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Found 8 papers, 0 papers with code
Statistical Inference with Limited Memory: A Survey
The problem of statistical inference in its various forms has been the subject of decades-long extensive research.
Batches Stabilize the Minimum Norm Risk in High Dimensional Overparameterized Linear Regression
Learning algorithms that divide the data into batches are prevalent in many machine-learning applications, typically offering useful trade-offs between computational efficiency and performance.
Planted Bipartite Graph Detection
To provide an evidence for this statistical computational gap, we prove computational lower bounds based on the low-degree conjecture, and show that the class of low-degree polynomials algorithms fail in the conjecturally hard region.
Learning User Preferences in Non-Stationary Environments
One of the main surprising observations in our experiments is the fact our algorithm outperforms other static algorithms even when preferences do not change over time.
Simple Modulo can Significantly Outperform Deep Learning-based Deepcode
This Modulo-SK scheme has been omitted from the performance comparisons made in the Deepcode paper, due to its use of common randomness (dither), and in a later version since it was incorrectly interpreted as a variable-length coding scheme.
Sharp Thresholds of the Information Cascade Fragility Under a Mismatched Model
We analyze a sequential decision making model in which decision makers (or, players) take their decisions based on their own private information as well as the actions of previous decision makers.
Communication Complexity of Estimating Correlations
Our results also imply an $\Omega(n)$ lower bound on the information complexity of the Gap-Hamming problem, for which we show a direct information-theoretic proof.
Distributed Estimation of Gaussian Correlations
We study a distributed estimation problem in which two remotely located parties, Alice and Bob, observe an unlimited number of i. i. d.
