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Found 10 papers, 4 papers with code
Analyzing the Efficacy of an LLM-Only Approach for Image-based Document Question Answering
Recent document question answering models consist of two key components: the vision encoder, which captures layout and visual elements in images, and a Large Language Model (LLM) that helps contextualize questions to the image and supplements them with external world knowledge to generate accurate answers.
FineDeb: A Debiasing Framework for Language Models
Our results show that FineDeb offers stronger debiasing in comparison to other methods which often result in models as biased as the original language model.
Long Term Fairness for Minority Groups via Performative Distributionally Robust Optimization
Fairness researchers in machine learning (ML) have coalesced around several fairness criteria which provide formal definitions of what it means for an ML model to be fair.
Resonance in Weight Space: Covariate Shift Can Drive Divergence of SGD with Momentum
In this work, we show that SGDm under covariate shift with a fixed step-size can be unstable and diverge.
IITK at SemEval-2021 Task 10: Source-Free Unsupervised Domain Adaptation using Class Prototypes
To tackle this issue of availability of annotated data, a lot of research has been done on unsupervised domain adaptation that tries to generate systems for an unlabelled target domain data, given labeled source domain data.
Multi Type Mean Field Reinforcement Learning
We consider two different kinds of mean field environments: a) Games where agents belong to predefined types that are known a priori and b) Games where the type of each agent is unknown and therefore must be learned based on observations.
Privacy-Preserving Q-Learning with Functional Noise in Continuous Spaces
Our aim is to protect the value function approximator, without regard to the number of states queried to the function.
Privacy-preserving Q-Learning with Functional Noise in Continuous State Spaces
Our aim is to protect the value function approximator, without regard to the number of states queried to the function.
Adaptive Active Hypothesis Testing under Limited Information
We consider the problem of active sequential hypothesis testing where a Bayesian decision maker must infer the true hypothesis from a set of hypotheses.
The Price of Privacy in Untrusted Recommendation Engines
In the information-rich regime, where each user rates at least a constant fraction of items, a spectral clustering approach is shown to achieve a sample-complexity lower bound derived from a simple information-theoretic argument based on Fano's inequality.
