Zobrazeno 1 - 10
of 193
pro vyhledávání: '"Lange, Jane"'
We investigate the problem of predicting the output behavior of unknown quantum channels. Given query access to an $n$-qubit channel $E$ and an observable $O$, we aim to learn the mapping \begin{equation*} \rho \mapsto \mathrm{Tr}(O E[\rho]) \end{equ
Externí odkaz:
http://arxiv.org/abs/2409.03684
Autor:
A'mar, Teresa, Beatty, J David, Fedorenko, Catherine, Markowitz, Daniel, Corey, Thomas, Lange, Jane, Schwartz, Stephen M, Huang, Bin, Chubak, Jessica, Etzioni, Ruth
Publikováno v:
JMIR Cancer, Vol 6, Iss 2, p e23821 (2020)
Externí odkaz:
https://doaj.org/article/f6865018f62140908b62ba742a2348bc
Autor:
A'mar, Teresa, Beatty, J David, Fedorenko, Catherine, Markowitz, Daniel, Corey, Thomas, Lange, Jane, Schwartz, Stephen M, Huang, Bin, Chubak, Jessica, Etzioni, Ruth
Publikováno v:
JMIR Cancer, Vol 6, Iss 2, p e18143 (2020)
BackgroundThere is a need for automated approaches to incorporate information on cancer recurrence events into population-based cancer registries. ObjectiveThe aim of this study is to determine the accuracy of a novel data mining algorithm to extrac
Externí odkaz:
https://doaj.org/article/079e30591ba440f5a48a5e19d2c259c1
We propose a simple generalization of standard and empirically successful decision tree learning algorithms such as ID3, C4.5, and CART. These algorithms, which have been central to machine learning for decades, are greedy in nature: they grow a deci
Externí odkaz:
http://arxiv.org/abs/2310.01551
We study local filters for the Lipschitz property of real-valued functions $f: V \to [0,r]$, where the Lipschitz property is defined with respect to an arbitrary undirected graph $G=(V,E)$. We give nearly optimal local Lipschitz filters both with res
Externí odkaz:
http://arxiv.org/abs/2308.14716
Multi-cancer early detection (MCED) tests offer to screen for multiple types of cancer with a single blood sample. Despite their promising diagnostic performance, evidence regarding their population benefit is not yet available. Expecting that benefi
Externí odkaz:
http://arxiv.org/abs/2307.00092
Autor:
Lange, Jane, Vasilyan, Arsen
We give the first agnostic, efficient, proper learning algorithm for monotone Boolean functions. Given $2^{\tilde{O}(\sqrt{n}/\varepsilon)}$ uniformly random examples of an unknown function $f:\{\pm 1\}^n \rightarrow \{\pm 1\}$, our algorithm outputs
Externí odkaz:
http://arxiv.org/abs/2304.02700
We show how any PAC learning algorithm that works under the uniform distribution can be transformed, in a blackbox fashion, into one that works under an arbitrary and unknown distribution $\mathcal{D}$. The efficiency of our transformation scales wit
Externí odkaz:
http://arxiv.org/abs/2303.16208
In the certification problem, the algorithm is given a function $f$ with certificate complexity $k$ and an input $x^\star$, and the goal is to find a certificate of size $\le \text{poly}(k)$ for $f$'s value at $x^\star$. This problem is in $\mathsf{N
Externí odkaz:
http://arxiv.org/abs/2211.02257
We design an algorithm for finding counterfactuals with strong theoretical guarantees on its performance. For any monotone model $f : X^d \to \{0,1\}$ and instance $x^\star$, our algorithm makes \[ {S(f)^{O(\Delta_f(x^\star))}\cdot \log d}\] queries
Externí odkaz:
http://arxiv.org/abs/2207.07072