Search Results for author: Harit Vishwakarma

Found 9 papers, 6 papers with code

Lifting Weak Supervision To Structured Prediction

1 code implementation24 Nov 2022 Harit Vishwakarma, Nicholas Roberts, Frederic Sala

Weak supervision (WS) is a rich set of techniques that produce pseudolabels by aggregating easily obtained but potentially noisy label estimates from a variety of sources.

Binary Classification Structured Prediction

Promises and Pitfalls of Threshold-based Auto-labeling

2 code implementations NeurIPS 2023 Harit Vishwakarma, Heguang Lin, Frederic Sala, Ramya Korlakai Vinayak

Given the long shelf-life and diverse usage of the resulting datasets, understanding when the data obtained by such auto-labeling systems can be relied on is crucial.

Universalizing Weak Supervision

no code implementations ICLR 2022 Changho Shin, Winfred Li, Harit Vishwakarma, Nicholas Roberts, Frederic Sala

We apply this technique to important problems previously not tackled by WS frameworks including learning to rank, regression, and learning in hyperbolic space.

Computational Efficiency Learning-To-Rank +1

Optimal Lottery Tickets via Subset Sum: Logarithmic Over-Parameterization is Sufficient

1 code implementation NeurIPS 2020 Ankit Pensia, Shashank Rajput, Alliot Nagle, Harit Vishwakarma, Dimitris Papailiopoulos

We show that any target network of width $d$ and depth $l$ can be approximated by pruning a random network that is a factor $O(log(dl))$ wider and twice as deep.

Optimal Lottery Tickets via SubsetSum: Logarithmic Over-Parameterization is Sufficient

1 code implementation14 Jun 2020 Ankit Pensia, Shashank Rajput, Alliot Nagle, Harit Vishwakarma, Dimitris Papailiopoulos

We show that any target network of width $d$ and depth $l$ can be approximated by pruning a random network that is a factor $O(\log(dl))$ wider and twice as deep.

Quantum Embedding of Knowledge for Reasoning

1 code implementation NeurIPS 2019 Dinesh Garg, Shajith Ikbal Mohamed, Santosh K. Srivastava, Harit Vishwakarma, Hima Karanam, L. Venkata Subramaniam

Statistical Relational Learning (SRL) methods are the most widely used techniques to generate distributional representations of the symbolic Knowledge Bases (KBs).

Logical Reasoning Relational Reasoning

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