A Game-Theoretic Approach for Power Allocation in Bidirectional Cooperative Communication. - Majid Janzamin; Mohammad Reza Pakravan; Hanie Sedghi (WCNC, 2010)

Multi-Step Stochastic ADMM in High Dimensions: Applications to Sparse Optimization and Matrix Decomposition. - Hanie Sedghi; Anima Anandkumar; Edmond A. Jonckheere (NIPS, 2014)

Score Function Features for Discriminative Learning: Matrix and Tensor Framework. - Majid Janzamin; Hanie Sedghi; Anima Anandkumar (CoRR, 2014)

Provable Tensor Methods for Learning Mixtures of Classifiers. - Hanie Sedghi; Anima Anandkumar (CoRR, 2014)

Guarantees for Multi-Step Stochastic ADMM in High Dimensions. - Hanie Sedghi; Anima Anandkumar; Edmond A. Jonckheere (CoRR, 2014)

Statistical Structure Learning, Towards a Robust Smart Grid. - Hanie Sedghi; Edmond A. Jonckheere (CoRR, 2014)

FEAST at Play: Feature ExtrAction using Score function Tensors. - Majid Janzamin; Hanie Sedghi; U. N. Niranjan; Animashree Anandkumar (FE@NIPS, 2015)

Learning Mixed Membership Community Models in Social Tagging Networks through Tensor Methods. - Anima Anandkumar; Hanie Sedghi (CoRR, 2015)

Score Function Features for Discriminative Learning. - Majid Janzamin; Hanie Sedghi; Anima Anandkumar (ICLR (Workshop), 2015)

Generalization Bounds for Neural Networks through Tensor Factorization. - Majid Janzamin; Hanie Sedghi; Anima Anandkumar (CoRR, 2015)

Provable Methods for Training Neural Networks with Sparse Connectivity. - Hanie Sedghi; Anima Anandkumar (ICLR (Workshop), 2015)

Statistical Structure Learning to Ensure Data Integrity in Smart Grid. - Hanie Sedghi; Edmond A. Jonckheere (IEEE Trans. Smart Grid, 2015)

Provable Tensor Methods for Learning Mixtures of Generalized Linear Models. - Hanie Sedghi; Majid Janzamin; Anima Anandkumar (AISTATS, 2016)

Training Input-Output Recurrent Neural Networks through Spectral Methods. - Hanie Sedghi; Anima Anandkumar (CoRR, 2016)

How Good Are My Predictions? Efficiently Approximating Precision-Recall Curves for Massive Datasets. - Ashish Sabharwal; Hanie Sedghi (UAI, 2017)

Knowledge Completion for Generics Using Guided Tensor Factorization. - Hanie Sedghi; Ashish Sabharwal (Trans. Assoc. Comput. Linguistics, 2018)

The Singular Values of Convolutional Layers. - Hanie Sedghi; Vineet Gupta 001; Philip M. Long (ICLR (Poster), 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; et al. (CoRR, 2019)

Size-free generalization bounds for convolutional neural networks. - Philip M. Long; Hanie Sedghi (CoRR, 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)

The intriguing role of module criticality in the generalization of deep networks. - Niladri S. Chatterji; Behnam Neyshabur; Hanie Sedghi (ICLR, 2020)

Generalization bounds for deep convolutional neural networks. - Philip M. Long; Hanie Sedghi (ICLR, 2020)

What is being transferred in transfer learning? - Behnam Neyshabur; Hanie Sedghi; Chiyuan Zhang (NeurIPS, 2020)

The Deep Bootstrap: Good Online Learners are Good Offline Generalizers. - Preetum Nakkiran; Behnam Neyshabur; Hanie Sedghi (CoRR, 2020)

The Deep Bootstrap Framework: Good Online Learners are Good Offline Generalizers. - Preetum Nakkiran; Behnam Neyshabur; Hanie Sedghi (ICLR, 2021)

Gradual Domain Adaptation in the Wild: When Intermediate Distributions are Absent. - Samira Abnar; Rianne van den Berg; Golnaz Ghiasi; Mostafa Dehghani 001; Nal Kalchbrenner; Hanie Sedghi (CoRR, 2021)

Exploring the Limits of Large Scale Pre-training. - Samira Abnar; Mostafa Dehghani 001; Behnam Neyshabur; Hanie Sedghi (ICLR, 2022)

The Role of Permutation Invariance in Linear Mode Connectivity of Neural Networks. - Rahim Entezari; Hanie Sedghi; Olga Saukh; Behnam Neyshabur (ICLR, 2022)

Leveraging unlabeled data to predict out-of-distribution performance. - Saurabh Garg; Sivaraman Balakrishnan; Zachary Chase Lipton; Behnam Neyshabur; Hanie Sedghi (ICLR, 2022)

Understanding the effect of sparsity on neural networks robustness. - Lukas Timpl; Rahim Entezari; Hanie Sedghi; Behnam Neyshabur; Olga Saukh (CoRR, 2022)

Teaching Algorithmic Reasoning via In-context Learning. - Hattie Zhou; Azade Nova; Hugo Larochelle; Aaron C. Courville; Behnam Neyshabur; Hanie Sedghi (CoRR, 2022)

Layer-Stack Temperature Scaling. - Amr Khalifa; Michael C. Mozer; Hanie Sedghi; Behnam Neyshabur; Ibrahim Alabdulmohsin (CoRR, 2022)

Leveraging Unlabeled Data to Track Memorization. - Mahsa Forouzesh; Hanie Sedghi; Patrick Thiran (ICLR, 2023)

REPAIR: REnormalizing Permuted Activations for Interpolation Repair. - Keller Jordan; Hanie Sedghi; Olga Saukh; Rahim Entezari; Behnam Neyshabur (ICLR, 2023)

Can Neural Network Memorization Be Localized? - Pratyush Maini; Michael Curtis Mozer; Hanie Sedghi; Zachary Chase Lipton; J. Zico Kolter; Chiyuan Zhang (ICML, 2023)

The Role of Pre-training Data in Transfer Learning. - Rahim Entezari; Mitchell Wortsman; Olga Saukh; Moein Shariatnia; Hanie Sedghi; Ludwig Schmidt (CoRR, 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; et al. (CoRR, 2023)

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 001; Angeliki Lazaridou; Orhan Firat; Noah Fiedel (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)

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; et al. (CoRR, 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 001; Kelvin Xu; Aaron T. Parisi; Abhishek Kumar; et al. (Trans. Mach. Learn. Res., 2024)

Improving Large Language Model Planning with Action Sequence Similarity. - Xinran Zhao; Hanie Sedghi; Bernd Bohnet; Dale Schuurmans; Azade Nova (ICLR, 2025)