[Ml-stat-talks] Fwd: [talks] 12:30pm Today- Colloquium Speaker- Percy Liang

Barbara Engelhardt bee at princeton.edu
Thu Oct 1 09:08:35 EDT 2015

Talk of interest today.

---------- Forwarded message ----------
From: Nicole E. Wagenblast <nwagenbl at cs.princeton.edu>
Date: Thu, Oct 1, 2015 at 9:05 AM
Subject: [talks] 12:30pm Today- Colloquium Speaker- Percy Liang
To: "Talks (colloquium)" <talks at lists.cs.princeton.edu>

Colloquium Speaker
Percy Liang, Stanford University
Thursday, October 1st- *12:30pm*
Computer Science 105

Learning Hidden Computational Processes

We are interested in prediction problems in which evaluating the learned
function requires multiple intermediate steps of computation. One
motivation is building a system that can answer complex questions: here the
function would need to map "How many countries have held the Summer
Olympics more than once?" to "3" by applying a sequence of aggregation and
filtering operations on a database.  In this talk, we examine two key
machine learning problems that arise in this setting. First, how do we
model the computational process?  We argue that the classic paradigm of
decoupling modeling from inference is inadequate, and we propose techniques
that directly model the inference procedure. Second, learning is very
difficult: in our example, the supervision "3" constrains the hidden
computational process in a very indirect way.  We propose methods that
relax the output supervision in a parameterized way, and learn both the
relaxation and model parameters jointly subject to an explici
 t computational constraint.  Finally, we show some empirical progress on a
new challenging question answering task.

Percy Liang is an Assistant Professor of Computer Science at Stanford
University (B.S. from MIT, 2004; Ph.D. from UC Berkeley, 2011).  His
research interests include (i) modeling natural language semantics, (ii)
developing machine learning methods that infer rich latent structures from
limited supervision, (iii) and studying the tradeoff between statistical
and computational efficiency.  He is a 2015 Sloan Research Fellow, 2014
Microsoft Research Faculty Fellow, a 2010 Siebel Scholar, and won the best
student paper at the International Conference on Machine Learning in 2008.

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