NeurIPS 2019 Workshop on Machine Learning with Guarantees

Accepted Papers

• Marcu, Antonia; Prugel-Bennett, Adam, "Rethinking generalisation" (poster)
• Yang, Yao-Yuan; Rashtchian, Cyrus; Wang, Yizhen; Chaudhuri, Kamalika, "Adversarial Examples for Non-Parametric Methods: Attacks, Defenses and Large Sample Limits" (poster)
• Gourdeau, Pascale; Kanade, Varun; Kwiatkowska, Marta; Worrell, James, "On the Hardness of Robust Classification"
• Zhu, Chen; Ni, Renkun; Chiang, Ping-yeh; Li, Hengduo; Huang, Furong; Goldstein, Tom, "Improved Training of Certifiably Robust Models"
• Blum, Avrim; Lykouris, Thodoris, "Advancing subgroup fairness via sleeping experts"
• Chi, Jianfeng; Zhao, Han; Tian, Yuan; Gordon, Geoff, "Adversarial Privacy Preservation under Attribute Inference Attack" (poster)
• Sypherd, Tyler; Sankar, Lalitha; Diaz, Mario; Kairouz, Peter; Dasarathy, Gautam, "A Class of Parameterized Loss Functions for Classification: Optimization Tradeoffs and Robustness Characteristics" (poster)
• Kozdoba, Mark; Moroshko, Edward; Mannor, Shie; Crammer, Koby, "Variance Estimation For Dynamic Regression via Spectrum Thresholding" (poster)
• Bhagoji, Arjun Nitin; Cullina, Daniel; Mittal, Prateek, "Lower Bounds on Adversarial Robustness from Optimal Transport"
• Wu, Xiaoxia; Du, Simon S; Xie, Yuege; Ward, Rachel, "Global Convergence of Adaptive Gradient Methodsfor An Over-parameterized Neural Network"
• Nandy, Jay; Hsu, Wynne; Lee, Mong Li, "Robustness for Adversarial $\ell_{p\geq 1}$ Perturbations" (poster)
• Smith, Michael T; Grosse, Kathrin; Backes, Michael; Alvarez, Mauricio A, "Adversarial Vulnerability Bounds for Gaussian Process Classification"
• Wen, Bingyang; Aydore, Sergul, "ROMark: A Robust Watermarking System Using Adversarial Training"
• Xie, Yuege; Wu, Xiaoxia; Ward, Rachel, "Linear Convergence of Adaptive Stochastic Gradient Descent" (poster)
• Pitas, Konstantinos, "Some limitations of norm based generalization bounds in deep neural networks"
• Shit, Suprosanna; Ravi, Abinav; Ezhov, Ivan; Lipkova, Jana; Piraud, Marie; Menze, Bjoern, "Implicit Neural Solver for Time-dependent Linear PDEs with Convergence Guarantee" (poster)
• Yang, Yichen; Rinard, Martin, "Correctness Verification of Neural Networks" (poster)
• Andriushchenko, Maksym; Hein, Matthias, "Provably Robust Boosted Decision Stumps and Trees against Adversarial Attacks" (poster)
• Wang, Xuezhi; Thain, Nithum; Sinha, Anu; Chi, Ed; Chen, Jilin; Beutel, Alex, "Practical Compositional Fairness: Understanding Fairness in Multi-Task ML Systems" (poster)
• Dong, Bin; Hou, Jikai; Lu, Yiping; Zhang, Zhihua, "Distillation $\approx$ Early Stopping? Harvesting Dark Knowledge by Self-Distillation"
• Arora, Sanjeev; Du, Simon S; Li, Zhiyuan; Salakhutdinov, Ruslan; Wang, Ruosong; Yu, Dingli, "Harnessing the Power of Infinitely Wide Deep Nets on Small-data Tasks"
• Letarte, Gaël; Germain, Pascal; Guedj, Benjamin; Laviolette, Francois, "Dichotomize and Generalize: PAC-Bayesian Binary Activated Deep Neural Networks" (poster)
• Hu, Wei; Li, Zhiyuan; Yu, Dingli, "Simple and Effective Regularization Methods for Training on Noisily Labeled Data with Generalization Guarantee"
• Khodak, Mikhail; Balcan, Maria-Florina; Talwalkar, Ameet, "Adaptive Gradient-Based Meta-Learning Methods" (poster)
• Mozannar, Hussein; Ohannessian, Mesrob; Srebro, Nathan, "Fair Learning with Private Data" (poster)
• Freeman, Rupert; Pennock, David; Podimata, Chara; Wortman Vaughan, Jennifer, "No-Regret and Incentive-Compatible Prediction with Expert Advice"
• Chen, Yiling; Liu, Yang; Podimata, Chara, "Grinding the Space: Learning to Classify Against Strategic Agents"
• Foulds, Jimmy; Islam, Rashidul; Keya, Kamrun Naher; Pan, Shimei , "Differential Fairness" (poster)
• Wang, Yizhen; Chaudhuri, Kamalika; Jha, Somesh, "An Investigation of Data Poisoning Defenses for Online Learning" (poster)
• Zhang, Huishuai, "Stability and Convergence Theory of Learning ResNet: A Full Characterization"
• Kuzelka, Ondrej; Wang, Yuyi, "Generalization Bounds for Knowledge Graph Embedding (Trained by Maximum Likelihood)"
• Levine, Alexander J; Singla, Sahil; Feizi, Soheil, "Certifiably Robust Interpretation in Deep Learning" (poster)
• Axelrod, Brian; Garg, Shivam; Sharan, Vatsal; Valiant, Gregory, "Sample Amplification: Increasing Dataset Size even when Learning is Impossible"
• Meinke, Alexander; Hein, Matthias, "Towards neural networks that provably know when they don't know"
• Lu, Nan; Zhang, Tianyi; Niu, Gang; Menon, Aditya K; Sugiyama, Masashi, "Learning from Only Unlabeled Data via Empirical Risk Minimization"
• Pentina, Anastasia; Lampert, Christoph H, "Multi-source domain adaptation with guarantees" (poster)
• Rizk, Geovani; Colin, Igor; Thomas, Albert; Draief, Moez, "Refined bounds for randomized experimental design"
• Mhammedi, Zakaria; Grunwald, Peter; Guedj, Benjamin, "PAC-Bayes Un-Expected Bernstein Inequality"
• Viallard, Paul; Emonet, Rémi; Germain, Pascal; Habrard, Amaury; Morvant, Emilie, "Interpreting Neural Networks as Majority Votes through the PAC-Bayesian Theory" (poster)
• Cai, Diana; Campbell, Trevor; Broderick, Tamara, "Finite mixture models are typically inconsistent for the number of components"
• Gondara, Lovedeep Singh; Wang, Ke; Silva Carvalho, Ricardo, "Winning Privately: The Differentially Private Lottery Ticket Mechanism "
• Zhao, Han; Coston, Amanda L; Adel, Tameem; Gordon, Geoff, "Conditional Learning of Fair Representations"
• Croce, Francesco; Hein, Matthias, "Provable robustness against all adversarial $l_p$-perturbations for $p\geq 1$"
• Lucas, James R; Ren, Mengye; Zemel, Richard, "Information-theoretic limitations on novel task generalization"
• Mahdaviyeh, Yasamansadat, "Asymptotic Risk of Least Squares Minimum Norm Estimator under Spike Covariance Model"
• Gillot, Pierre; Parviainen, Pekka, "Large-scale Bayesian network structure learning with quality guarantees" (poster)
• Ledent, Antoine; Lei, Yunwen; Kloft, Marius, "Improved Generalisation Bounds for Deep Learning Through $L^\infty$ Covering Numbers"
• Baratin, Aristide, "Implicit Regularization in Deep Learning: A View from Function Space"
• Bommasani, Rishi; Steven Wu, Zhiwei; Schofield, Alexandra K, "Towards Private Synthetic Text Generation"
• Barp, Alessandro, "Minimum Stein Discrepancy Estimators"
• Daskalakis, Constantinos; Ilyas, Andrew; Rao, Sujit; Zampetakis, Emmanouil, "A Framework for Learning from Truncated Samples"
• Wu, Kaiwen; Yu, Yaoliang, "Understanding Adversarial Robustness: The Trade-off between Minimum and Average Margin" (poster)
• Behrmann, Jens; Vicol, Paul; Wang, Kuan-Chieh; Jacobsen, Joern-Henrik, "On the Invertibility of Invertible Neural Networks"
• Rivasplata, Omar; Kuzborskij , Ilja; Szepesvari, Csaba; Shawe-Taylor, John, "PAC-Bayes Analysis Beyond the Usual Bounds" (poster)
• Banijamali, Ershad; Ghodsi, Ali, "Semi-Supervised Dimensionality Reduction via Probabilistic Labeling with Performance Guarantee"
• Nazemi, Amir; Fieguth, Paul, "Potential adversarial samples for white-box attacks" (poster)
• Carmon, Yair; Raghunathan, Aditi; Schmidt, Ludwig; Liang, Percy; Duchi, John, "Unlabeled Data Improves Adversarial Robustness "
• Stephenson, William T; Broderick, Tamara, "Approximate Cross-Validation in High Dimensions with Guarantees"
• Singla, Sahil; Feizi, Soheil, "Curvature based robustness certificates against adversarial examples" (poster)
• Gupta, Akhil; shukla, naman; Marla, Lavanya; Kolbeinsson, Arinbjörn; Yellepeddi, Kartik, "How to Incorporate Monotonicity in Deep Networks While Preserving Flexibility?" (poster)
• Choi, YooJung; Farnadi, Golnoosh; Babaki, Behrouz; Van den Broeck, Guy, "Learning Fair Naive Bayes Classifiers by Discovering and Eliminating Discrimination Patterns"
• Mell, Stephen; Brown, Olivia; Goodwin, Justin; Son, Sung-Hyun, "Safe Predictors for Enforcing Input-Output Specifications" (poster)
• Kilcher, Yannic; Roth, Kevin; Hofmann, Thomas, "Adversarial Training Generalizes Data-dependent Spectral Norm Regularization" (poster)
• Lyle, Clare; Bloem-Reddy, Benjamin; Gal, Yarin; Kwiatkowska, Marta, "Generalization Bounds for Invariant Neural Networks"
• Manino, Edoardo; Tran-Thanh, Long; Jennings, Nicholas R, "Streaming Bayesian Inference for Crowdsourced Classification" (poster)
• Bennett, Andrew; Kallus, Nathan, "Policy Evaluation with Latent Confounders via Optimal Balance"
• Shah, Devavrat; Xie, Qiaomin; Xu, Zhi, "On Reinforcement Learning For Turn-Based Zero-Sum Markov Games"
• Chatterji, Niladri S; Neyshabur, Behnam; Sedghi, Hanie, "The intriguing role of module criticality in the generalization of deep networks"
• Barut, Emre; Luo, Shunyan, "Statistically Consistent Saliency Estimation"
• Ba, Jimmy; Erdogdu, Murat; Suzuki, Taiji; Wu, Yi; Zhang, Tianzong, "Generalization of Two-layer Neural Networks: An Asymptotic Viewpoint"
• Gur-Ari, Guy; Dyer, Ethan, "Asymptotics of Wide Networks from Feynman Diagrams"
• Prost, Flavien; Chen, Jilin; Beutel, Alex; Chi, Ed; Chen, Qiuwen; Qian, Hai, "Toward a better trade-off between performance and fairness with kernel-based distribution matching"
• Toro Icarte, Rodrigo A; Illanes, León; Castro, Margarita; Cire, Andre A.; McIlraith, Sheila A.; Beck, Christopher, "Training Binarized Neural Networks using MIP and CP (Abridged Report)"
• Blaas, Arno; Patane, Andrea; Laurenti, Luca; Kwiatkowska, Marta; Cardelli, Luca; Roberts, Stephen, "Adversarial Robustness Guarantees for Classification with Gaussian Processes"
• Gu, Jindong; Tresp, Volker, "Neural Network Memorization Dissection"
• Yun, Chulhee; Bhojanapalli, Srinadh; Rawat, Ankit Singh; Reddi, Sashank; Kumar, Sanjiv, "Are Transformers universal approximators of sequence-to-sequence functions?"
• Lale, Ali Sahin; Azizzadenesheli, Kamyar; Anandkumar, Animashree; Hassibi, Babak, "Stochastic Linear Bandits with Hidden Low Rank Structure" (poster)
• Jiang, YiDing; Neyshabur, Behnam; Mobahi, Hossein; Krishnan, Dilip; Yak, Scott; Bengio, Samy, "Fantastic Generalization Measures and Where to Find Them"
• Medini, Tharun; Shrivastava, Anshumali, "A Deep Dive into Count-Min Sketch for Extreme Classification in Logarithmic Memory"
• Sharan, Vatsal; Wang, Xin; Juba, Brendan; Panigrahy, Rina, "Understanding the Capabilities and Limitations of Neural Networks for Multi-task Learning"
• Rezaei, Ashkan; Fathony, Rizal; Memarrast, Omid; Ziebart, Brian, "Fairness for Robust Log Loss Classification" (poster)
• Garg, Siddhant; Akash, Aditya Kumar, "Stochastic Bandits with Delayed Composite Anonymous Feedback" (poster)
• Pydi, Muni Sreenivas S; Lokhande, Vishnu , "Active Learning with Importance Sampling" (poster)
• MacAulay, Noah G, "Low Intrinsic Dimension Implies Generalization"
• Xu, Minjie; Kazantsev, Gary, "Understanding Goal-Oriented Active Learning via Influence Functions" (poster)