We give a polynomial-time algorithm for learning latent-state linear dynamical systems without system identification, and without assumptions on the spectral radius of the system's transition matrix. The algorithm extends the recently introduced technique of spectral filtering, previously applied only to systems with a symmetric transition matrix, using a novel convex relaxation to allow for the efficient identification of phases.

This paper studies the problem of efficient exploration in finite episodic MDPs. They present a variant of optimistic initialization tuned learning rates for Q-learning that recover a UCB-style algorithm. The main contribution of this work is a polynomial regret bound for perhaps one of the most iconic "model-free" algorithms. There are several things to like about this paper: - Q-learning is perhaps the classic intro to RL algorithms, so it's nice to see that we can recover sample efficient guarantees for a variant of this algorithm. The computational time is also particularly appealing compared to existing model-free algorithms with sqrt{T} *expected* (Bayesian) regret (such as RLSVI), which have much higher computational and memory requirements.

Finding minimum distortion of adversarial examples and thus certifying robustness in neural network classifiers for given data points is known to be a challenging problem. Nevertheless, recently it has been shown to be possible to give a nontrivial certified lower bound of minimum adversarial distortion, and some recent progress has been made towards this direction by exploiting the piece-wise linear nature of ReLU activations. However, a generic robustness certification for general activation functions still remains largely unexplored.

We consider the problem of maximizing a submodular function when given access to its approximate version. Submodular functions are heavily studied in a wide variety of disciplines since they are used to model many real world phenomena and are amenable to optimization. There are many cases however in which the phenomena we observe is only approximately submodular and the optimization guarantees cease to hold. In this paper we describe a technique that yields strong guarantees for maximization of monotone submodular functions from approximate surrogates under cardinality and intersection of matroid constraints. In particular, we show tight guarantees for maximization under a cardinality constraint and 1/(1 + P) approximation under intersection of P matroids.

Google unveiled a slew of new AI tools and features at I/O, dropping the term Gemini 95 times and AI 92 times. However, the best announcement of the entire show wasn't an AI feature; rather, the title went to one of the two hardware products announced -- the Android XR glasses. CNET: I hated smart glasses until I tried Google's Android XR. For the first time, Google gave the public a look at its long-awaited smart glasses, which pack Gemini's assistance, in-lens displays, speakers, cameras, and mics into the form factor of traditional eyeglasses. I had the opportunity to wear them for five minutes, during which I ran through a demo of using them to get visual Gemini assistance, take photos, and get navigation directions.

Tech expert Kurt Knutsson discusses LEVA, the autonomous robot that walks, rolls and lifts 187 pounds of cargo for all-terrain deliveries. Autonomous delivery robots are already starting to change the way goods move around cities and warehouses, but most still need humans to load and unload their cargo. That's where LEVA comes in. Developed by engineers and designers from ETH Zurich and other Swiss universities, LEVA is a robot that can not only navigate tricky environments but also lift and carry heavy boxes all on its own, making deliveries smoother and more efficient. Join the FREE "CyberGuy Report": Get my expert tech tips, critical security alerts and exclusive deals, plus instant access to my free "Ultimate Scam Survival Guide" when you sign up!

TL;DR: Mosalingua uses AI to help you learn a new language, and it's only 98 for life. Being able to speak a second language is super useful. The problem is that learning to speak another language is pretty tough, especially if you're balancing work, school, and so many other responsibilities. If you want an easier way to learn a second, third, or even fourth language, check out Mosalingua. Their self-paced lessons give you the chance to learn up to 11 languages in a way that works for you, and it's only 97.99 for a lifetime subscription (reg.

There is little debate that AI will revolutionize working practices, but there is less agreement about the best way to exploit this transformation. While 90% of CIOs are piloting AI or investing in small or large-scale developments, over two-thirds (67%) haven't seen measurable ROI, according to the recently released Nash Squared/Harvey Nash Digital Leadership Report. "Leaders know the technology, but they're struggling with its application in the business to create value," Nash Squared CIO Ankur Anand told ZDNET during a conversation about the key points emerging from the leadership survey. So, how can business leaders overcome this struggle? Four business leaders provide their best-practice tips for using AI to solve big business problems.

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/