Linear Projections of Teacher Embeddings for Few-Class Distillation
with Noel Loo, Wei Hu and Erik Vee.
Manuscript, September 2024.
[Arxiv]
SLaM: Student-Label Mixing for Distillation with Unlabeled Examples
with Vasilis Kontonis, Cenk Baykal, Gaurav Menghani, Khoa Trinh and Erik Vee. Proceedings of the 37th Conference on Neural Information Processing Systems (NeurIPS), November 2023.
[Arxiv]
Robust Active Distillation
with Cenk Baykal, Khoa Trihn, Gaurav Menghani and Erik Vee. Proceedings of the 11th International Conference on Learning Representations (ICLR), May 2023.
[Arxiv]
Weighted Distillation with Unlabeled Examples
with Vasilis Kontonis, Cenk Baykal, Gaurav Menghani, Khoa Trinh and Erik Vee. Proceedings of the 36th Conference on Neural Information Processing Systems (NeurIPS), November 2022.
[Arxiv]
Improved bounds for coloring locally sparse hypergraphs
Proceedings of Approximation, Randomization, and Combinatorial Optimization (APPROX/RANDOM), August 2021, pp 39:1-39:16
Invited to to the Theory of Computing (ToC) Special Issue for APPROX/RANDOM 2021
[Arxiv]
A new notion of commutativity for the algorithmic Lovasz Local Lemma
with David G. Harris and Vladimir Kolmogorov. Proceedings of Approximation, Randomization, and Combinatorial Optimization (APPROX/RANDOM), August 2021, pp. 31:1-31:25
[Arxiv]
Group testing and local search:
is there a computational-statistical gap?
with Ilias Zadik. Proceedings of the 34th Annual Conference on Learning Theory (COLT), August 2021, pp. 2499-2551.
[Arxiv]
Simple Local Computation Algorithms for the General Lovasz Local Lemma
with Dimitris Achlioptas and Themis Gouleakis. Proceedings of the 32nd ACM Symposium on Parallelism in Algorithms and Architectures (SPAA), July 2020, pp. 1-10.
[Arxiv]
Efficiently list-edge coloring multigraphs asymptotically optimally
with Alistair Sinclair. Random Stuctures & Algorithms, Volume 61, pp. 724-753, 2022. A preliminary version appeared in the proceedings of the 31st ACM-SIAM Symposium on Discrete Algorithms (SODA), January 2020, pp. 2319-2336.
[Arxiv]
Beyond the Lovasz Local Lemma: Point to Set Correlations and Their Algorithmic Applications
with Dimitris Achlioptas and Alistair Sinclair. Proceedings of the 60th IEEE Symposium on Foundations of Computer Science (FOCS), November 2019, pp. 725-744.
[Arxiv]
A Local Lemma for Focused Stochastic Algorithms
with Dimitris Achlioptas and Vladimir Kolmogorov. SIAM Journal on Computing (SICOMP), Volume 48(5):1583-602; 2019.
[Arxiv][local copy]
Commutative Algorithms Approximate the LLL-distribution
Proceedings of Approximation, Randomization, and Combinatorial Optimization (APPROX/RANDOM), August 2018, pp. 44:1 - 44:20
[Arxiv]
Stochastic Control via Entropy Compression
with Dimitris Achlioptas and Nikos Vlassis. Proceedings of the 44th International Colloquium on Automata, Language and Programming (ICALP), July 2017, pp. 83:1 - 83:13.
[Arxiv]
Focused Stochastic Local Search and the Lovasz Local Lemma
with Dimitris Achlioptas. Proceedings of the 27th ACM-SIAM Symposium on Discrete Algorithms (SODA), January 2016, pp. 2024-2038.
[Arxiv]
Random walks that Find Perfect Objects and the Lovasz Local Lemma
with Dimitris Achlioptas. Journal of the ACM (J. ACM), Volume 63(3): 22:1-22:29; 2016. A preliminary version appeared in the proceedings of the 55th IEEE Annual Symposium on Foundations of Computer Science (FOCS), November 2014, pp. 494-503
[Arxiv]