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Statistics-Powered ML: Reliable Black-Box Inference from Untrusted Data
Abstract AI systems are increasingly shaping people’s lives, opportunities, and scientific progress. But how can we trust the inferences of such complex, black-box systems? This question becomes even more urgent in the presence of two core challenges that are ubiquitous in high-stakes applications: data scarcity and test-time distribution shift. These issues not only limit… Continue Reading Statistics-Powered ML: Reliable Black-Box Inference from Untrusted Data
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Large Alphabet Inference
Abstract: Consider a finite sample from an unknown distribution over a large alphabet. Making inference in a large alphabet regime is a fundamental problem in statistics and related fields, which entails several basic challenges. For example, how accurately can we infer the parameters of events that do not appear in the sample? What can we say about the most frequent events in… Continue Reading Large Alphabet Inference
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The beauty and mystery of total variation
Abstract: The variational distance between probability distributions is, in some natural sense, the canonical one. Its central importance is readily evident in probability, statistics, privacy, and algorithms. Unfortunately, it is an analytically unwieldy object, and in particular, is strongly computationally intractable, even for products of Bernoullis. This talk, after providing the necessary background, will discuss… Continue Reading The beauty and mystery of total variation
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The beauty and mystery of total variation
Abstract: The variational distance between probability distributions is, in some natural sense, the canonical one. Its central importance is readily evident in probability, statistics, privacy, and algorithms. Unfortunately, it is an analytically unwieldy object, and in particular, is strongly computationally intractable, even for products of Bernoullis. This talk, after providing the necessary background, will discuss… Continue Reading The beauty and mystery of total variation
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Process Mining in Stochastic Settings
Abstract: Alignment-based conformance checking constructs optimal correspondences between observed process executions and normative models, enabling precise identification of deviations for root-cause analysis and process improvement. However, two fundamental challenges limit its applicability in modern settings: exponential state-space growth, which renders exact methods infeasible for very long traces, and uncertainty introduced when activity labels are inferred… Continue Reading Process Mining in Stochastic Settings
people
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Cohen, (Aya) Ayala
Professor Ayala Cohen is the Head of the Statistics Laboratory that operates within the Technion Research and Development Foundation Ltd. and is part of the Faculty of Industrial Engineering and Management. Its staff includes in addition to Professor Cohen, two statisticians, with a Ph.D degree, and Professor Paul D. Feigin, who serves as a consultant.… Continue Reading Cohen, (Aya) Ayala
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Levin, Asaf
Professor Asaf Levin joined the Technion in 2008. He received his Ph.D. in Operations Research from Tel Aviv University in 2003. From 2003 to 2004 he was a Postdoctoral Fellow at the Minerva Optimization Center, the Technion, then, he joined the Department of Statistics at the Hebrew University of Jerusalem as a lecturer. Prof. Levin… Continue Reading Levin, Asaf
