Hanie Sedghi

According to our database, Hanie Sedghi authored at least 41 papers between 2010 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models.

Avi Singh, John D. Co-Reyes, Rishabh Agarwal, Ankesh Anand, Piyush Patil, Xavier Garcia, Peter J. Liu, James Harrison, Jaehoon Lee, Kelvin Xu, Aaron T. Parisi, Abhishek Kumar, Alexander A. Alemi, Alex Rizkowsky, Azade Nova, Ben Adlam, Bernd Bohnet, Gamaleldin Fathy Elsayed, Hanie Sedghi, Igor Mordatch, Isabelle Simpson, Izzeddin Gur, Jasper Snoek, Jeffrey Pennington, Jiri Hron, Kathleen Kenealy, Kevin Swersky, Kshiteej Mahajan, Laura Culp, Lechao Xiao, Maxwell L. Bileschi, Noah Constant, Roman Novak, Rosanne Liu, Tris Warkentin, Yundi Qian, Yamini Bansal, Ethan Dyer, Behnam Neyshabur, Jascha Sohl-Dickstein, Noah Fiedel

Trans. Mach. Learn. Res., 2024

Training Language Models on the Knowledge Graph: Insights on Hallucinations and Their Detectability.

Jiri Hron, Laura Culp, Gamaleldin F. Elsayed, Rosanne Liu, Ben Adlam, Maxwell L. Bileschi, Bernd Bohnet, JD Co-Reyes, Noah Fiedel, C. Daniel Freeman, Izzeddin Gur, Kathleen Kenealy, Jaehoon Lee, Peter J. Liu, Gaurav Mishra, Igor Mordatch, Azade Nova, Roman Novak, Aaron Parisi, Jeffrey Pennington, Alex Rizkowsky, Isabelle Simpson, Hanie Sedghi, Jascha Sohl-Dickstein, Kevin Swersky, Sharad Vikram, Tris Warkentin, Lechao Xiao, Kelvin Xu, Jasper Snoek, Simon Kornblith

CoRR, 2024

Exploring and Benchmarking the Planning Capabilities of Large Language Models.

Bernd Bohnet, Azade Nova, Aaron T. Parisi, Kevin Swersky, Katayoon Goshvadi, Hanjun Dai, Dale Schuurmans, Noah Fiedel, Hanie Sedghi

CoRR, 2024

Long-Span Question-Answering: Automatic Question Generation and QA-System Ranking via Side-by-Side Evaluation.

Bernd Bohnet, Kevin Swersky, Rosanne Liu, Pranjal Awasthi, Azade Nova, Javier Snaider, Hanie Sedghi, Aaron T. Parisi, Michael Collins, Angeliki Lazaridou, Orhan Firat, Noah Fiedel

CoRR, 2024

2023

Frontier Language Models are not Robust to Adversarial Arithmetic, or "What do I need to say so you agree 2+2=5?

C. Daniel Freeman, Laura Culp, Aaron Parisi, Maxwell L. Bileschi, Gamaleldin F. Elsayed, Alex Rizkowsky, Isabelle Simpson, Alex Alemi, Azade Nova, Ben Adlam, Bernd Bohnet, Gaurav Mishra, Hanie Sedghi, Igor Mordatch, Izzeddin Gur, Jaehoon Lee, John D. Co-Reyes, Jeffrey Pennington, Kelvin Xu, Kevin Swersky, Kshiteej Mahajan, Lechao Xiao, Rosanne Liu, Simon Kornblith, Noah Constant, Peter J. Liu, Roman Novak, Yundi Qian, Noah Fiedel, Jascha Sohl-Dickstein

CoRR, 2023

The Role of Pre-training Data in Transfer Learning.

Rahim Entezari, Mitchell Wortsman, Olga Saukh, Moein Shariatnia, Hanie Sedghi, Ludwig Schmidt

CoRR, 2023

Can Neural Network Memorization Be Localized?

Pratyush Maini, Michael Curtis Mozer, Hanie Sedghi, Zachary Chase Lipton, J. Zico Kolter, Chiyuan Zhang

Proceedings of the International Conference on Machine Learning, 2023

REPAIR: REnormalizing Permuted Activations for Interpolation Repair.

Keller Jordan, Hanie Sedghi, Olga Saukh, Rahim Entezari, Behnam Neyshabur

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Leveraging Unlabeled Data to Track Memorization.

Mahsa Forouzesh, Hanie Sedghi, Patrick Thiran

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Layer-Stack Temperature Scaling.

Amr Khalifa, Michael C. Mozer, Hanie Sedghi, Behnam Neyshabur, Ibrahim Alabdulmohsin

CoRR, 2022

Teaching Algorithmic Reasoning via In-context Learning.

Hattie Zhou, Azade Nova, Hugo Larochelle, Aaron C. Courville, Behnam Neyshabur, Hanie Sedghi

CoRR, 2022

Understanding the effect of sparsity on neural networks robustness.

Lukas Timpl, Rahim Entezari, Hanie Sedghi, Behnam Neyshabur, Olga Saukh

CoRR, 2022

Leveraging unlabeled data to predict out-of-distribution performance.

Saurabh Garg, Sivaraman Balakrishnan, Zachary Chase Lipton, Behnam Neyshabur, Hanie Sedghi

Proceedings of the Tenth International Conference on Learning Representations, 2022

The Role of Permutation Invariance in Linear Mode Connectivity of Neural Networks.

Rahim Entezari, Hanie Sedghi, Olga Saukh, Behnam Neyshabur

Proceedings of the Tenth International Conference on Learning Representations, 2022

Exploring the Limits of Large Scale Pre-training.

Samira Abnar, Mostafa Dehghani, Behnam Neyshabur, Hanie Sedghi

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Gradual Domain Adaptation in the Wild: When Intermediate Distributions are Absent.

Samira Abnar, Rianne van den Berg, Golnaz Ghiasi, Mostafa Dehghani, Nal Kalchbrenner, Hanie Sedghi

CoRR, 2021

The Deep Bootstrap Framework: Good Online Learners are Good Offline Generalizers.

Preetum Nakkiran, Behnam Neyshabur, Hanie Sedghi

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

The Deep Bootstrap: Good Online Learners are Good Offline Generalizers.

Preetum Nakkiran, Behnam Neyshabur, Hanie Sedghi

CoRR, 2020

What is being transferred in transfer learning?

Behnam Neyshabur, Hanie Sedghi, Chiyuan Zhang

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Generalization bounds for deep convolutional neural networks.

Philip M. Long, Hanie Sedghi

Proceedings of the 8th International Conference on Learning Representations, 2020

The intriguing role of module criticality in the generalization of deep networks.

Niladri S. Chatterji, Behnam Neyshabur, Hanie Sedghi

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

On the Effect of the Activation Function on the Distribution of Hidden Nodes in a Deep Network.

Philip M. Long, Hanie Sedghi

Neural Comput., 2019

Size-free generalization bounds for convolutional neural networks.

Philip M. Long, Hanie Sedghi

CoRR, 2019

SysML: The New Frontier of Machine Learning Systems.

Alexander Ratner, Dan Alistarh, Gustavo Alonso, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Eric S. Chung, Bill Dally, Jeff Dean, Inderjit S. Dhillon, Alexandros G. Dimakis, Pradeep Dubey, Charles Elkan, Grigori Fursin, Gregory R. Ganger, Lise Getoor, Phillip B. Gibbons, Garth A. Gibson, Joseph E. Gonzalez, Justin Gottschlich, Song Han, Kim M. Hazelwood, Furong Huang, Martin Jaggi, Kevin G. Jamieson, Michael I. Jordan, Gauri Joshi, Rania Khalaf, Jason Knight, Jakub Konecný, Tim Kraska, Arun Kumar, Anastasios Kyrillidis, Jing Li, Samuel Madden, H. Brendan McMahan, Erik Meijer, Ioannis Mitliagkas, Rajat Monga, Derek Gordon Murray, Dimitris S. Papailiopoulos, Gennady Pekhimenko, Theodoros Rekatsinas, Afshin Rostamizadeh, Christopher Ré, Christopher De Sa, Hanie Sedghi, Siddhartha Sen, Virginia Smith, Alex Smola, Dawn Song, Evan Randall Sparks, Ion Stoica, Vivienne Sze, Madeleine Udell, Joaquin Vanschoren, Shivaram Venkataraman, Rashmi Vinayak, Markus Weimer, Andrew Gordon Wilson, Eric P. Xing, Matei Zaharia, Ce Zhang, Ameet Talwalkar

CoRR, 2019

The Singular Values of Convolutional Layers.

Hanie Sedghi, Vineet Gupta, Philip M. Long

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

Knowledge Completion for Generics Using Guided Tensor Factorization.

Hanie Sedghi, Ashish Sabharwal

Trans. Assoc. Comput. Linguistics, 2018

2017

How Good Are My Predictions? Efficiently Approximating Precision-Recall Curves for Massive Datasets.

Ashish Sabharwal, Hanie Sedghi

Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence, 2017

2016

Training Input-Output Recurrent Neural Networks through Spectral Methods.

Hanie Sedghi, Anima Anandkumar

CoRR, 2016

Provable Tensor Methods for Learning Mixtures of Generalized Linear Models.

Hanie Sedghi, Majid Janzamin, Anima Anandkumar

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015

Statistical Structure Learning to Ensure Data Integrity in Smart Grid.

Hanie Sedghi, Edmond A. Jonckheere

IEEE Trans. Smart Grid, 2015

Provable Methods for Training Neural Networks with Sparse Connectivity.

Hanie Sedghi, Anima Anandkumar

Proceedings of the 3rd International Conference on Learning Representations, 2015

Generalization Bounds for Neural Networks through Tensor Factorization.

Majid Janzamin, Hanie Sedghi, Anima Anandkumar

CoRR, 2015

Score Function Features for Discriminative Learning.

Majid Janzamin, Hanie Sedghi, Anima Anandkumar

Proceedings of the 3rd International Conference on Learning Representations, 2015

Learning Mixed Membership Community Models in Social Tagging Networks through Tensor Methods.

Anima Anandkumar, Hanie Sedghi

CoRR, 2015

FEAST at Play: Feature ExtrAction using Score function Tensors.

Majid Janzamin, Hanie Sedghi, U. N. Niranjan, Animashree Anandkumar

Proceedings of the 1st Workshop on Feature Extraction: Modern Questions and Challenges, 2015

2014

Statistical Structure Learning, Towards a Robust Smart Grid.

Hanie Sedghi, Edmond A. Jonckheere

CoRR, 2014

Guarantees for Multi-Step Stochastic ADMM in High Dimensions.

Hanie Sedghi, Anima Anandkumar, Edmond A. Jonckheere

CoRR, 2014

Provable Tensor Methods for Learning Mixtures of Classifiers.

Hanie Sedghi, Anima Anandkumar

CoRR, 2014

Score Function Features for Discriminative Learning: Matrix and Tensor Framework.

Majid Janzamin, Hanie Sedghi, Anima Anandkumar

CoRR, 2014

Multi-Step Stochastic ADMM in High Dimensions: Applications to Sparse Optimization and Matrix Decomposition.

Hanie Sedghi, Anima Anandkumar, Edmond A. Jonckheere

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

2010

A Game-Theoretic Approach for Power Allocation in Bidirectional Cooperative Communication.

Majid Janzamin, Mohammad Reza Pakravan, Hanie Sedghi

Proceedings of the 2010 IEEE Wireless Communications and Networking Conference, 2010