Search Results for author: Piyush Gupta

Found 10 papers, 4 papers with code

SARC: Soft Actor Retrospective Critic

1 code implementation28 Jun 2023 Sukriti Verma, Ayush Chopra, Jayakumar Subramanian, Mausoom Sarkar, Nikaash Puri, Piyush Gupta, Balaji Krishnamurthy

The two-time scale nature of SAC, which is an actor-critic algorithm, is characterised by the fact that the critic estimate has not converged for the actor at any given time, but since the critic learns faster than the actor, it ensures eventual consistency between the two.

Deterministic Sequencing of Exploration and Exploitation for Reinforcement Learning

no code implementations12 Sep 2022 Piyush Gupta, Vaibhav Srivastava

During exploration, DSEE explores the environment and updates the estimates for expected reward and transition probabilities.

Efficient Exploration reinforcement-learning +1

Information-theoretic Evolution of Model Agnostic Global Explanations

no code implementations14 May 2021 Sukriti Verma, Nikaash Puri, Piyush Gupta, Balaji Krishnamurthy

Our approach builds on top of existing local model explanation methods to extract conditions important for explaining model behavior for specific instances followed by an evolutionary algorithm that optimizes an information theory based fitness function to construct rules that explain global model behavior.

Marketing

MixBoost: Synthetic Oversampling with Boosted Mixup for Handling Extreme Imbalance

no code implementations3 Sep 2020 Anubha Kabra, Ayush Chopra, Nikaash Puri, Pinkesh Badjatiya, Sukriti Verma, Piyush Gupta, Balaji K

Training a classification model on a dataset where the instances of one class outnumber those of the other class is a challenging problem.

Data Augmentation Fraud Detection +1

Retrospective Loss: Looking Back to Improve Training of Deep Neural Networks

no code implementations24 Jun 2020 Surgan Jandial, Ayush Chopra, Mausoom Sarkar, Piyush Gupta, Balaji Krishnamurthy, Vineeth Balasubramanian

Deep neural networks (DNNs) are powerful learning machines that have enabled breakthroughs in several domains.

Explain Your Move: Understanding Agent Actions Using Focused Feature Saliency

1 code implementation ICLR 2020 Piyush Gupta, Nikaash Puri, Sukriti Verma, Dhruv Kayastha, Shripad Deshmukh, Balaji Krishnamurthy, Sameer Singh

We show through illustrative examples (Chess, Atari, Go), human studies (Chess), and automated evaluation methods (Chess) that our approach generates saliency maps that are more interpretable for humans than existing approaches.

Atari Games Board Games +2

Towards Safer Self-Driving Through Great PAIN (Physically Adversarial Intelligent Networks)

1 code implementation24 Mar 2020 Piyush Gupta, Demetris Coleman, Joshua E. Siegel

Automated vehicles' neural networks suffer from overfit, poor generalizability, and untrained edge cases due to limited data availability.

ShapeVis: High-dimensional Data Visualization at Scale

no code implementations15 Jan 2020 Nupur Kumari, Siddarth R., Akash Rupela, Piyush Gupta, Balaji Krishnamurthy

This graph captures the structural characteristics of the point cloud, and its weights are determined using a Finite Markov Chain.

Community Detection Data Visualization +3

Explain Your Move: Understanding Agent Actions Using Specific and Relevant Feature Attribution

2 code implementations23 Dec 2019 Nikaash Puri, Sukriti Verma, Piyush Gupta, Dhruv Kayastha, Shripad Deshmukh, Balaji Krishnamurthy, Sameer Singh

We show through illustrative examples (Chess, Atari, Go), human studies (Chess), and automated evaluation methods (Chess) that SARFA generates saliency maps that are more interpretable for humans than existing approaches.

Atari Games Board Games +2

MAGIX: Model Agnostic Globally Interpretable Explanations

no code implementations22 Jun 2017 Nikaash Puri, Piyush Gupta, Pratiksha Agarwal, Sukriti Verma, Balaji Krishnamurthy

Explaining the behavior of a black box machine learning model at the instance level is useful for building trust.

BIG-bench Machine Learning Marketing

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