Tommi S. Jaakkola, Ph.D.
Thomas Siebel Professor of Electrical Engineering and Computer Science and the Institute for Data, Systems, and Society
MIT Computer Science and Artificial Intelligence Laboratory
Stata Center, Bldg 32-G470
Cambridge, MA 02139
tommi at csail dot mit dot edu[home] [papers] [research] [people] [courses]
Our goal is to develop a new flexible probabilistic modeling paradigm for high dimensional structured prediction problems. The approach is based on the idea of perturbation models and builds on decades of work on structured probability models and structured prediction as well as advances in relaxations of combinatorial optimization problems. Perturbation models, broadly construed, realize flexible probability models by linking latent randomization of parameters or configurations with combinatorial optimization. One of the main advantages of these models is that, despite the complex distributions they represent, and in contrast to typical structured probability models, they are easy to draw unbiased samples from. Randomization in these models is used as the modeling tool, together with the combinatorial structure of the problem embedded in the optimization part. We seek to understand, leverage, extend, and learn unique and powerful properties of these models as well as mitigate their deficiencies.
Syntactic and semantic parsing
The best performing parsers today are typically discriminative in nature, i.e., they are tailored directly to the goal of predicting the correct (dependency) parse given the sentence. A rich set of features (associated parameters) are introduced into the parsing model in order to tie properties of candidate parse trees to the words (and tags) on the sentence level. This explosion of features is necessary to capture inherent linguistic variability but requires estimating a large number of parameters. Moreover, parsing with rich feature sets is also computationally challenging. Our goals in this context include developing parsimonious (e.g., tensor based) parameterizations, low-complexity inference algorithms, and novel semi-supervised approaches towards robust, cross-domain methods for parsing.
Recommender problems are typically formulated in terms of large matrices where the matrix dimensions refer to users and items. Since only limited information is available about each user, strong regularity assumptions are needed about the underlying rating matrix. Viewing recommender problems in terms of matrices is limiting, however, especially when recommendations involve inherent combinatorial constraints or biases as in recommending sets of items such as accessories, keywords, or more structured objects such as sentences. Our work in this context focuses on developing efficient algorithms for combinatorial recommendations, balancing scaling, statistical accuracy, and privacy.
In computational biology our motivation comes from the need to
understand cellular mechanisms responsible for transcriptional
control. Accurate predictions of this kind are based on identifying regularities across multiple heterogeneous, incomplete, or fragmented sources of information. Finding such regularities forces us to formulate, manipulate, and learn complex models that entertain a number of alternative hypotheses about observations. We have been developing methods to reveal comprehensive and predicative cis- and trans-regulatory networks in collaboration with Prof. David Gifford's group.
We develop automatic on-demand methods for filling multi-way relational tables based on tailored web-queries. Our goal is to ascertain whether any selected multi-way relation holds and find evidential support in the form of articles for positive calls. The broader problem involves a novel combination of collaborative prediction, query formulation, and information extraction (IE). We couple query formulation with extraction, tailoring queries towards articles for which extraction succeeds; we explicitly leverage the fact that the queries and extraction tasks are coupled across the multi-way relations. Many pertinent problems fall naturally in our setting. For example, we seek to identify possible adulterations of food products, i.e., when a potentially harmful chemical is added to a food product in the manufacturing process (often willfully for financial gain). The relations sought in this case are between food products and candidate adulterants (in a context), and the task is to find support for possible relations across scientific, news, and social media articles.