Research
Operations Research
Computer Science
Optimization
Artificial Intelligence
Machine Learning
Matching Algorithms
Targeted Marketing
Sample Complexity
Neural Information Processing Systems
Algorithm Design
Information Systems
Management Science
Neural Network, Neural Networks
Pairwise Comparisons
Small Datasets
Computational Resources
Noisy Data
Social Networks, Social Network
Structural Analysis
List of Publications (51)
In 2023
51
Exemplary Artificial Intelligence Track Paper Award (awarded to one paper at EC 2023).
Found on CV
In 2019
50
Exemplary Artificial Intelligence Track Paper Award (awarded to one paper at EC 2019).
Found on CV
49
Best Presentation by a Student or Postdoctoral Researcher (EC 2019).
Found on CV
48
Invited to the ACM Transactions on Economics and Computation (TEAC) Special Issue for EC 2019.
Found on CV
Unspecified
47
Maria-Florina Balcan, Dan DeBlasio, Travis Dick, Carl Kingsford, Tuomas Sandholm, and Ellen Viter- cik.
Found on CV
46
How much data is sufficient to learn high-performing algorithms? Journal of the ACM (JACM).
Found on CV
45
Maria-Florina Balcan, Travis Dick, Tuomas Sandholm, and Ellen Vitercik.
Found on CV
44
Learning to branch: Generalization guarantees and limits of data-independent discretization.
Found on CV
43
Supersedes the ICML'20 and ICML'18 papers below.
Found on CV
42
Maria-Florina Balcan, Tuomas Sandholm, and Ellen Vitercik.
Found on CV
41
Generalization guarantees for multi-item profit maximization: Pricing, auctions, and randomized mech- anisms.
Found on CV
40
Joon Suk Huh, Ellen Vitercik, and Kirthevasan Kandasamy.
Found on CV
39
ACM Conference on Economics and Computation (EC).
Found on CV
38
Alexandre Hayderi, Amin Saberi, Ellen Vitercik, and Anders Wikum.
Found on CV
37
MAGNOLIA: Matching algorithms via GNNs for online value-to-go approximation.
Found on CV
36
International Conference on Machine Learning (ICML).
Found on CV
35
Wenshuo Guo, Nika Haghtalab, Kirthevasan Kandasamy, and Ellen Vitercik.
Found on CV
34
Leveraging reviews: Learning to price with buyer and seller uncertainty.
Found on CV
33
Christian Borgs, Jennifer Chayes, Christian Ikeokwu, and Ellen Vitercik.
Found on CV
32
ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO).
Found on CV
31
Maria-Florina Balcan, Siddharth Prasad, Tuomas Sandholm, and Ellen Vitercik.
Found on CV
30
Structural analysis of branch-and-cut and the learnability of Gomory mixed integer cuts.
Found on CV
29
Conference on Neural Information Processing Systems (NeurIPS).
Found on CV
28
Wenshuo Guo, Michael I. Jordan, and Ellen Vitercik.
Found on CV
27
Improved sample complexity bounds for branch-and-cut.
Found on CV
26
International Conference on Principles and Practice of Constraint Programming (CP).
Found on CV
25
Sample complexity of tree search configuration: Cutting planes and beyond.
Found on CV
24
Revenue maximization via machine learning with noisy data.
Found on CV
23
How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data- driven algorithm design.
Found on CV
22
ACM Symposium on Theory of Computing (STOC).
Found on CV
21
Andre s Mun oz Medina, Umar Syed, Sergei Vassilvitskii, and Ellen Vitercik.
Found on CV
20
International Conference on Artificial Intelligence and Statistics (AISTATS).
Found on CV
19
Refined bounds for algorithm configuration: The knife-edge of dual class approximability.
Found on CV
18
Learning to optimize computational resources: Frugal training with generalization guarantees.
Found on CV
17
Daniel Alabi, Adam Kalai, Katrina Ligett, Cameron Musco, Christos Tzamos, and Ellen Vitercik.
Found on CV
16
Learning to prune: Speeding up repeated computations.
Found on CV
15
Christian Borgs, Jennifer Chayes, Nika Haghtalab, Adam Kalai, and Ellen Vitercik.
Found on CV
14
Algorithmic greenlining: An approach to increase diversity.
Found on CV
13
AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AIES).
Found on CV
12
Maria-Florina Balcan, Travis Dick, and Ellen Vitercik.
Found on CV
11
Dispersion for data-driven algorithm design, online learning, and private optimization.
Found on CV
10
IEEE Symposium on Foundations of Computer Science (FOCS).
Found on CV
9
A general theory of sample complexity for multi-item profit maximization.
Found on CV
8
Bernhard Haeupler, Amirbehshad Shahrasbi, and Ellen Vitercik.
Found on CV
7
Synchronization strings: Channel simulations and interactive coding for insertions and deletions.
Found on CV
6
International Colloquium on Automata, Languages and Programming (ICALP).
Found on CV
5
Maria-Florina Balcan, Vaishnavh Nagarajan, Ellen Vitercik, and Colin White.
Found on CV
4
Learning-theoretic foundations of algorithm configuration for combinatorial partitioning problems.
Found on CV
3
Sample complexity of automated mechanism design.
Found on CV
2
Maria-Florina Balcan, Ellen Vitercik, and Colin White.
Found on CV
1
Learning combinatorial functions from pairwise comparisons.
Found on CV
