2018
Friday September 28, 3-4 pm, 3401 Walnut St Room 401B
1. Seth Neel: “Mitigating Bias in Adaptive Data Gathering via Differential Privacy”. Seth Neel, Aaron Roth . ICML 2018
2. Arpit Agarwal: “Accelerated Spectral Ranking”. Arpit Agarwal, Prathamesh Patil, Shivani Agarwal. ICML 2018
3. Shamya Karumbaiah: “Predicting Quitting in Students Playing a Learning Game”. Shamya Karumbaiah, Ryan S Baker, Valerie Shute. Educational Data Mining (EDM), 2018 (nominated best paper)
4. Jieming Mao: “Contextual Pricing for Lipschitz Buyers”. Jieming Mao, Renato Paes Leme, Jon Schneider. NIPS 2018
5. Matthew Joseph: “Local Differential Privacy for Evolving Data”. Matthew Joseph, Aaron Roth, Jonathan Ullman, Bo Waggoner. NIPS 2018
6. Christopher Jung: “Online Learning with an Unknown Fairness Metric”. Stephen Gillen, Christopher Jung, Michael Kearns, Aaron Roth. NIPS 2018
7. Jorge A. Mendez: “Lifelong Inverse Reinforcement Learning”. Jorge A. Mendez, Shashank Shivkumar, Eric Eaton. NIPS 2018
8. Osbert Bastani: “Verifiable Reinforcement Learning via Policy Extraction”. Osbert Bastani, Yewen Pu, Armando Solar-Lezama. NIPS 2018
9. Xujie Si: “Learning Loop Invariants for Program Verification”. Xujie Si, Hanjun Dai, Mukund Raghothaman, Mayur Naik, Le Song. NIPS 2018
10. Shahin Jabbari: “Fair Algorithms for Learning in Allocation Problems”. Hadi Elzayn, Shahin Jabbari, Christopher Jung, Michael Kearns, Seth Neel, Aaron Roth, Zachary Schutzman. 2018 (Under Submission)
11. Yibo Yang: “Physics-informed Neural Nets and Applications in Uncertainty Quantification”. Yibo Yang, Paris Perdikaris. 2018 (Under Submission)
12. Mark Eisen: “Learning Optimal Resource Allocations in Wireless Systems”. Mark Eisen, Clark Zhang, Luiz F. O. Chamon, Daniel D. Lee, Alejandro Ribeiro. 2018 (Under Submission)
2017
Friday September 15, 3-4 pm, 3401 Walnut St Room 401B
1. Seth Neel: “Accuracy First: Selecting a Differential Privacy Level for ERM“. Seth Neel, Katrina Ligett, Aaron Roth, Steven Wu, Bo Waggoner. NIPS 2017
2. Santiago Paternain: “Second Order Method for Nonconvex Optimization“. Santiago Paternain, Aryan Mokhtari, and Alejandro Ribeiro. SIAM Journal on Optimization (SIOPT), submitted
3. Arpit Agarwal: “Learning with Limited Rounds of Adaptivity: Coin Tossing, Multi-Armed Bandits, and Ranking from Pairwise Comparisons”. Arpit Agarwal, Shivani Agarwal, Sepehr Assadi, Sanjeev Khanna. COLT 2017
4. Anupama Jha: “Integrative deep models for alternative splicing”. Anupama Jha, Matthew R Gazzara, Yoseph Barash. ISMB 2017
5. Lyle Ungar: “EmoNet: Fine-Grained Emotion Detection with Gated Recurrent Neural Networks”. Muhammad Abdul-Mageed and Lyle Ungar. ACL 2017
6. Matthew Joseph: “Fairness in Reinforcement Learning“. Shahin Jabbari, Matthew Joseph, Michael Kearns, Jamie Morgenstern, Aaron Roth. ICML 2017
7. Gedas Bertasius: “Convolutional Random Walk Networks for Semantic Image Segmentation“. Gedas Bertasius, Lorenzo Torresani, Stella X. Yu, Jianbo Shi. CVPR 2017
8. Aaron Roth: “A Smoothed Regret Analysis of the Greedy Algorithm for Linear Contextual Bandits”. Sampath Kannan, Jamie Morgenstern, Aaron Roth, Bo Waggoner, Steven Wu. In preparation
9. Luiz Chamon: “Approximate Supermodularity Bounds for Experimental Design”. Luiz Chamon and Alejandro Ribeiro. NIPS 2017
10. Hamed Hassani: “Stochastic Submodular Maxizimation”. NIPS 2017
11. Alejandro Ribeiro: “Adaptive Newton Method for Empirical Risk Minimization to Statistical Accuracy“. Aryan Mokhtari, Hadi Daneshmand, Aurelien Lucchi, Thomas Hofmann, and Alejandro Ribeiro. NIPS 2016
