Zobrazeno 1 - 10
of 4 635
pro vyhledávání: '"A Javanmard"'
Autor:
Khan, Salman, Teeti, Izzeddin, Alitappeh, Reza Javanmard, Stoian, Mihaela C., Giunchiglia, Eleonora, Singh, Gurkirt, Bradley, Andrew, Cuzzolin, Fabio
Autonomous Vehicle (AV) perception systems require more than simply seeing, via e.g., object detection or scene segmentation. They need a holistic understanding of what is happening within the scene for safe interaction with other road users. Few dat
Externí odkaz:
http://arxiv.org/abs/2411.01683
We distinguish between two sources of uncertainty in experimental causal inference: design uncertainty, due to the treatment assignment mechanism, and sampling uncertainty, when the sample is drawn from a super-population. This distinction matters in
Externí odkaz:
http://arxiv.org/abs/2410.21464
Recently, there have been numerous studies on feature learning with neural networks, specifically on learning single- and multi-index models where the target is a function of a low-dimensional projection of the input. Prior works have shown that in h
Externí odkaz:
http://arxiv.org/abs/2410.16449
We study the dynamic pricing problem faced by a broker that buys and sells a large number of financial securities in the credit market, such as corporate bonds, government bonds, loans, and other credit-related securities. One challenge in pricing th
Externí odkaz:
http://arxiv.org/abs/2410.14839
Autor:
Herringer, Paul, Bulchandani, Vir B., Javanmard, Younes, Stephen, David T., Raussendorf, Robert
We present the first examples of topological phases of matter with uniform power for measurement-based quantum computation. This is possible thanks to a new framework for analyzing the computational properties of phases of matter that is more general
Externí odkaz:
http://arxiv.org/abs/2410.02716
Autor:
Javanmard, Younes
We present a quantum algorithm for simulating complex many-body systems and finding their ground states, combining the use of tensor networks and density matrix renormalization group (DMRG) techniques. The algorithm is based on von Neumann's measurem
Externí odkaz:
http://arxiv.org/abs/2407.19348
Autor:
Das, Rudrajit, Dhillon, Inderjit S., Epasto, Alessandro, Javanmard, Adel, Mao, Jieming, Mirrokni, Vahab, Sanghavi, Sujay, Zhong, Peilin
The performance of a model trained with \textit{noisy labels} is often improved by simply \textit{retraining} the model with its own predicted \textit{hard} labels (i.e., $1$/$0$ labels). Yet, a detailed theoretical characterization of this phenomeno
Externí odkaz:
http://arxiv.org/abs/2406.11206
We consider a weakly supervised learning problem called Learning from Label Proportions (LLP), where examples are grouped into ``bags'' and only the average label within each bag is revealed to the learner. We study various learning rules for LLP tha
Externí odkaz:
http://arxiv.org/abs/2406.00487
This work studies algorithms for learning from aggregate responses. We focus on the construction of aggregation sets (called bags in the literature) for event-level loss functions. We prove for linear regression and generalized linear models (GLMs) t
Externí odkaz:
http://arxiv.org/abs/2402.04987
We present a quantum-classical algorithm to study the dynamics of the Rohksar-Kivelson plaquette ladder on NISQ devices. We show that complexity is largely reduced using gauge invariance, additional symmetries, and a crucial property associated to ho
Externí odkaz:
http://arxiv.org/abs/2401.16326