Browsing by Author "Jaakkola, Tommi S"
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Active boundary annotation using random MAP perturbations
Maji, Subhransu; Hazan, Tamir; Jaakkola, Tommi S (PLMR, 201404)We address the problem of efficiently annotating labels of objects when they are structured. Often the distribution over labels can be described using a joint potential function over the labels for which sampling is provably ... 
Approximate inference in additive factorial HMMs with application to energy disaggregation
Kolter, Jeremy Z.; Jaakkola, Tommi S. (Proceedings of Machine Learning Research, 201204)This paper considers additive factorial hidden Markov models, an extension to HMMs where the state factors into multiple independent chains, and the output is an additive function of all the hidden states. Although such ... 
Collaborative future event recommendation
Minkov, Einat; Charrow, Ben; Ledlie, Jonathan; Teller, Seth; Jaakkola, Tommi S. (Association for Computing Machinery, 201010)We demonstrate a method for collaborative ranking of future events. Previous work on recommender systems typically relies on feedback on a particular item, such as a movie, and generalizes this to other items or other ... 
Controlling privacy in recommender systems
Xin, Yu; Jaakkola, Tommi S. (Neural Information Processing Systems, 2014)Recommender systems involve an inherent tradeoff between accuracy of recommendations and the extent to which users are willing to release information about their preferences. In this paper, we explore a twotiered notion ... 
Convolutional Embedding of Attributed Molecular Graphs for Physical Property Prediction
Coley, Connor Wilson; Barzilay, Regina; Green Jr, William H; Jaakkola, Tommi S; Jensen, Klavs F (American Chemical Society (ACS), 201707)The task of learning an expressive molecular representation is central to developing quantitative structure–activity and property relationships. Traditional approaches rely on group additivity rules, empirical measurements ... 
Current and Future Roles of Artificial Intelligence in Medicinal Chemistry Synthesis
Struble, Thomas J; Jaakkola, Tommi S; Green Jr, William H; Barzilay, Regina (American Chemical Society (ACS), 202004)Artificial intelligence and machine learning have demonstrated their potential role in predictive chemistry and synthetic planning of small molecules; there are at least a few reports of companies employing in silico ... 
Dual decomposition for parsing with nonprojective head automata
Koo, Terry; Rush, Alexander Matthew; Collins, Michael; Jaakkola, Tommi S.; Sontag, David Alexander (Association for Computational Linguistics, 201010)This paper introduces algorithms for nonprojective parsing based on dual decomposition. We focus on parsing algorithms for nonprojective head automata, a generalization of headautomata models to nonprojective structures. ... 
From random walks to distances on unweighted graphs
Hashimoto, Tatsunori Benjamin; Jaakkola, Tommi S; Sun, Yi (Neural Information Processing Systems Foundation, Inc., 201512)Large unweighted directed graphs are commonly used to capture relations between entities. A fundamental problem in the analysis of such networks is to properly define the similarity or dissimilarity between any two vertices. ... 
Greed Is Good If Randomized: New Inference for Dependency Parsing
Zhang, Yuan; Lei, Tao; Barzilay, Regina; Jaakkola, Tommi S. (201410)Dependency parsing with highorder features results in a provably hard decoding problem. A lot of work has gone into developing powerful optimization methods for solving these combinatorial problems. In contrast, we explore, ... 
Inverse Covariance Estimation for HighDimensional Data in Linear Time and Space: Spectral Methods for Riccati and Sparse Models
Honorio, Jean; Jaakkola, Tommi S. (Association for Uncertainty in Artificial Intelligence (AUAI), 201307)We propose maximum likelihood estimation for learning Gaussian graphical models with a Gaussian (ℓ[2 over 2]) prior on the parameters. This is in contrast to the commonly used Laplace (ℓ[subscript 1) prior for encouraging ... 
Learning bayesian network structure using lp relaxations
Jaakkola, Tommi S.; Sontag, David Alexander; Globerson, Amir; Meila, Marina (Society for Artificial Intelligence and Statistics, 201005)We propose to solve the combinatorial problem of finding the highest scoring Bayesian network structure from data. This structure learning problem can be viewed as an inference problem where the variables specify ... 
Learning efficient random maximum aposteriori predictors with nondecomposable loss functions
Hazan, Tamir; Maji, Subhransu; Keshet, Joseph; Jaakkola, Tommi S. (Neural Information Processing Systems, 2013)In this work we develop efficient methods for learning random MAP predictors for structured label problems. In particular, we construct posterior distributions over perturbations that can be adjusted via stochastic gradient ... 
Learning efficiently with approximate inference via dual losses
Meshi, Ofer; Sontag, David Alexander; Jaakkola, Tommi S.; Globerson, Amir (International Machine Learning Society, 201001)Many structured prediction tasks involve complex models where inference is computationally intractable, but where it can be well approximated using a linear programming relaxation. Previous approaches for learning for ... 
Lineagebased identification of cellular states and expression programs
Hashimoto, Tatsunori Benjamin; Jaakkola, Tommi S.; Sherwood, Richard; Mazzoni, Esteban O.; Wichterle, Hynek; e.a. (Oxford University Press, 201201)We present a method, LineageProgram, that uses the developmental lineage relationship of observed gene expression measurements to improve the learning of developmentally relevant cellular states and expression programs. ... 
LowRank Tensors for Scoring Dependency Structures
Lei, Tao; Zhang, Yuan; Barzilay, Regina; Jaakkola, Tommi S. (Association for Computational Linguistics, 201406)Accurate scoring of syntactic structures such as headmodifier arcs in dependency parsing typically requires rich, highdimensional feature representations. A small subset of such features is often selected manually. This ... 
Metric recovery from directed unweighted graphs
Hashimoto, Tatsunori Benjamin; Sun, Yi; Jaakkola, Tommi S (PMLR, 201505)We analyze directed, unweighted graphs obtained from x[subscript i] ∈ R[superscript d] by connecting vertex i to j iff x[subscript i] − x[subscript j] < ε(x[subscript i]). Examples of such graphs include knearest neighbor ... 
More data means less inference: A pseudomax approach to structured learning
Sontag, David; Meshi, Ofer; Jaakkola, Tommi S.; Globerson, Amir (Neural Information Processing Systems Foundation, 201012)The problem of learning to predict structured labels is of key importance in many applications. However, for general graph structure both learning and inference in this setting are intractable. Here we show that it is ... 
On dual decomposition and linear programming relaxations for natural language processing
Rush, Alexander Matthew; Sontag, David Alexander; Collins, Michael; Jaakkola, Tommi S. (Association for Computational Linguistics, 201010)This paper introduces dual decomposition as a framework for deriving inference algorithms for NLP problems. The approach relies on standard dynamicprogramming algorithms as oracle solvers for subproblems, together ... 
On measure concentration of random maximum aposteriori perturbations
Orabona, Francesco; Hazan, Tamir; Sarwate, Anand D.; Jaakkola, Tommi S. (Association for Computing Machinery (ACM), 2014)The maximum aposteriori (MAP) perturbation framework has emerged as a useful approach for inference and learning in high dimensional complex models. By maximizing a randomly perturbed potential function, MAP perturbations ... 
On sampling from the Gibbs distribution with random maximum aposteriori perturbations
Hazan, Tamir; Maji, Subhransu; Jaakkola, Tommi S. (Neural Information Processing Systems, 2013)In this paper we describe how MAP inference can be used to sample efficiently from Gibbs distributions. Specifically, we provide means for drawing either approximate or unbiased samples from Gibbs' distributions by introducing ...