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Alumni Profile: Yiding Feng, Microsoft Research Lab – New England
Feng received his PhD in computer science in 2021 and is now a postdoctoral researcher at Microsoft Research Lab – New England Yiding Feng (PhD ’21) is a postdoctoral researcher at Microsoft Research Lab – New England (MSR). He earned his PhD in computer science with...
published research
Publications
H. Shao, L. Cohen, A. Blum, Y. Mansour, A. Saha, M. Walter, Eliciting User Preferences for Personalized Multi-Objective Decision Making through Comparative Feedback. (2023).
K. Makarychev, Y. Makarychev, L. Shan, A. Vijayaraghavan, Higher-Order Cheeger Inequality for Partitioning with Buffers. (2023).
C. Carlson, J. Jafarov, K. Makarychev, Y. Makarychev, L. Shan, Approximation Algorithm for Norm Multiway Cut. (2023).
I. Hong, S. Na, M. Mahoney, M. Kolar, Constrained Optimization via Exact Augmented Lagrangian and Randomized Iterative Sketching. (2023).
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 here.
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 here.
Ren, J., Liu, C., Yu, G., & Guo, D. (2021). A New Distributed Method for Training Generative Adversarial Networks. ArXiv, abs/2107.0868.
Available here.
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 here.
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 here.
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 here.
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 here.
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 here.
Chao Gao and John Lafferty. Model Repair: Robust Recovery of Over-Parameterized Statistical Models, 2020. ArXiv, abs/2005.09912
Available here.
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 here.
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 here.
Nasir, Y.S., & Guo, D. (2020). Deep Reinforcement Learning for Joint Spectrum and Power Allocation in Cellular Networks. ArXiv, abs/2012.10682.
Available here.
Auerbach, E. (2020). Testing for Differences in Stochastic Network Structure. ArXiv, abs/1903.11117.
Available here.
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The Phase II operations of the IDEAL is supported by the National Science Foundation through the TRIPODS HDR program (under the awards EECS 2216970, 2217023, 2216926, 2216912, 2216899). The IDEAL Phase II institute builds on the activities of two NSF TRIPODS Phase 1 institutes: IDEAL Phase 1 (supported by the NSF award CCF 1934931) and UIC TRIPODS Institute (supported by the NSF award CCF 1934915).