Publications

S. Yang, S. Khuller, S. Choudhary, S. Mitra, K. Mahadik, Correlated Stochastic Knapsack with a Submodular Objective. (2022)

S. Ahmadi; P. Awasthi; S. Khuller; M. Kleindessner; J. Morgenstern; P. Sukprasert, Individual Preference Stability for Clustering. (2022)
 
S. Yang, S. Khuller, S. Choudhary, S. Mitra, K. Mahadik, Scheduling ML training on unreliable spot instances. UCC ’21: Proceedings of the 14th IEEE/ACM International Conference on Utility and Cloud Computing Companion- December 2021. Available from https://dl.acm.org/doi/10.1145/3492323.3495594.
 

Lang, H., Reddy, A., Sontag, D., & Vijayaraghavan, A. (2021). Beyond Perturbation Stability: LP Recovery Guarantees for MAP Inference on Noisy Stable Instances. AISTATS. ArXiv, abs/2103.00034. Available from https://arxiv.org/pdf/2103.00034.pdf.

Ren, J., Liu, C., Yu, G., & Guo, D. (2021). A New Distributed Method for Training Generative Adversarial Networks. ArXiv, abs/2107.0868. Available from https://arxiv.org/pdf/2107.08681.pdf.

Chen, A., De, A. & Vijayaraghavan, A.. (2021). Learning a mixture of two subspaces over finite fields. Proceedings of the 32nd International Conference on Algorithmic Learning Theory, in Proceedings of Machine Learning Research 132:481-504. ArXiv, abs/2010.02841. Available from https://arxiv.org/pdf/2010.02841.pdf.

Awasthi, P., Tang, A.K., & Vijayaraghavan, A. (2021). Efficient Algorithms for Learning Depth-2 Neural Networks with General ReLU Activations. ArXiv, abs/2107.10209. Available from https://arxiv.org/pdf/2107.10209.pdf.

Jafarov, J., Kalhan, S., Makarychev, K.; Makarychev, Y. (2021). Local Correlation Clustering with Asymmetric Classification Errors. Proceedings of the 38th International Conference on Machine Learning in Proceedings of Machine Learning Research 139:4677-4686. ArXiv, abs/2108.05697. Available from https://arxiv.org/pdf/2108.05697.pdf.

Makarychev, K.; Shan, L.. (2021). Near-Optimal Algorithms for Explainable k-Medians and k-Means. Proceedings of the 38th International Conference on Machine Learning, in Proceedings of Machine Learning Research 139:7358-7367. ArXiv, abs/2107.00798. Available from https://arxiv.org/pdf/2107.00798.pdf. 

Makarychev, Y. & Vakilian, A.. (2021). Approximation Algorithms for Socially Fair Clustering. Proceedings of Thirty Fourth Conference on Learning Theory, in Proceedings of Machine Learning Research 134:3246-3264. ArXiv, abs/2103.02512. Available from https://arxiv.org/pdf/2103.02512.pdf. 

Chao Gao and John Lafferty. Model Repair: Robust Recovery of Over-Parameterized Statistical Models, 2020. ArXiv, abs/2005.09912. Available from https://arxiv.org/pdf/2005.09912.pdf. 

Pinhan Chen, Chao Gao and Anderson Zhang. Partial Recovery for Top-k Ranking: Optimality of MLE and Sub-Optimality of Spectral Method, 2020. ArXiv, abs/2006.16485. Available from https://arxiv.org/pdf/2006.16485.pdf. 

P. Poojary and R. Berry. Observational Learning with Fake Agents, 2020. IEEE International Symposium on Information Theory (ISIT), Los Angeles, CA, 2020. ArXiv, abs/2005.05518. Available from https://arxiv.org/pdf/2005.05518.pdf. 

Birge, John R. and Candogan, Ozan and Feng, Yiding, Controlling Epidemic Spread: Reducing Economic Losses with Targeted Closures (May 18, 2020). University of Chicago, Becker Friedman Institute for Economics Working Paper No. 2020- 57. Available at SSRN: https://ssrn.com/abstract=3590621 or http://dx.doi.org/10.2139/ssrn.3590621.

Nasir, Y.S., & Guo, D. (2020). Deep Reinforcement Learning for Joint Spectrum and Power Allocation in Cellular Networks. ArXiv, abs/2012.10682. Available from https://arxiv.org/pdf/2012.10682.pdf.

Auerbach, E. (2020). Testing for Differences in Stochastic Network Structure. ArXiv, abs/1903.11117. Available from https://arxiv.org/pdf/1903.11117.pdf.