012

Random Walks and Markov Chains

Dimitris Bertsimas, Santosh Vempala. Solving convex programs by random walks. J. ACM 51(4): 540-556 (2004)
Fang Chen, Laszlo Lovasz, Igor Pak. Lifting Markov Chains to Speed up Mixing. STOC 1999: 275-281
Yoav Freund, Michael J. Kearns, Dana Ron, Ronitt Rubinfeld, Robert E. Schapire, Linda Sellie. Efficient learning of typical finite automata from random walks. STOC 1993: 315-324
Daniel A. Spielman, Nikhil Srivastava. Graph sparsification by effective resistances. STOC 2008: 563-568

Streaming and Sketching Algorithms

Michael Kapralov, Jelani Nelson, Jakub Pachocki, Zhengyu Wang, David P. Woodruff, Mobin Yahyazadeh. Optimal lower bounds for universal relation, samplers, and finding duplicates in streams. FOCS 2017
Moses Charikar, Liadan O'Callaghan, Rina Panigrahy. Better streaming algorithms for clustering problems. STOC 2003: 30-39
David P. Woodruff. Optimal space lower bounds for all frequency moments. SODA 2004: 167-175
Edo Liberty. Simple and deterministic matrix sketching. KDD 2013: 581-588
Amit Chakrabarti, Graham Cormode, Navin Goyal, Justin Thaler. Annotations for Sparse Data Streams. SODA 2014: 687-706

Clustering and Partitioning

Maria-Florina Balcan, Avrim Blum, Anupam Gupta. Approximate clustering without the approximation. SODA 2009: 1068-1077
Shai Ben-David, Ulrike von Luxburg, David Pal. A Sober Look at Clustering Stability. COLT 2006: 5-19
Frank McSherry. Spectral Partitioning of Random Graphs. FOCS 2001: 529-537
Nir Ailon, Moses Charikar, Alantha Newman. Aggregating inconsistent information: Ranking and clustering. J. ACM 55(5) (2008)
Noga Alon, Seannie Dar, Michal Parnas, Dana Ron. Testing of Clustering. SIAM J. Discrete Math. 16(3): 393-417 (2003)
Shalev Ben-David, Lev Reyzin. Data Stability in Clustering: A Closer Look. Theor. Comp. Sci. 558: 51-61 (2014)


Praneeth Netrapalli, UN Niranjan, Sujay Sanghavi, Animashree Anandkumar, and Prateek Jain. Non-convex robust PCA. NIPS 2014: 1107-1115.
Navin Goyal, Santosh Vempala, Ying Xiao. Fourier PCA and robust tensor decomposition. STOC 2014: 584-593
Siu On Chan, Dimitris Papailiopoulos, Aviad Rubinstein. On the Approximability of Sparse PCA. COLT 2016: 623-646.
Kasper Green Larsen, Jelani Nelson. Optimality of the Johnson-Lindenstrauss lemma. FOCS 2017


Ankur Moitra, Gregory Valiant. Settling the Polynomial Learnability of Mixtures of Gaussians. FOCS 2010: 93-102
Yuchen Zhang, Jason Lee, Michael I. Jordan. l1-regularized Neural Networks are Improperly Learnable in Polynomial Time. ICML 2016.
Avrim Blum, Adam Kalai, Hal Wasserman. Noise-tolerant learning, the parity problem, and the statistical query model. J. ACM 50(4): 506-519 (2003)
Sanjeev Arora, Rong Ge, Ankur Moitra. A spectral algorithm for learning Hidden Markov Models. J. Comput. Syst. Sci. 78(5): 1460-1480 (2012)

Other Topics

Ankur Moitra. Approximate Counting and the Lovasz Local Lemma. STOC 2017, 356-369.
Jason Lee. Gradient Descent Converges to Minimizers. COLT 2016.
Vitaly Feldman, Will Perkins, Santosh Vempala. On the Complexity of Random Satisfiability Problems with Planted Solutions. STOC 15, 77-86
Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toni Pitassi, Omer Reingold, Aaron Roth. The Reusable Holdout: Preserving Validity in Adaptive Data Analysis Science 2015: Vol 349, Issue 6248, pp. 636-638