8 citations · 16 across the 14 of their papers we have counts for
16 papers
Distribution-dependent Generalization Bounds for Tuning Linear Regression Across Tasks
Maria-Florina Balcan, Saumya Goyal, Dravyansh Sharma
Modern regression problems often involve high-dimensional data and a careful tuning of the regularization hyperparameters is crucial to avoid overly complex models that may overfit…
On Learning Verifiers and Implications to Chain-of-Thought Reasoning
Maria-Florina Balcan, Avrim Blum, Zhiyuan Li +1
Chain-of-Thought reasoning has emerged as a powerful approach for solving complex mathematical and logical problems. However, it can often veer off track through incorrect or unsub…
Offline-to-online hyperparameter transfer for stochastic bandits
Dravyansh Sharma, Arun Sai Suggala
Classic algorithms for stochastic bandits typically use hyperparameters that govern their critical properties such as the trade-off between exploration and exploitation. Tuning the…
Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function
Maria-Florina Balcan, Anh Tuan Nguyen, Dravyansh Sharma
Modern machine learning algorithms, especially deep learning based techniques, typically involve careful hyperparameter tuning to achieve the best performance. Despite the surge of…
Subsidy design for better social outcomes
Maria-Florina Balcan, Matteo Pozzi, Dravyansh Sharma
Overcoming the impact of selfish behavior of rational players in multiagent systems is a fundamental problem in game theory. Without any intervention from a central agent, strategi…
Algorithm Configuration for Structured Pfaffian Settings
Maria-Florina Balcan, Anh Tuan Nguyen, Dravyansh Sharma
Data-driven algorithm design automatically adapts algorithms to specific application domains, achieving better performance. In the context of parameterized algorithms, this approac…
Learning accurate and interpretable tree-based models
Maria-Florina Balcan, Dravyansh Sharma
Decision trees and their ensembles are popular in machine learning as easy-to-understand models. Several techniques have been proposed in the literature for learning tree-based cla…
Efficiently Learning the Graph for Semi-supervised Learning
Dravyansh Sharma, Maxwell Jones
Computational efficiency is a major bottleneck in using classic graph-based approaches for semi-supervised learning on datasets with a large number of unlabeled examples. Known tec…
Reliable learning in challenging environments
Maria-Florina Balcan, Steve Hanneke, Rattana Pukdee +1
The problem of designing learners that provide guarantees that their predictions are provably correct is of increasing importance in machine learning. However, learning theoretic g…
Provably tuning the ElasticNet across instances
Maria-Florina Balcan, Mikhail Khodak, Dravyansh Sharma +1
An important unresolved challenge in the theory of regularization is to set the regularization coefficients of popular techniques like the ElasticNet with general provable guarante…
Accelerating ERM for data-driven algorithm design using output-sensitive techniques
Maria-Florina Balcan, Christopher Seiler, Dravyansh Sharma
Data-driven algorithm design is a promising, learning-based approach for beyond worst-case analysis of algorithms with tunable parameters. An important open problem is the design o…
Robustly-reliable learners under poisoning attacks
Maria-Florina Balcan, Avrim Blum, Steve Hanneke +1
Data poisoning attacks, in which an adversary corrupts a training set with the goal of inducing specific desired mistakes, have raised substantial concern: even just the possibilit…
Learning-to-learn non-convex piecewise-Lipschitz functions
Maria-Florina Balcan, Mikhail Khodak, Dravyansh Sharma +1
We analyze the meta-learning of the initialization and step-size of learning algorithms for piecewise-Lipschitz functions, a non-convex setting with applications to both machine le…
Data driven semi-supervised learning
Maria-Florina Balcan, Dravyansh Sharma
We consider a novel data driven approach for designing learning algorithms that can effectively learn with only a small number of labeled examples. This is crucial for modern machi…
An Analysis of Robustness of Non-Lipschitz Networks
Maria-Florina Balcan, Avrim Blum, Dravyansh Sharma +1
Despite significant advances, deep networks remain highly susceptible to adversarial attack. One fundamental challenge is that small input perturbations can often produce large mov…
Learning piecewise Lipschitz functions in changing environments
Maria-Florina Balcan, Travis Dick, Dravyansh Sharma
Optimization in the presence of sharp (non-Lipschitz), unpredictable (w.r.t. time and amount) changes is a challenging and largely unexplored problem of great significance. We cons…