Search Results for author: Sanket Shah

Found 15 papers, 4 papers with code

Learning (Local) Surrogate Loss Functions for Predict-Then-Optimize Problems

no code implementations30 Mar 2022 Sanket Shah, Bryan Wilder, Andrew Perrault, Milind Tambe

Decision-Focused Learning (DFL) is a paradigm for tailoring a predictive model to a downstream optimisation task that uses its predictions, so that it can perform better on that specific task.

Decision-Focused Learning in Restless Multi-Armed Bandits with Application to Maternal and Child Care Domain

no code implementations2 Feb 2022 Kai Wang, Shresth Verma, Aditya Mate, Sanket Shah, Aparna Taneja, Neha Madhiwalla, Aparna Hegde, Milind Tambe

To address this shortcoming we propose a novel approach for decision-focused learning in RMAB that directly trains the predictive model to maximize the Whittle index solution quality.

Multi-Armed Bandits

Data-Driven Methods for Balancing Fairness and Efficiency in Ride-Pooling

1 code implementation7 Oct 2021 Naveen Raman, Sanket Shah, John Dickerson

Rideshare and ride-pooling platforms use artificial intelligence-based matching algorithms to pair riders and drivers.

Fairness

Q-Learning Lagrange Policies for Multi-Action Restless Bandits

1 code implementation22 Jun 2021 Jackson A. Killian, Arpita Biswas, Sanket Shah, Milind Tambe

Multi-action restless multi-armed bandits (RMABs) are a powerful framework for constrained resource allocation in which $N$ independent processes are managed.

Multi-Armed Bandits Q-Learning

Learning MDPs from Features: Predict-Then-Optimize for Sequential Decision Problems by Reinforcement Learning

no code implementations NeurIPS 2021 Kai Wang, Sanket Shah, Haipeng Chen, Andrew Perrault, Finale Doshi-Velez, Milind Tambe

In the predict-then-optimize framework, the objective is to train a predictive model, mapping from environment features to parameters of an optimization problem, which maximizes decision quality when the optimization is subsequently solved.

reinforcement-learning

Learning MDPs from Features: Predict-Then-Optimize for Sequential Decision Making by Reinforcement Learning

no code implementations NeurIPS 2021 Kai Wang, Sanket Shah, Haipeng Chen, Andrew Perrault, Finale Doshi-Velez, Milind Tambe

In the predict-then-optimize framework, the objective is to train a predictive model, mapping from environment features to parameters of an optimization problem, which maximizes decision quality when the optimization is subsequently solved.

Decision Making reinforcement-learning

Cross-lingual and Multilingual Spoken Term Detection for Low-Resource Indian Languages

no code implementations12 Nov 2020 Sanket Shah, Satarupa Guha, Simran Khanuja, Sunayana Sitaram

Since no publicly available dataset exists for Spoken Term Detection in these languages, we create a new dataset using a publicly available TTS dataset.

Learning not to Discriminate: Task Agnostic Learning for Improving Monolingual and Code-switched Speech Recognition

no code implementations9 Jun 2020 Gurunath Reddy Madhumani, Sanket Shah, Basil Abraham, Vikas Joshi, Sunayana Sitaram

Recently, we showed that monolingual ASR systems fine-tuned on code-switched data deteriorate in performance on monolingual speech recognition, which is not desirable as ASR systems deployed in multilingual scenarios should recognize both monolingual and code-switched speech with high accuracy.

Automatic Speech Recognition

Solving Online Threat Screening Games using Constrained Action Space Reinforcement Learning

no code implementations20 Nov 2019 Sanket Shah, Arunesh Sinha, Pradeep Varakantham, Andrew Perrault, Milind Tambe

To solve the online problem with a hard bound on risk, we formulate it as a Reinforcement Learning (RL) problem with constraints on the action space (hard bound on risk).

reinforcement-learning

Neural Approximate Dynamic Programming for On-Demand Ride-Pooling

1 code implementation20 Nov 2019 Sanket Shah, Meghna Lowalekar, Pradeep Varakantham

This is because even a myopic assignment in ride-pooling involves considering what combinations of passenger requests that can be assigned to vehicles, which adds a layer of combinatorial complexity to the ToD problem.

CoSSAT: Code-Switched Speech Annotation Tool

no code implementations WS 2019 Sanket Shah, Pratik Joshi, Sebastin Santy, Sunayana Sitaram

Code-switching refers to the alternation of two or more languages in a conversation or utterance and is common in multilingual communities across the world.

KVQA: Knowledge-Aware Visual Question Answering

no code implementations AAAI Conference on Artificial Intelligence 2019 Sanket Shah, Hyderabad Anand Mishra, Naganand Yadati, Partha Pratim Talukdar

In spite of this progress, the important problem of answering questions requiring world knowledge about named entities (e. g., Barack Obama, White House, United Nations) in the image has not been addressed in prior research.

Knowledge Graphs Question Answering +2

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