Harish Karnick

AAAI Conference 2020 Conference Paper

Deep Attentive Ranking Networks for Learning to Order Sentences

• Pawan Kumar
• Dhanajit Brahma
• Harish Karnick
• Piyush Rai

We present an attention-based ranking framework for learning to order sentences given a paragraph. Our framework is built on a bidirectional sentence encoder and a self-attention based transformer network to obtain an input order invariant representation of paragraphs. Moreover, it allows seamless training using a variety of ranking based loss functions, such as pointwise, pairwise, and listwise ranking. We apply our framework on two tasks: Sentence Ordering and Order Discrimination. Our framework outperforms various state-ofthe-art methods on these tasks on a variety of evaluation metrics. We also show that it achieves better results when using pairwise and listwise ranking losses, rather than the pointwise ranking loss, which suggests that incorporating relative positions of two or more sentences in the loss function contributes to better learning.

AAAI Conference 2019 Conference Paper

Distributional Semantics Meets Multi-Label Learning

• Vivek Gupta
• Rahul Wadbude
• Nagarajan Natarajan
• Harish Karnick
• Prateek Jain
• Piyush Rai

We present a label embedding based approach to large-scale multi-label learning, drawing inspiration from ideas rooted in distributional semantics, specifically the Skip Gram Negative Sampling (SGNS) approach, widely used to learn word embeddings. Besides leading to a highly scalable model for multi-label learning, our approach highlights interesting connections between label embedding methods commonly used for multi-label learning and paragraph embedding methods commonly used for learning representations of text data. The framework easily extends to incorporating auxiliary information such as label-label correlations; this is crucial especially when many training instances are only partially annotated. To facilitate end-to-end learning, we develop a joint learning algorithm that can learn the embeddings as well as a regression model that predicts these embeddings for the new input to be annotated, via efficient gradient based methods. We demonstrate the effectiveness of our approach through an extensive set of experiments on a variety of benchmark datasets, and show that the proposed models perform favorably as compared to state-of-the-art methods for large-scale multi-label learning.

ICML Conference 2013 Conference Paper

On the Generalization Ability of Online Learning Algorithms for Pairwise Loss Functions

• Purushottam Kar
• Bharath K. Sriperumbudur
• Prateek Jain 0002
• Harish Karnick

In this paper, we study the generalization properties of online learning based stochastic methods for supervised learning problems where the loss function is dependent on more than one training sample (e. g. , metric learning, ranking). We present a generic decoupling technique that enables us to provide Rademacher complexity-based generalization error bounds. Our bounds are in general tighter than those obtained by Wang et al. (COLT 2012) for the same problem. Using our decoupling technique, we are further able to obtain fast convergence rates for strongly con-vex pairwise loss functions. We are also able to analyze a class of memory efficient on-line learning algorithms for pairwise learning problems that use only a bounded subset of past training samples to update the hypothesis at each step. Finally, in order to complement our generalization bounds, we propose a novel memory efficient online learning algorithm for higher order learning problems with bounded regret guarantees.

TCS Journal 1991 Journal Article

A resolution rule for well-formed formulae

• K.S.H.S.R. Bhatta
• Harish Karnick