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Found 20 papers, 4 papers with code
Analyzing Cost-Sensitive Surrogate Losses via $\mathcal{H}$-calibration
This paper aims to understand whether machine learning models should be trained using cost-sensitive surrogates or cost-agnostic ones (e. g., cross-entropy).
Finite-Horizon Single-Pull Restless Bandits: An Efficient Index Policy For Scarce Resource Allocation
However, in many practical settings with highly scarce resources, where each agent can only receive at most one resource, such as healthcare intervention programs, the standard RMAB framework falls short.
Efficient Public Health Intervention Planning Using Decomposition-Based Decision-Focused Learning
However, the availability and time of these health workers are limited resources.
Evaluating the Effectiveness of Index-Based Treatment Allocation
When resources are scarce, an allocation policy is needed to decide who receives a resource.
Leaving the Nest: Going Beyond Local Loss Functions for Predict-Then-Optimize
In this paper, we propose solutions to these issues, avoiding the aforementioned assumptions and utilizing the ML model's features to increase the sample efficiency of learning loss functions.
Decision-Focused Learning without Differentiable Optimization: Learning Locally Optimized Decision Losses
Decision-Focused Learning (DFL) is a paradigm for tailoring a predictive model to a downstream optimization task that uses its predictions in order to perform better on that specific task.
Scalable Decision-Focused Learning in Restless Multi-Armed Bandits with Application to Maternal and Child Health
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.
Data-Driven Methods for Balancing Fairness and Efficiency in Ride-Pooling
Rideshare and ride-pooling platforms use artificial intelligence-based matching algorithms to pair riders and drivers.
Q-Learning Lagrange Policies for Multi-Action Restless Bandits
Multi-action restless multi-armed bandits (RMABs) are a powerful framework for constrained resource allocation in which $N$ independent processes are managed.
Learning MDPs from Features: Predict-Then-Optimize for Sequential Decision Problems by Reinforcement Learning
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.
Learning MDPs from Features: Predict-Then-Optimize for Sequential Decision Making by Reinforcement Learning
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.
Multilingual and code-switching ASR challenges for low resource Indian languages
For this purpose, we provide a total of ~600 hours of transcribed speech data, comprising train and test sets, in these languages including two code-switched language pairs, Hindi-English and Bengali-English.
Cross-lingual and Multilingual Spoken Term Detection for Low-Resource Indian Languages
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
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
Automatic Speech Recognition (ASR)
+1
Learning to Recognize Code-switched Speech Without Forgetting Monolingual Speech Recognition
In this work, we show that fine-tuning ASR models on code-switched speech harms performance on monolingual speech.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Unsung Challenges of Building and Deploying Language Technologies for Low Resource Language Communities
In this paper, we examine and analyze the challenges associated with developing and introducing language technologies to low-resource language communities.
Solving Online Threat Screening Games using Constrained Action Space Reinforcement Learning
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).
Neural Approximate Dynamic Programming for On-Demand Ride-Pooling
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
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
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.
