2024
A Little Help Goes a Long Way: Efficient LLM Training by Leveraging Small LMs.
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CoRR, 2024
No more hard prompts: SoftSRV prompting for synthetic data generation.
CoRR, 2024
SpacTor-T5: Pre-training T5 Models with Span Corruption and Replaced Token Detection.
CoRR, 2024
DistillSpec: Improving Speculative Decoding via Knowledge Distillation.
Proceedings of the Twelfth International Conference on Learning Representations, 2024
2023
Leveraging Importance Weights in Subset Selection.
Proceedings of the Eleventh International Conference on Learning Representations, 2023
2022
Is margin all you need? An extensive empirical study of active learning on tabular data.
CoRR, 2022
Vexation-Aware Active Learning for On-Menu Restaurant Dish Availability.
Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022
Churn Reduction via Distillation.
Proceedings of the Tenth International Conference on Learning Representations, 2022
2021
Batch Active Learning at Scale.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021
Learning with Labeling Induced Abstentions.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021
Proceedings of the 38th International Conference on Machine Learning, 2021
Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms.
Proceedings of the 9th International Conference on Learning Representations, 2021
2020
An Analysis of SVD for Deep Rotation Estimation.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020
A System for Massively Parallel Hyperparameter Tuning.
Proceedings of the Third Conference on Machine Learning and Systems, 2020
Understanding the Effects of Batching in Online Active Learning.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020
2019
Combining MixMatch and Active Learning for Better Accuracy with Fewer Labels.
CoRR, 2019
The Practical Challenges of Active Learning: Lessons Learned from Live Experimentation.
CoRR, 2019
SysML: The New Frontier of Machine Learning Systems.
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CoRR, 2019
Learning a Compressed Sensing Measurement Matrix via Gradient Unrolling.
Proceedings of the 36th International Conference on Machine Learning, 2019
Categorical Feature Compression via Submodular Optimization.
Proceedings of the 36th International Conference on Machine Learning, 2019
2018
Massively Parallel Hyperparameter Tuning.
CoRR, 2018
The Sparse Recovery Autoencoder.
CoRR, 2018
2017
Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization.
J. Mach. Learn. Res., 2017
Hyperband: Bandit-Based Configuration Evaluation for Hyperparameter Optimization.
Proceedings of the 5th International Conference on Learning Representations, 2017
2016
Efficient Hyperparameter Optimization and Infinitely Many Armed Bandits.
CoRR, 2016
Where to Sell: Simulating Auctions From Learning Algorithms.
Proceedings of the 2016 ACM Conference on Economics and Computation, 2016
Greedy Column Subset Selection: New Bounds and Distributed Algorithms.
Proceedings of the 33nd International Conference on Machine Learning, 2016
2015
Foundations of Coupled Nonlinear Dimensionality Reduction.
CoRR, 2015
An $\tilde{O}(\frac{1}{\sqrt{T}})$-error online algorithm for retrieving heavily perturbated statistical databases in the low-dimensional querying mode.
CoRR, 2015
A Survey of Modern Questions and Challenges in Feature Extraction.
Proceedings of the 1st Workshop on Feature Extraction: Modern Questions and Challenges, 2015
Generalization Bounds for Supervised Dimensionality Reduction.
Proceedings of the 1st Workshop on Feature Extraction: Modern Questions and Challenges, 2015
An Optimal Online Algorithm For Retrieving Heavily Perturbed Statistical Databases In The Low-Dimensional Querying Model.
Proceedings of the 24th ACM International Conference on Information and Knowledge Management, 2015
Applying WebTables in Practice.
Proceedings of the Seventh Biennial Conference on Innovative Data Systems Research, 2015
Scalable and Interpretable Data Representation for High-Dimensional, Complex Data.
Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015
2014
Repeated Contextual Auctions with Strategic Buyers.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014
Corporate learning at scale: lessons from a large online course at google.
Proceedings of the First (2014) ACM Conference on Learning @ Scale, 2014
2013
Perceptron Mistake Bounds
CoRR, 2013
Learning Prices for Repeated Auctions with Strategic Buyers.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013
Multi-Class Classification with Maximum Margin Multiple Kernel.
Proceedings of the 30th International Conference on Machine Learning, 2013
2012
Algorithms for Learning Kernels Based on Centered Alignment.
J. Mach. Learn. Res., 2012
Foundations of Machine Learning.
Adaptive computation and machine learning, MIT Press, ISBN: 978-0-262-01825-8, 2012
2011
Online and Batch Learning Algorithms for Data with Missing Features
CoRR, 2011
Learning with Missing Features.
Proceedings of the UAI 2011, 2011
Ensembles of Kernel Predictors.
Proceedings of the UAI 2011, 2011
2010
Theoretical Foundations and Algorithms for Learning with Multiple Kernels.
PhD thesis, 2010
Stability Bounds for Stationary phi-mixing and beta-mixing Processes.
J. Mach. Learn. Res., 2010
Matrix Coherence and the Nystrom Method.
Proceedings of the UAI 2010, 2010
Generalization Bounds for Learning Kernels.
Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010
Two-Stage Learning Kernel Algorithms.
Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010
2009
New Generalization Bounds for Learning Kernels
CoRR, 2009
Multiple Source Adaptation and the Rényi Divergence.
Proceedings of the UAI 2009, 2009
L2 Regularization for Learning Kernels.
Proceedings of the UAI 2009, 2009
Learning Non-Linear Combinations of Kernels.
Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009
Domain Adaptation: Learning Bounds and Algorithms.
Proceedings of the COLT 2009, 2009
2008
Stability Bound for Stationary Phi-mixing and Beta-mixing Processes
CoRR, 2008
Rademacher Complexity Bounds for Non-I.I.D. Processes.
Proceedings of the Advances in Neural Information Processing Systems 21, 2008
Domain Adaptation with Multiple Sources.
Proceedings of the Advances in Neural Information Processing Systems 21, 2008
Sample Selection Bias Correction Theory.
Proceedings of the Algorithmic Learning Theory, 19th International Conference, 2008
2007
Stability Bounds for Non-i.i.d. Processes.
Proceedings of the Advances in Neural Information Processing Systems 20, 2007
Balancing traffic load in wireless networks with curveball routing.
Proceedings of the 8th ACM Interational Symposium on Mobile Ad Hoc Networking and Computing, 2007