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SOCS Graduate Seminar Series Seminar Schedule

Date Category Seminar Info
2014/04/15 Graduate Seminar Series Place: MC103
Time: 12:10 - 12:40
Speaker: Pierre-Luc Bacon
Affiliation: PhD Student, SOCS
Title: A short tutorial on starcluster and ipython parallel
Abstract:

I recently faced the challenge of computing some memory-hungry statistics on our modest lab machines. With a conference deadline approaching, I had to quickly find a solution that would not involve buying new hardware. Due to the administrative delays, applying to CLUMEQ was also not an option. I first experimented with a duck-tape homemade cluster solution that allowed me to pool the computing power of all our lab machines simultaneously. While fully functional, this solution was limited by the lack of administrative privileges on the SOCS machines. And then came Starcluster. I will tell you the story of how I used Amazon EC2 to fit 750Gb of data in RAM while sipping a Cappuccino from a fancy coffee place. Not only did I get my results back in less than 30 mins, but the overall experiment also only cost $5.

I will give a brief tutorial on how to set up Starcluster ( http://star.mit.edu/cluster/) with with IPython parallel. I will also show you how to reduce the cost of your experiment even more by bidding on cheap "spot instances". Roboticists, biologists, machine learnists, and all other "ists" dealing with abundant data are welcome.


2014/04/08 Graduate Seminar Series Place: MC103
Time: 12:10 - 12:40
Speaker: Bundit Laekhanukit
Affiliation: PhD Student, McGill SOCS
Title: The Impossibility of PAC-learning DFAs
Abstract:

Probably approximately correct learning (PAC learning) is a learning model proposed by Leslie Valiant that introduces the concepts of computational complexity to machine learning. In particular, given positive and negative samples, the learner is expected to find a polynomial-time computable function that could approximately distinguish between positive and negative samples.

In this work, we study the PAC-learnability of DFAs, i.e., the case where positive and negative samples are drawn from an unknown DFA. We show that, unless NP=RP, DFAs are not PAC-learnable, thus, answering an open question raised 25 years ago by Pitt and Warmuth.

This is a joint work with Parinya Chalermsook and Danupon Nanongkai.


2014/04/02 Graduate Seminar Series Place: MC320
Time: 12:10 - 12:40
Speaker: Annie Ying
Affiliation: PhD Candidate, McGill SOCS
Title: Tips on Applying for Ethics Approval for Research Involving Human Participants
Abstract:

For some of us who do research involving human participants, one of the first steps is to apply for approval from the McGill Reseasrch Ethics Board. In this talk, I will share some of my experience in writing the application for ethics approval in my research.


2014/03/25 Graduate Seminar Series Place: MC103
Time: 12:10 - 12:40
Speaker: Ouais Alsharif
Affiliation:
Title: Lifelong Learning of Discriminative Representations
Abstract:

We envision a machine learning service provider facing a continuous stream of problems with the same input domain, but with output domains that may differ. Clients present the provider with problems implicitly, by labeling a few example inputs, and then ask the provider to train models which reasonably extend their labelings to novel inputs. The provider wants to avoid constraining its users to a set of common labels, so it does not assume any particular correspondence between labels for a new task and labels for previously encountered tasks. To perform well in this setting, the provider needs a representation of the input domain which, in expectation, permits effective models for new problems to be learned efficiently from a small number of examples. While this bears a resemblance to settings considered in previous work on multitask and lifelong learning, our non-assumption of inter-task label correspondence leads to a novel algorithm: Lifelong Learner of Discriminative Representations (LLDR), which explicitly minimizes a proxy for the intra-task small-sample generalization error. We examine the relative benefits of our approach on a diverse set of real-world datasets in three significant scenarios: representation learning, multitask learning and lifelong learning.


2014/02/24 Graduate Seminar Series Place: MC320
Time: 12:10 - 12:40
Speaker: Annie Ying
Affiliation: PhD Candidate, SOCS
Title: Selection and Presentation Practices in Code Example Summarization
Abstract:

Code examples are an important source for answering questions about software libraries and applications. Many usage contexts for code examples require them to be distilled to their essence: for example when serving as cues to longer documents, or for reminding developers of a previously-known idiom. We conducted a study on the practices employed by 16 programmers to summarize code examples. As part of the study, we collected 156 pairs of code examples and their summaries, along with over 26 hours of think-aloud verbalizations detailing the decisions of the participants during their summarization activities. We report on the summarization process, provide a list of practices followed by the participants to summarize code examples, and propose empirically-supported hypotheses justifying the use of specific practices. The results provide a grounded basis for the development of code example summarization technology.

Special acknowledgements to the study's participants, many of whom are from SOCS!


2014/02/20 Graduate Seminar Series Place: MC103
Time: 12:10 - 12:55
Speaker: Anqi Xu
Affiliation: PhD student, McGill CIM
Title: An Introduction to Open-Source 3D Printing
Abstract:

3-D printing technologies have existed in research and industrial domains for several decades, although in recent years many of these designs and concepts are beginning to transition into the consumer and hobbyist communities. In this talk I will present a brief introduction to this exciting and fast-evolving field, and highlight a number of open-source (and other free) tools and services for 3-D printing hobbyists. I will also give a live demonstration of the end-to-end process of 3-D printing, starting from the design of a object model from scratch, to its preparation for 3-D printing, and culminating with an actual printed object by the end of the talk.

Come check out various 3-D printed models and functional objects, learn about some free and powerful tools and services, and discover how YOU can participate in the open-source 3-D printing revolution!


2014/02/13 Graduate Seminar Series Place: MC103
Time: 12:10 - 12:40
Speaker: Hang Ma
Affiliation: MSc Candidate, SOCS
Title: Information Gathering and Reward Exploitation of Subgoals for POMDPs
Abstract:

Planning in large partially observable Markov decision processes (POMDPs) is challenging especially when a long planning horizon is required. A few recent algorithms successfully tackle this case but at the expense of weaker information-gathering capacity. In this paper, we propose \emph{Information Gathering and Reward Exploitation of Subgoals} (IGRES), a randomized POMDP planning algorithm that leverages information in the state space to automatically generate ``macro-actions'' that can tackle tasks with long planning horizons, while locally exploring the belief space to allow effective information gathering. Experimental results show that IGRES is an effective multi-purpose POMDP solver, providing state-of-the-art performance for both long horizon planning tasks and information gathering tasks on benchmark domains. Additional experiments with an ecological adaptive management problem presented in the IJCAI 2013 data challenge track indicate that IGRES is a promising tool for POMDP planning in real-world settings.


2014/02/06 Graduate Seminar Series Place: MC103
Time: 12:10 - 12:40
Speaker: Francisco Ferreira
Affiliation: PhD student, McGill SOCS
Area: Computation and Logic Group
Title: An opinionated history of functional programming.
Abstract:

The opinionated nature of this talk is because of the selection of ideas. Functional programming has a very long history, and as such this presentation will revolve around only a handful of the ideas that motivate the current programming languages in this space. Topics range from the lambda calculus to Lisp, from logic to types, and how the paradigm helps with important topics like abstraction, and hiding implementation details. The talk will not cover the implementation techniques, or discuss the exhaustive family tree of languages; rather, the main point will be to briefly discuss some key insights that lead to languages such as Lisp, Scheme, Haskell, ML and Agda.


2014/01/23 Graduate Seminar Series Place: MC103
Time: 12:10 - 12:40
Speaker: Sheldon Andrews
Affiliation: PhD Candidate, SOCS
Title: FORKS: Interactive Compliant Mechanisms with Parallel State Computation
Abstract:

We present a method for the simulation of compliant, articulated structures using a plausible approximate model that focuses on modeling endpoint interaction. We approximate the structure’s behavior about a reference configuration, resulting in a first order reduced compliant system, or FORK-1S. Several levels of approximation are available depending on which parts and surfaces we would like to have interactive contact forces, allowing various levels of detail to be selected. Our approach is fast and computation of the full structure’s state may be parallelized. Our approach is suitable for stiff, articulate grippers, such as those used in robotic simulation, or physics based characters under static proportional derivative control. We demonstrate that simulations with our method can deal with kinematic chains and loops with non-uniform stiffness across joints, and that it produces plausible effects due to stiffness, damping, and inertia.

http://www.cs.mcgill.ca/~sandre17/forks/


2014/01/14 Graduate Seminar Series Place: MC103
Time: 12:10 - 12:40
Speaker: Christopher Dragert
Affiliation: PhD Candidate, Games Research @ McGill (gr@m) lab
Title: Exploring Model-Driven Development of Game AI
Abstract:

Game development is often an ad-hoc affair, focused more on performance and meeting deadlines than on writing maintainable and well-structured code. AI in particular is often highly customized to a specific game context, impairing reusability and verification. During my Ph.D. research, I explored how a model-driven approach to game AI can improve the development process. In this talk, I will present some of my major results, including the layered statechart-based AI formalism, which represents AI logic and behaviour in a fundamentally modular fashion. This approach leads naturally to effective reuse of AI behaviours, generative approaches to create varied NPC populations, tool support for managing development of game AI, and verification of AI behaviours through model-checking.