Dravyansh Sharma

I am currently an IDEAL Postdoc in Chicago. I completed my PhD in the Computer Science Department at the Carnegie Mellon University, and was fortunate to be advised by Nina Balcan. I am interested in designing algorithms for machine learning with strong and provable performance guarantees.

Publications

2026
[C25] Gradient Descent with Provably Tuned Learning-rate Schedules, AISTATS 2026
2025
[C24] Learning with improvements in challenging and natural settings, NeurIPS 2025
with Alec Sun
[C23] On Learning Verifiers for Chain-of-Thought Reasoning, NeurIPS 2025
with Maria-Florina Balcan, Avrim Blum and Zhiyuan Li
[C22] Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function, NeurIPS 2025
with Maria-Florina Balcan and Anh Nguyen
[C21] Algorithm Configuration for Structured Pfaffian Settings, AutoML 2025 (Hot-off-the-press track, also accepted to journal TMLR 2025)
with Maria-Florina Balcan and Anh Tuan Nguyen
[C20] PAC Learning with Improvements, ICML 2025
with Idan Attias, Avrim Blum, Keziah Naggita, Donya Saless and Matthew Walter
[C19] Tuning Algorithmic and Architectural Hyperparameters in Graph-Based Semi-Supervised Learning with Provable Guarantees, UAI 2025
with Ally Yalei Du and Eric Huang
[C18] Offline-to-online hyperparameter transfer for stochastic bandits, AAAI 2025
with Arun Sai Suggala
[J3] Algorithm Configuration for Structured Pfaffian Settings, TMLR 2025
with Maria-Florina Balcan and Anh Tuan Nguyen
[A1] Learning Accurate and Interpretable Decision Trees (Extended Abstract), IJCAI 2025 (Best Papers from Sister Conferences Track)
with Maria-Florina Balcan
[A2] An Analysis of Robustness of Non-Lipschitz Networks (Extended Abstract), Workshop on Safe AI (UAI 2025)
with Maria-Florina Balcan, Avrim Blum and Hongyang Zhang
[W13] Distribution-dependent Generalization Bounds for Tuning Linear Regression Across Tasks., DiffCoALG: Differentiable Learning of Combinatorial Algorithms (NeurIPS 2025).
with Maria-Florina Balcan and Saumya Goyal
[W12] Algorithm design and sharper bounds for improving-bandits., OPT2025: 17th Annual Workshop on Optimization for Machine Learning (NeurIPS 2025).
with Avrim Blum, Marten Garicano and Kavya Ravichandran
[W11] Learning reliably under adversarial attacks, distribution shifts and strategic agents, Reliable ML from Unreliable Data (NeurIPS 2025).
with Maria-Florina Balcan
[W10] On Learning Verifiers for Chain-of-Thought Reasoning, Workshop on Reliable and Responsible Foundation Models (ICML 2025)
with Maria-Florina Balcan, Avrim Blum and Zhiyuan Li
[W9] PAC Learning with Improvements, 2nd Workshop on Social Choice and Learning Algorithms (IJCAI 2025)
with Idan Attias, Avrim Blum, Keziah Naggita, Donya Saless and Matthew Walter
[W8] Learning how to step in gradient-based optimization: beyond convexity and smoothness,
3rd Workshop on High-dimensional Learning Dynamics (ICML 2025)
[W7] Gradient descent in presence of extreme flatness and steepness,
Methods and Opportunities at Small Scale (ICML 2025)
2024
[T1] CMU CSD PhD Thesis Data-driven algorithm design and principled hyperparameter tuning in machine learning
[C17] An Analysis of Robustness of Non-Lipschitz Networks, NeurIPS 2024 (Journal-to-conference track)
with Maria-Florina Balcan, Avrim Blum and Hongyang Zhang
[C16] Accelerating ERM for data-driven algorithm design using output-sensitive techniques, NeurIPS 2024
with Maria-Florina Balcan and Christopher Seiler
[C15] Subsidy for repair in component maintenance games, EMI/PMC 2024
with Maria-Florina Balcan and Matteo Pozzi
[C14] Learning Accurate and Interpretable Decision Trees, UAI 2024 (Outstanding student paper award)
with Maria-Florina Balcan
[C13] No Internal Regret with Non-convex Loss Functions, AAAI 2024
[W6] Theoretical Analyses of Hyperparameter Selection in Graph-Based Semi-Supervised Learning, ICML 2024
Workshop on Geometry-grounded Representation Learning and Generative Modeling
with Ally Yalei Du and Eric Huang
[W5] Accelerating data-driven algorithm design using output-sensitive techniques, AAAI 2024
Workshop on Learnable Optimization
with Maria-Florina Balcan and Christopher Seiler
[W4] Shifting regret for tuning combinatorial algorithms with applications to clustering, AAAI 2024
Workshop on Learnable Optimization
with Maria-Florina Balcan and Travis Dick
2023
[C12] New Bounds for Hyperparameter Tuning of Regression Problems Across Instances, NeurIPS 2023
with Maria-Florina Balcan and Anh Tuan Nguyen
[C11] Reliable Learning for Test-time Attacks and Distribution Shift, NeurIPS 2023
with Maria-Florina Balcan, Steve Hanneke and Rattana Pukdee
[C10] Efficiently Learning the Graph for Semi-supervised Learning, UAI 2023
with Maxwell Jones
[J2] An analysis of robustness of non-Lipschitz networks, JMLR 2023 (earlier version in ICLR 2022
SRML workshop)
with Maria-Florina Balcan, Avrim Blum and Hongyang Zhang
2022
[C9] Provably tuning the ElasticNet across instances, NeurIPS 2022 [blog post]
with Maria-Florina Balcan, Mikhail Khodak and Ameet Talwalkar
[C8] Robustly-reliable learners under poisoning attacks, COLT 2022
with Maria-Florina Balcan, Avrim Blum and Steve Hanneke
[W3] On the Power of Abstention and Data-Driven Decision Making for Adversarial Robustness, ICLR 2022
Workshop on Socially Responsible Machine Learning (Oral)
with Maria-Florina Balcan, Avrim Blum and Hongyang Zhang
2021
[C7] Data driven semi-supervised learning, NeurIPS 2021 (Oral, <1%)
with Maria-Florina Balcan
[C6] Learning-to-learn non-convex piecewise-Lipschitz functions, NeurIPS 2021
with Maria-Florina Balcan, Mikhail Khodak, Ameet Talwalkar
[W2] Improved pronunciation prediction accuracy using morphology, ACL SIG on Computational Morphology and Phonology (ACL 2021)
with Saumya Sahai, Neha Chaudhari, Antoine Bruguier.
[W1] Predicting and Explaining French Grammatical Gender, ACL Special Interest Group (SIG) on Typology (NAACL 2021)
with Saumya Sahai
2020 and before
[C5] Learning Piecewise Lipschitz Functions in Changing Environments, AISTATS 2020 [slides]
with Maria-Florina Balcan and Travis Dick
[C4] Better morphology prediction for better speech systems, Interspeech 2019
with Melissa Wilson and Antoine Bruguier
[C3] On Training and Evaluation of Grapheme-to-Phoneme Mappings with Limited Data, Interspeech 2018
[C2] Dictionary Augmented Sequence-to-Sequence Neural Network for Grapheme to Phoneme Prediction, Interspeech 2018
with Antoine Bruguier and Anton Bakhtin
[J1] Some results on a class of mixed van der Waerden numbers, Rocky Mountain J. Math. 2018
with Kaushik Maran, Sai Praneeth Reddy and Amitabha Tripathi
[C1] On greedy maximization of entropy, ICML 2015
with Amit Deshpande and Ashish Kapoor

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