Dravyansh Sharma
I am a PhD student in the Computer Science Department at the Carnegie Mellon University advised by Nina Balcan. I am interested in designing algorithms for machine learning with strong and provable performance guarantees. Previously I have worked with the Speech team at Google and completed my undergraduate studies at IIT Delhi.
Publications
- No Internal Regret with Non-convex Loss Functions, AAAI 2024 (to appear)
- Accelerating data-driven algorithm design using output-sensitive techniques, AAAI 2024 Workshop on Learnable Optimization (to appear) with Maria-Florina Balcan and Christopher Seiler
- Shifting regret for tuning combinatorial algorithms with applications to clustering, AAAI 2024 Workshop on Learnable Optimization (to appear) 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 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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