We propose a mechanism for answering an arbitrarily long sequence of potentially adaptive statistical queries, by charging a price for each query and using the proceeds to collect additional samples. Crucially, we guarantee statistical validity without any assumptions on how the queries are generated. We also ensure with high probability that the cost for $M$ non-adaptive queries is $O(\log M)$, while the cost to a potentially adaptive user who makes $M$ queries that do not depend on any others is $O(\sqrt{M})$.

We propose a mechanism for answering an arbitrarily long sequence of potentially adaptive statistical queries, by charging a price for each query and using the proceeds to collect additional samples. Crucially, we guarantee statistical validity without any assumptions on how the queries are generated. We also ensure with high probability that the cost for M non-adaptive queries is O(\log M), while the cost to a potentially adaptive user who makes M queries that do not depend on any others is O(\sqrt{M}) .

This paper studies the problem of answering a (possibly infinite) sequence of (adaptive and non-adaptive) statistical queries without overfitting. Queries trigger the acquisition of fresh data when the mechanism determines that overfitting is likely, so adaptive queries necessitate new data. By continually acquiring fresh data as needed, the mechanism can (whp) guarantee accuracy in perpetuity. Moreover, by passing on the "cost" of data acquisition to queries that trigger it, the mechanism guarantees that (whp) non-adaptive queries pay cost O(log(# queries)) while adaptive queries pay cost O(sqrt(# queries)). Suggested applications are the normal ones for adaptive data analysis: ML competition leaderboards and scientific discovery.

Point processes often have a natural interpretation with respect to a continuous process. We propose a point process construction that describes arrival time observations in terms of the state of a latent diffusion process. In this framework, we relate the return times of a diffusion in a continuous path space to new arrivals of the point process. This leads to a continuous sample path that is used to describe the underlying mechanism generating the arrival distribution. These models arise in many disciplines, such as financial settings where actions in a market are determined by a hidden continuous price or in neuroscience where a latent stimulus generates spike trains. Based on the developments in It\^o's excursion theory, we propose methods for inferring and sampling from the point process derived from the latent diffusion process. We illustrate the approach with numerical examples using both simulated and real data. The proposed methods and framework provide a basis for interpreting point processes through the lens of diffusions.

Jim O'Brien, general manager, Americas at RAVIN.AI, discusses how AI can be used as a tool to increase transparency in used car sales Consumers have turned the used car market into one of the hottest ever, with prices and sales volumes hitting record highs as new vehicles remain in short supply. But these sales of pre-owned vehicles are still challenging and complex for both buyers and dealers. So now is the time for AI technology, widely deployed in other parts of the auto industry from manufacturing to marketing to autonomous driving features, to play a larger role in making used car sales more efficient and transparent -- for both buyers and sellers. The explosion of used car sales means an explosion of opportunity for dealers -- and the salespeople that sell them. For those who do well, the rewards are great; the gross profit on a used car is between 12% and 15% of the total price, compared to about 7% for new cars.

We propose a mechanism for answering an arbitrarily long sequence of potentially adaptive statistical queries, by charging a price for each query and using the proceeds to collect additional samples. Crucially, we guarantee statistical validity without any assumptions on how the queries are generated. We also ensure with high probability that the cost for $M$ non-adaptive queries is $O(\log M)$, while the cost to a potentially adaptive user who makes $M$ queries that do not depend on any others is $O(\sqrt{M})$. Papers published at the Neural Information Processing Systems Conference.

When we talk about artificial intelligence (AI), we often speak in giant, world-shifting terms about revolutionizing a certain industry. But AI can also benefit a single person at a time. In the case of Intello Labs, its AI can be used to help prevent a poor farmer from getting screwed. Food inspection is often still done manually. One person's perfect tomato may be another's piece of trash, and these basic biases can lead to an imbalance of power.

Plus, it's not just for data analysts -- machine learning has real benefits in the lives of the average consumer. Ever wonder how Netflix serves up recommendations for the next movie or how your smartphone knows that you will be driving to work on Monday morning? Those are both examples of machine learning. How is machine learning different from ordinary analytics? With traditional methods, an analyst defines the objective and looks for correlations between the objective and a defined set of data inputs.

Denver-based startup Bext Holdings Inc. (Bext360) is harnessing the power of artificial intelligence and blockchain technology to make it easier for coffee farmers around the world to get paid a fair price for the coffee beans they grow and sell. The company has developed a unique scale-inspired machine that utilizes machine learning and AI to rapidly analyze the quality of a farmer's coffee cherries and divide them into grades in the field. Both farmers and buyers will then use Bext360's mobile app to negotiate a fair price. Previously, buyers were required to sort and inspect coffee beans manually, a process that left farmers in limbo -- and without pay -- for months at a time. Bext360 uses the Stellar network, a distributed decentralized platform for real-time transactions, to operate its app and cloud-based software.