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

Maximizing Fair Content Spread via Edge Suggestion in Social Networks. - Ian P. Swift; Sana Ebrahimi; Azade Nova; Abolfazl Asudeh (Proc. VLDB Endow., 2022)

LFPS: Learned Formal Proof Strengthening for Efficient Hardware Verification. - Minwoo Kang; Azade Nova; Eshan Singh; Geetheeka Sharron Bathini; Yuriy Viktorov (ICCAD, 2023)

Gradient-Free Structured Pruning with Unlabeled Data. - Azade Nova; Hanjun Dai; Dale Schuurmans (ICML, 2023)

Revisiting Sampling for Combinatorial Optimization. - Haoran Sun; Katayoon Goshvadi; Azade Nova; Dale Schuurmans; Hanjun Dai (ICML, 2023)

DISCS: A Benchmark for Discrete Sampling. - Katayoon Goshvadi; Haoran Sun; Xingchao Liu; Azade Nova; Ruqi Zhang; Will Grathwohl; Dale Schuurmans; Hanjun Dai (NeurIPS, 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)

Many-Shot In-Context Learning. - Rishabh Agarwal; Avi Singh; Lei Zhang; Bernd Bohnet; Luis Rosias; Stephanie C. Y. Chan; Biao Zhang; Ankesh Anand; Zaheer Abbas; Azade Nova; John D. Co-Reyes; Eric Chu; et al. (NeurIPS, 2024)

UQE: A Query Engine for Unstructured Databases. - Hanjun Dai; Bethany Wang; Xingchen Wan; Bo Dai 001; Sherry Yang 001; Azade Nova; Pengcheng Yin; Phitchaya Mangpo Phothilimthana; Charles Sutton; Dale Schuurmans (NeurIPS, 2024)

Many-Shot In-Context Learning. - Rishabh Agarwal; Avi Singh; Lei M. Zhang; Bernd Bohnet; Stephanie Chan 003; Ankesh Anand; Zaheer Abbas; Azade Nova; John D. Co-Reyes; Eric Chu; Feryal M. P. Behbahani; Aleksandra Faust; et al. (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 001; Angeliki Lazaridou; Orhan Firat; Noah Fiedel (CoRR, 2024)

NATURAL PLAN: Benchmarking LLMs on Natural Language Planning. - Huaixiu Steven Zheng; Swaroop Mishra; Hugh Zhang; Xinyun Chen; Minmin Chen; Azade Nova; Le Hou; Heng-Tze Cheng; Quoc V. Le; Ed H. Chi; Denny Zhou (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)