Efficient Regret Minimization in Non-Convex Games. - Elad Hazan; Karan Singh; Cyril Zhang (ICML, 2017)

Learning Linear Dynamical Systems via Spectral Filtering. - Elad Hazan; Karan Singh; Cyril Zhang (NIPS, 2017)

Towards Provable Control for Unknown Linear Dynamical Systems. - Sanjeev Arora; Elad Hazan; Holden Lee; Karan Singh; Cyril Zhang; Yi Zhang 074 (ICLR (Workshop), 2018)

Not-So-Random Features. - Brian Bullins; Cyril Zhang; Yi Zhang (ICLR (Poster), 2018)

Spectral Filtering for General Linear Dynamical Systems. - Elad Hazan; Holden Lee; Karan Singh; Cyril Zhang; Yi Zhang 074 (NeurIPS, 2018)

The Case for Full-Matrix Adaptive Regularization. - Naman Agarwal; Brian Bullins; Xinyi Chen 001; Elad Hazan; Karan Singh; Cyril Zhang; Yi Zhang 074 (CoRR, 2018)

Efficient Full-Matrix Adaptive Regularization. - Naman Agarwal; Brian Bullins; Xinyi Chen 001; Elad Hazan; Karan Singh; Cyril Zhang; Yi Zhang 074 (ICML, 2019)

Robust guarantees for learning an autoregressive filter. - Holden Lee; Cyril Zhang (ALT, 2020)

No-Regret Prediction in Marginally Stable Systems. - Udaya Ghai; Holden Lee; Karan Singh; Cyril Zhang; Yi Zhang (COLT, 2020)

Extreme Tensoring for Low-Memory Preconditioning. - Xinyi Chen 001; Naman Agarwal; Elad Hazan; Cyril Zhang; Yi Zhang 074 (ICLR, 2020)

Calibration, Entropy Rates, and Memory in Language Models. - Mark Braverman; Xinyi Chen; Sham M. Kakade; Karthik Narasimhan; Cyril Zhang; Yi Zhang (ICML, 2020)

Stochastic Optimization with Laggard Data Pipelines. - Naman Agarwal; Rohan Anil; Tomer Koren; Kunal Talwar; Cyril Zhang (NeurIPS, 2020)

Disentangling Adaptive Gradient Methods from Learning Rates. - Naman Agarwal; Rohan Anil; Elad Hazan; Tomer Koren; Cyril Zhang (CoRR, 2020)

Regret-Minimizing Algorithms Beyond Classical Optimization and Control - Cyril Zhang (2020)

Acceleration via Fractal Learning Rate Schedules. - Naman Agarwal; Surbhi Goel; Cyril Zhang (ICML, 2021)

Machine Learning for Mechanical Ventilation Control. - Daniel Suo; Cyril Zhang; Paula Gradu; Udaya Ghai; Xinyi Chen 001; Edgar Minasyan; Naman Agarwal; Karan Singh; Julienne LaChance; Tom Zajdel; Manuel Schottdorf; Daniel J. Cohen; et al. (CoRR, 2021)

Deluca - A Differentiable Control Library: Environments, Methods, and Benchmarking. - Paula Gradu; John Hallman; Daniel Suo; Alex Yu; Naman Agarwal; Udaya Ghai; Karan Singh; Cyril Zhang; Anirudha Majumdar; Elad Hazan (CoRR, 2021)

Machine Learning for Mechanical Ventilation Control (Extended Abstract). - Daniel Suo; Cyril Zhang; Paula Gradu; Udaya Ghai; Xinyi Chen 001; Edgar Minasyan; Naman Agarwal; Karan Singh; Julienne LaChance; Tom Zajdel; Manuel Schottdorf; Daniel J. Cohen; et al. (CoRR, 2021)

Anti-Concentrated Confidence Bonuses For Scalable Exploration. - Jordan T. Ash; Cyril Zhang; Surbhi Goel; Akshay Krishnamurthy; Sham M. Kakade (ICLR, 2022)

Inductive Biases and Variable Creation in Self-Attention Mechanisms. - Benjamin L. Edelman; Surbhi Goel; Sham M. Kakade; Cyril Zhang (ICML, 2022)

Sparsity in Partially Controllable Linear Systems. - Yonathan Efroni; Sham M. Kakade; Akshay Krishnamurthy; Cyril Zhang (ICML, 2022)

Understanding Contrastive Learning Requires Incorporating Inductive Biases. - Nikunj Saunshi; Jordan T. Ash; Surbhi Goel; Dipendra Misra; Cyril Zhang; Sanjeev Arora; Sham M. Kakade; Akshay Krishnamurthy (ICML, 2022)

Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit. - Boaz Barak; Benjamin L. Edelman; Surbhi Goel; Sham M. Kakade; Eran Malach; Cyril Zhang (NeurIPS, 2022)

Recurrent Convolutional Neural Networks Learn Succinct Learning Algorithms. - Surbhi Goel; Sham M. Kakade; Adam Kalai; Cyril Zhang (NeurIPS, 2022)

Neural Active Learning on Heteroskedastic Distributions. - Savya Khosla; Chew Kin Whye; Jordan T. Ash; Cyril Zhang; Kenji Kawaguchi; Alex Lamb (CoRR, 2022)

Learning Hidden Markov Models Using Conditional Samples. - Gaurav Mahajan; Sham M. Kakade; Akshay Krishnamurthy; Cyril Zhang (COLT, 2023)

Understanding and Improving Neural Active Learning on Heteroskedastic Distributions. - Savya Khosla; Chew Kin Whye; Jordan T. Ash; Cyril Zhang; Kenji Kawaguchi; Alex Lamb (ECAI, 2023)

Transformers Learn Shortcuts to Automata. - Bingbin Liu; Jordan T. Ash; Surbhi Goel; Akshay Krishnamurthy; Cyril Zhang (ICLR, 2023)

Pareto Frontiers in Deep Feature Learning: Data, Compute, Width, and Luck. - Benjamin L. Edelman; Surbhi Goel; Sham M. Kakade; Eran Malach; Cyril Zhang (NeurIPS, 2023)

Exposing Attention Glitches with Flip-Flop Language Modeling. - Bingbin Liu; Jordan T. Ash; Surbhi Goel; Akshay Krishnamurthy; Cyril Zhang (NeurIPS, 2023)

Autocalibrating Gaze Tracking: A Demonstration through Gaze Typing. - Akanksha Saran; Jacob Alber; Danielle Bragg; Cyril Zhang; John Langford 001 (CoRR, 2023)

Pareto Frontiers in Neural Feature Learning: Data, Compute, Width, and Luck. - Benjamin L. Edelman; Surbhi Goel; Sham M. Kakade; Eran Malach; Cyril Zhang (CoRR, 2023)

ASL STEM Wiki: Dataset and Benchmark for Interpreting STEM Articles. - Kayo Yin; Chinmay Singh; Fyodor Minakov; Vanessa Milan; Hal Daumé III; Cyril Zhang; Alex Lu 002; Danielle Bragg (EMNLP, 2024)

Butterfly Effects of SGD Noise: Error Amplification in Behavior Cloning and Autoregression. - Adam Block; Dylan J. Foster; Akshay Krishnamurthy; Max Simchowitz; Cyril Zhang (ICLR, 2024)

Can large language models explore in-context? - Akshay Krishnamurthy; Keegan Harris; Dylan J. Foster; Cyril Zhang; Aleksandrs Slivkins (NeurIPS, 2024)

Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone. - Marah I Abdin; Sam Ade Jacobs; Ammar Ahmad Awan; Jyoti Aneja; Ahmed Awadallah 001; Hany Awadalla; Nguyen Bach; Amit Bahree; Arash Bakhtiari; Harkirat S. Behl; Alon Benhaim; Misha Bilenko; et al. (CoRR, 2024)

Phi-4 Technical Report. - Marah I Abdin; Jyoti Aneja; Harkirat S. Behl; Sébastien Bubeck; Ronen Eldan; Suriya Gunasekar; Michael Harrison; Russell J. Hewett; Mojan Javaheripi; Piero Kauffmann; James R. Lee; Yin Tat Lee; et al. (CoRR, 2024)

EyeO: Autocalibrating Gaze Output with Gaze Input for Gaze Typing. - Akanksha Saran; Jacob Alber; Cyril Zhang; Ann Paradiso; Danielle Bragg; John Langford 001 (CHI Extended Abstracts, 2025)

Self-Improvement in Language Models: The Sharpening Mechanism. - Audrey Huang; Adam Block; Dylan J. Foster; Dhruv Rohatgi; Cyril Zhang; Max Simchowitz; Jordan T. Ash; Akshay Krishnamurthy (ICLR, 2025)

On the Query Complexity of Verifier-Assisted Language Generation. - Edoardo Botta; Yuchen Li; Aashay Mehta; Jordan T. Ash; Cyril Zhang; Andrej Risteski (CoRR, 2025)

EMA Without the Lag: Bias-Corrected Iterate Averaging Schemes. - Adam Block; Cyril Zhang (CoRR, 2025)