Communication-Efficient Integrative Regression in High-Dimensions

We consider the task of meta-analysis in high-dimensional settings in which the data sources we wish to integrate are similar but non-identical. To borrow strength across such heterogeneous data sources, we introduce a global parameter that addresses several identification issues. We also propose a one-shot estimator of the global parameter that preserves the anonymity of the data sources and converges at a rate that depends on the size of the combined dataset. Finally, we demonstrate the benefits of our approach on a large-scale drug treatment dataset involving several different cancer cell lines

Volume Holographic Optical Elements

The final two papers are concerned with the analysis of novel holograms. Banerjee et al. investigate holographic recording and reconstruction for edge-lit holograms recorded in a 90-degree geometry. Various cases of recording and readout that incorporate propagational diffraction have been modeled. It is shown that the 90-degree geometry can result in beam shaping, as evidenced through preliminary experimental results with photorefractive lithium niobate. Nguyen et al. propose a new approach for designing computer-generated holograms. An artificial neural network is used to initiate the genetic algorithm so that the high computation cost of genetic algorithms for synthesizing holograms is significantly reduced while the high diffraction efficiency and uniformity are obtained

Conditional independence testing under misspecified inductive biases

Conditional independence (CI) testing is a fundamental and challenging task in modern statistics and machine learning. Many modern methods for CI testing rely on powerful supervised learning methods to learn regression functions or Bayes predictors as an intermediate step; we refer to this class of tests as regression-based tests. Although these methods are guaranteed to control Type-I error when the supervised learning methods accurately estimate the regression functions or Bayes predictors of interest, their behavior is less understood when they fail due to misspecified inductive biases; in other words, when the employed models are not flexible enough or when the training algorithm does not induce the desired predictors. Then, we study the performance of regression-based CI tests under misspecified inductive biases. Namely, we propose new approximations or upper bounds for the testing errors of three regression-based tests that depend on misspecification errors. Moreover, we introduce the Rao-Blackwellized Predictor Test (RBPT), a regression-based CI test robust against misspecified inductive biases. Finally, we conduct experiments with artificial and real data, showcasing the usefulness of our theory and methods.Comment: NeurIPS 2023 proceeding

On the group theoretic structure of a class of quantum dialogue protocols

Intrinsic symmetry of the existing protocols of quantum dialogue are explored. It is shown that if we have a set of mutually orthogonal $n$-qubit states {\normalsize $\{|\phi_{0}>,|\phi_{1}>,....,|\phi_{i}\}$ and a set of $m-qubit$ ($m\leq n$) unitary operators $\{U_{0},U_{2},...,U_{2^{n}-1}\}:U_{i}|\phi_{0}>=|\phi_{i}>$ and $\{U_{0},U_{2},...,U_{2^{n}-1}\}$ forms a group under multiplication then it would be sufficient to construct a quantum dialogue protocol using this set of quantum states and this group of unitary operators}. The sufficiency condition is used to provide a generalized protocol of quantum dialogue. Further the basic concepts of group theory and quantum mechanics are used here to systematically generate several examples of possible groups of unitary operators that may be used for implementation of quantum dialogue. A large number of examples of quantum states that may be used to implement the generalized quantum dialogue protocol using these groups of unitary operators are also obtained. For example, it is shown that GHZ state, GHZ-like state, W state, 4 and 5 qubit Cluster states, Omega state, Brown state, $Q_{4}$ state and $Q_{5}$ state can be used for implementation of quantum dialogue protocol. The security and efficiency of the proposed protocol is appropriately analyzed. It is also shown that if a group of unitary operators and a set of mutually orthogonal states are found to be suitable for quantum dialogue then they can be used to provide solutions of socialist millionaire problem.Comment: 15 page

Estimating Fr\'echet bounds for validating programmatic weak supervision

We develop methods for estimating Fr\'echet bounds on (possibly high-dimensional) distribution classes in which some variables are continuous-valued. We establish the statistical correctness of the computed bounds under uncertainty in the marginal constraints and demonstrate the usefulness of our algorithms by evaluating the performance of machine learning (ML) models trained with programmatic weak supervision (PWS). PWS is a framework for principled learning from weak supervision inputs (e.g., crowdsourced labels, knowledge bases, pre-trained models on related tasks, etc), and it has achieved remarkable success in many areas of science and engineering. Unfortunately, it is generally difficult to validate the performance of ML models trained with PWS due to the absence of labeled data. Our algorithms address this issue by estimating sharp lower and upper bounds for performance metrics such as accuracy/recall/precision/F1 score