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Vator Splash NY 2012
Splash is Vator’s once-a-quarter evening event, celebrating entrepreneurship with seasoned entrepreneurs sharing lessons and advice, and 10 promising startups pitching onstage. The top 10 are chosen through an online competition. The event draws some 350 to 400 attendees in the entrepreneur community, from seasoned to emerging entrepreneurs, venture capitalists and media.
Building Meaningful Customer Experiences
A one-day workshop on "Building Meaningful Customer Experiences" by the design expert, Nathan Shedroff, author of multiple books including Making Meaning, Experience Design 1.1, Design is the Problem, Experience Design 1 Cards, and, Dictionary of Sustainable Management.
How to start a startup as a non-technical founder
Tech Startup from the Ground Up: Advice from a non-Technical Founder. This workshop will cover the following topics:
- Day Job to Dream Job: Quit and Commit
- Execute! Learn from the Honey Badger
- Structuring & Hiring: Startup Oxygen
- Fundraising: Kickstart, Crowd-source, Accelerate, and Call in the Angels
- Growing Your Business: If Plan A Fails, there are 25 more Letters
- Recommended Resources, Questions from Viewers
Performance Evaluation for Learning Algorithms: Techniques, Application and Issues
The purpose of the tutorial is to promote an appreciation of the need for rigorous and objective evaluation and an understanding of the available alternatives along with their assumptions, constraints and context of application. Machine learning researchers and practitioners alike will all benefit from the contents of the tutorial, which discusses the need for sound evaluation strategies, practical approaches and tools for evaluation, going well beyond those described in existing machine learning and data mining textbooks, so far.
Spectral Approaches to Learning Latent Variable Models
Examples of popular latent variable models include latent tree graphical models and dynamical system models, both of which occupy a fundamental place in engineering, control theory, economics as well as the physical, biological, and social sciences. Unfortunately, to discover the right latent state representation and model parameters, we must solve difficult structural and temporal credit assignment problems. Work on learning latent variable structure has predominantly relied on likelihood maximization and local search heuristics such as expectation maximization (EM); these heuristics often lead to a search space with a host of bad local optima, and may therefore require impractically many restarts to reach a prescribed training precision.
This tutorial will focus on a recently-discovered class of spectral learning algorithms. These algorithms hold the promise of overcoming these problems and enabling learning of latent structure in tree and dynamical system models. Unlike the EM algorithm, spectral methods are computationally efficient, statistically consistent, and have no local optima; in addition, they can be simple to implement, and have state-of-the-art practical performance for many interesting learning problems.
We will describe the main theoretical, algorithmic, and empirical results related to spectral learning algorithms, starting with an overview of linear system identification results obtained in the last two decades, and then focusing on the remarkable recent progress in learning nonlinear dynamical systems, latent tree graphical models, and kernel-based nonparametric models.
PAC-Bayesian Analysis in Supervised, Unsupervised, and Reinforcement Learning
PAC-Bayesian analysis is a basic and very general tool for data-dependent analysis in machine learning. By now, it has been applied in such diverse areas as supervised learning, unsupervised learning, and reinforcement learning, leading to state-of-the-art algorithms and accompanying generalization bounds. PAC-Bayesian analysis, in a sense, takes the best out of Bayesian methods and PAC learning and puts it together: (1) it provides an easy way to exploit prior knowledge (like Bayesian methods); (2) it provides strict and explicit generalization guarantees (like VC theory); and (3) it is data-dependent and provides an easy and strict way of exploiting benign conditions (like Rademacher complexities). In addition, PAC-Bayesian bounds directly lead to efficient learning algorithms.
We will start with a general introduction to PAC-Bayesian analysis, which should be accessible to an average student, who is familiar with machine learning at the basic level. Then, we will survey multiple forms of PAC-Bayesian bounds and their numerous applications in different fields (including supervised and unsupervised learning, finite and continuous domains, and the very recent extension to martingales and reinforcement learning). Some of these applications will be explained in more details, while others will be surveyed at a high level. We will also describe the relations and distinctions between PAC-Bayesian analysis, Bayesian learning, VC theory, and Rademacher complexities. We will discuss the role, value, and shortcomings of frequentist bounds that are inspired by Bayesian analysis.