In this paper, we study stochastic structured bandits for minimizing regret.

Prior works havepresented several means to combat this phenomenon in IL.

When I spoke to my phone, my face appeared on the screen, and I said, 'Hi, my name is Robert, and I'm looking forward to telling you about my life.' I was talking to an AI avatar of myself, designed to allow people to'live on' after death so that relatives can talk to them and learn about their lives. My wife's reaction to my AI clone was absolute horror, as she simply said, 'My God, why?' The clone comes courtesy of a'digital afterlife' service, Hereafter.AI, part of a wave of AI-powered'grief tech' created by programmer James Vlahos after his father died of cancer in 2016. The service creates a'Legacy Avatar' that can live on after your death (Rob Waugh/Hereafter) Vlahos programmed a'Dadbot' while his father was still alive, recording his responses to questions - and Hereafter's service now uses AI to make it easier to interact. Science has unearthed several distinct patterns around when people tend to die.

Today's voice assistants are still a far cry from the hyper-intelligent thinking machines we've been musing about for decades. And it's because that technology is actually the combination of three different skills: speech recognition, natural language processing and voice generation. Each of these skills already presents huge challenges. In order to master just the natural language processing part? You pretty much have to recreate human-level intelligence. Deep learning, the technology driving the current AI boom, can train machines to become masters at all sorts of tasks. But it can only learn one at a time. And because most AI models train their skillset on thousands or millions of existing examples, they end up replicating patterns within historical data--including the many bad decisions people have made, like marginalizing people of color and women. Still, systems like the board-game champion AlphaZero and the increasingly convincing fake-text generator GPT-3 have stoked the flames of debate regarding when humans will create an artificial general intelligence--machines that can multitask, think, and reason for themselves. In this episode, we explore how machines learn to communicate--and what it means for the humans on the other end of the conversation. This episode was produced by Jennifer Strong, Emma Cillekens, Anthony Green, Karen Hao and Charlotte Jee.

The term "machine learning" especially refers to algorithms that derive mappings, i.e. intput/output transforms, by using numerical data that provide information about considered transforms. These transforms appear in many problems, related to classification/clustering, regression, system identification, system inversion and input signal restoration/separation. We here first analyze the connections between all these problems, in the classical and quantum frameworks. We then focus on their most challenging versions, involving quantum data and/or quantum processing means, and unsupervised, i.e. blind, learning. Moreover, we propose the quite general concept of SIngle-Preparation Quantum Information Processing (SIPQIP). The resulting methods only require a single instance of each state, whereas usual methods have to very accurately create many copies of each fixed state. We apply our SIPQIP concept to various tasks, related to system identification (blind quantum process tomography or BQPT, blind Hamiltonian parameter estimation or BHPE, blind quantum channel identification/estimation, blind phase estimation), system inversion and state estimation (blind quantum source separation or BQSS, blind quantum entangled state restoration or BQSR, blind quantum channel equalization) and classification. Numerical tests show that our framework moreover yields much more accurate estimation than the standard multiple-preparation approach. Our methods are especially useful in a quantum computer, that we propose to more briefly call a "quamputer": BQPT and BHPE simplify the characterization of the gates of quamputers; BQSS and BQSR allow one to design quantum gates that may be used to compensate for the non-idealities that alter states stored in quantum registers, and they open the way to the much more general concept of self-adaptive quantum gates (see longer version of abstract in paper).

OpenExO Community member Christiaan Dorfling posted a fascinating question about MTP's and Machine learned models. We decided to share the answer and here's a link back to the original post OpenExo Ecosystem-Community-Circles. You will need an account on the platform to get to the full thread. I'd have to start with what is the companies MTP? Someone or some organization is behind it. If they don't have an MTP or they are just bad then their ML uses cases are right in line w/ their MTP.

Quantum Process Tomography (QPT) methods aim at identifying, i.e. estimating, a given quantum process. QPT is a major quantum information processing tool, since it especially allows one to characterize the actual behavior of quantum gates, which are the building blocks of quantum computers. However, usual QPT procedures are complicated, since they set several constraints on the quantum states used as inputs of the process to be characterized. In this paper, we extend QPT so as to avoid two such constraints. On the one hand, usual QPT methods requires one to know, hence to precisely control (i.e. prepare), the specific quantum states used as inputs of the considered quantum process, which is cumbersome. We therefore propose a Blind, or unsupervised, extension of QPT (i.e. BQPT), which means that this approach uses input quantum states whose values are unknown and arbitrary, except that they are requested to meet some general known properties (and this approach exploits the output states of the considered quantum process). On the other hand, usual QPT methods require one to be able to prepare many copies of the same (known) input state, which is constraining. On the contrary, we propose "single-preparation methods", i.e. methods which can operate with only one instance of each considered input state. These two new concepts are here illustrated with practical BQPT methods which are numerically validated, in the case when: i) random pure states are used as inputs and their required properties are especially related to the statistical independence of the random variables that define them, ii) the considered quantum process is based on cylindrical-symmetry Heisenberg spin coupling. These concepts may be extended to a much wider class of processes and to BQPT methods based on other input quantum state properties.

When Andrew Kaplan reminisces, his engrossing tales leave the impression that he's managed to pack multiple lives into a single existence: globe-trotting war correspondent in his 20s, a member of the Israeli army who fought in the Six-Day War, successful entrepreneur and, later, the author of numerous spy novels and Hollywood scripts. Now -- as the silver-haired 78-year-old unwinds with his wife of 39 years in a suburban oasis outside Palm Springs -- he has realized he would like his loved ones to have access to those stories, even when he's no longer alive to share them. Kaplan has agreed to become "AndyBot," a virtual person who will be immortalized in the cloud for hundreds, perhaps thousands, of years. If all goes according to plan, future generations will be able to interact with him using mobile devices or voice computing platforms such as Amazon's Alexa, asking him questions, eliciting stories and drawing upon a lifetime's worth of advice long after his physical body is gone. Someday, Kaplan -- who playfully refers to himself as a "guinea pig" -- may be remembered as one of the world's first "digital humans."

Some explanations to Kaldi's PLDA implementation to make formula derivation easier to catch.