The rapid progress in artificial intelligence in recent years can largely be attributed to the resurgence of neural networks, which enables learning representations in an end-to-end manner. Although neural network are powerful, they have many limitations. For examples, neural networks are computationally expensive and memory inefficient; Neural network training needs many labeled exampled, especially for tasks that require reasoning and external knowledge.

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In recent years, there has been significant interest in component-based program synthesis, in which the goal is to automatically generate programs from a set of components that meet a high-level specification provided by the user. Automated program synthesis has proven to be useful to both end-users and programmers. For instance, program synthesis has been used to automate tedious tasks that arise in everyday life, such as string manipulations in spreadsheets or data wrangling tasks in R.

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This talk discusses a new paradigm for deep learning that integrates the solution of optimization problems "into the loop." We highlight two challenges present in today's deep learning landscape that involve adding structure to the input or latent space of a model. We will discuss how to overcome some of these challenges with the use of learnable optimization sub-problems that subsume standard architectures and layers.

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For the first time in 25 years, a new non-volatile memory (NVM) category is being created that is two orders of magnitude faster than current durable storage media. The advent of NVM will fundamentally change the dichotomy between volatile memory and durable storage in database systems. These new NVM devices are almost as fast as DRAM, but all writes to it are potentially persistent even after power loss. Existingdatabase systems are unable to take full advantage of this technology because their internal architectures are predicated on the assumption that memory is volatile.

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In this talk we consider "experimental design" problems, which have wide applications in statistics and machine learning. Mathematically, the problem consists of a class of discrete (combinatorial) optimization problems whose exact solutions are generally NP-hard, but their continuous relaxations are usually computationally tractable.

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Five of the Database Group's Masters students will present their Capstone Projects:

Shuyao Bi
— Building in-memory DBMS cost model in Peloton

Patrick Huang
— Extend Query Optimizer in Peloton

Chen Luo
— Robust Hash Join Execution (Chen Luo)

Prashasthi Prabhakar
— JIT Compilation of User-Defined Functions in High Performance Database Management Systems

Min Huang
— ART index and index scan code gen

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There is an unknown n-bit string x. A "trace" is a random substring of x formed by deleting each bit with probability (say) 1/2. Suppose an algorithm has access to independent traces of x. How many does it need to reconstruct x? The previous best method needed about exp(n^{1/2}) traces. We give a simple "mean-based" algorithm that uses about exp(n^{1/3}) traces (and time). We also show that any algorithm working in the restricted "mean-based" framework requires exp(n^{1/3}) traces. The main tool in our work is elementary complex analysis.

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Tree data structures play an important role in almost every area if computer science. Nowadays the explosion of data puts forward a higher demand for the performance of the trees, which requires efficient parallel algorithms for processing large-scale data, as well as a full-featured interface for achieving more functionalities. Traditional algorithm designing on balanced binary trees based on insertions and deletions are plagued with challenges such as being hard to parallelize, single-operand oriented, and varying among different balancing schemes.

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Approximation Algorithms, and its sister field Online Algorithms have had a pro- found impact in theoretical computer science. Questions in this field have created a host of new algorithmic tools and techniques that have had impact in the other fields such as operations research, machine learning and complexity theory. In this thesis, we study several problems and propose new variations on them. We achieve new results on classical problems such as finding indpendent sets in degree-d graphs and fraction- ally sub-additive network design problem.

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DNA read mapping is one of the fundamental problem in bioinformatics. The general goal is to map billions of short pieces of DNA (reads) to a high quality reference genome, while tolerating errors including genomic variations between individuals and the sequencer imprecision.

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