Search Results for author:
Found 19 papers, 1 papers with code
Pure Exploration in Bandits with Linear Constraints
Both these algorithms try to track an optimal allocation based on the lower bound and computed by a weighted projection onto the boundary of a normal cone.
Pragmatic Reasoning in Structured Signaling Games
In this work we introduce a structured signaling game, an extension of the classical signaling game with a similarity structure between meanings in the context, along with a variant of the Rational Speech Act (RSA) framework which we call structured-RSA (sRSA) for pragmatic reasoning in structured domains.
Cultural evolution via iterated learning and communication explains efficient color naming systems
It has been argued that semantic systems reflect pressure for efficiency, and a current debate concerns the cultural evolutionary process that produces this pattern.
Measuring poverty in India with machine learning and remote sensing
In this paper, we use deep learning to estimate living conditions in India.
Thompson Sampling for Bandits with Clustered Arms
We propose algorithms based on a multi-level Thompson sampling scheme, for the stochastic multi-armed bandit and its contextual variant with linear expected rewards, in the setting where arms are clustered.
Learning Approximate and Exact Numeral Systems via Reinforcement Learning
The agents gradually learn to communicate using reinforcement learning and the resulting numeral systems are shown to be efficient in the information-theoretic framework of Regier et al. (2015); Gibson et al. (2017).
Statistical modeling: the three cultures
The algorithmic modeling culture (AMC) refers to practices defining a machine-learning (ML) procedure that generates accurate predictions about an event of interest.
Causal Inference
Methodology
Computers and Society
Models for COVID-19 Pandemic: A Comparative Analysis
COVID-19 pandemic represents an unprecedented global health crisis in the last 100 years.
Analysis of Knowledge Transfer in Kernel Regime
Knowledge transfer is shown to be a very successful technique for training neural classifiers: together with the ground truth data, it uses the "privileged information" (PI) obtained by a "teacher" network to train a "student" network.
Do Kernel and Neural Embeddings Help in Training and Generalization?
The approximated representations induced by these kernels are fed to the neural network and the optimization and generalization properties of the final model are evaluated and compared.
Recovery Bounds on Class-Based Optimal Transport: A Sum-of-Norms Regularization Framework
We develop a novel theoretical framework for understating OT schemes respecting a class structure.
Easy High-Dimensional Likelihood-Free Inference
We introduce a framework using Generative Adversarial Networks (GANs) for likelihood--free inference (LFI) and Approximate Bayesian Computation (ABC) where we replace the black-box simulator model with an approximator network and generate a rich set of summary features in a data driven fashion.
Clustering by Sum of Norms: Stochastic Incremental Algorithm, Convergence and Cluster Recovery
Standard clustering methods such as K-means, Gaussian mixture models, and hierarchical clustering are beset by local minima, which are sometimes drastically suboptimal.
Thompson Sampling For Stochastic Bandits with Graph Feedback
We present a novel extension of Thompson Sampling for stochastic sequential decision problems with graph feedback, even when the graph structure itself is unknown and/or changing.
Weighted Theta Functions and Embeddings with Applications to Max-Cut, Clustering and Summarization
We introduce a unifying generalization of the Lovász theta function, and the associated geometric embedding, for graphs with weights on both nodes and edges.
The Lovász ϑ function, SVMs and finding large dense subgraphs
We show that the random graph with a planted clique is an example of $SVM-\theta$ graph, and as a consequence a SVM based approach easily identifies the clique in large graphs and is competitive with the state-of-the-art.
