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.

Research Résumé Reach me

Publications

Offline-to-online hyperparameter transfer for stochastic bandits, AAAI 2025
with Arun Sai Suggala
An Analysis of Robustness of Non-Lipschitz Networks, NeurIPS 2024 (Journal-to-conference track)
with Maria-Florina Balcan, Avrim Blum and Hongyang Zhang
Accelerating ERM for data-driven algorithm design using output-sensitive techniques, NeurIPS 2024
with Maria-Florina Balcan and Christopher Seiler
Learning Accurate and Interpretable Decision Trees, UAI 2024 (Outstanding student paper award)
with Maria-Florina Balcan
No Internal Regret with Non-convex Loss Functions, AAAI 2024
Accelerating data-driven algorithm design using output-sensitive techniques, AAAI 2024
Workshop on Learnable Optimization
with Maria-Florina Balcan and Christopher Seiler
Shifting regret for tuning combinatorial algorithms with applications to clustering, AAAI 2024
Workshop on Learnable Optimization
with Maria-Florina Balcan and Travis Dick
New Bounds for Hyperparameter Tuning of Regression Problems Across Instances, NeurIPS 2023
with Maria-Florina Balcan and Anh Tuan Nguyen
Reliable Learning for Test-time Attacks and Distribution Shift, NeurIPS 2023
with Maria-Florina Balcan, Steve Hanneke and Rattana Pukdee
Efficiently Learning the Graph for Semi-supervised Learning, UAI 2023
with Maxwell Jones
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
Provably tuning the ElasticNet across instances, NeurIPS 2022 [blog post]
with Maria-Florina Balcan, Mikhail Khodak and Ameet Talwalkar
Robustly-reliable learners under poisoning attacks, COLT 2022
with Maria-Florina Balcan, Avrim Blum and Steve Hanneke
Faster algorithms for learning to link, align sequences, and price two-part tariffs, Pre-print
with Maria-Florina Balcan and Christopher Seiler
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
Data driven semi-supervised learning, NeurIPS 2021 (Oral, <1%)
with Maria-Florina Balcan
Learning-to-learn non-convex piecewise-Lipschitz functions, NeurIPS 2021
with Maria-Florina Balcan, Mikhail Khodak, Ameet Talwalkar
Learning Piecewise Lipschitz Functions in Changing Environments, AISTATS 2020 [slides]
with Maria-Florina Balcan and Travis Dick
Better morphology prediction for better speech systems, Interspeech 2019
with Melissa Wilson and Antoine Bruguier
On Training and Evaluation of Grapheme-to-Phoneme Mappings with Limited Data, Interspeech 2018
Dictionary Augmented Sequence-to-Sequence Neural Network for Grapheme to Phoneme Prediction, Interspeech 2018
with Antoine Bruguier and Anton Bakhtin
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
On greedy maximization of entropy, ICML 2015
with Amit Deshpande and Ashish Kapoor

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