[Theory] NOW: 2/24 Talks at TTIC: Hongyuan Mei, Johns Hopkins University

[Mary Marre]

*When:*      Wednesday, February 24th at* 11:10 am CT*



*Where:*     Zoom Virtual Talk (*register in advance here
<https://uchicagogroup.zoom.us/webinar/register/WN_qJjjpBu2QTeaVuNFFUuANA>*)


*Who: *       Hongyuan Mei, Johns Hopkins University


*Title: * Probabilistic Modeling for Event Sequences

*Abstract:* Suppose we are monitoring discrete events in real time.  Can we
predict what events will occur in the future, and when? For example, can we
probabilistically predict a patient's prognosis, eventual diagnosis, and
treatment cost based on their symptoms and treatments so far? What will an
online customer buy in future? What will a social media user share, like,
or comment on? What workload will a computer system receive over the next 5
minutes?

This talk will present the neural Hawkes process (NHP), a flexible
probabilistic model that supports such reasoning.  I will sketch methods
for estimating its parameters (via MLE and NCE), sampling predictions of
the future (via rejection sampling), and imputing past events that we have
missed (via particle smoothing).

I'll then show how to scale the NHP (or neural sequential models in
general) to real-world domains that involve many event types.  We begin
with a temporal deductive database that tracks how relevant facts including
the possible event types change over time. We take the system state to be a
collection of vector-space embeddings of these facts, and derive a deep
recurrent dynamic neural architecture from the temporal Datalog program
that specifies the temporal database.  We call this method ``neural Datalog
through time.''

I'll also sketch a few future research directions, including embedding the
NHP model within a reinforcement learner to discover causal structure and
learn intervention policies that can improve future outcomes.

This work was done with Jason Eisner and other collaborators including
Guanghui Qin, Tom Wan, and Minjie Xu.

*Bio:* Hongyuan Mei is a Ph.D. student in the Department of Computer
Science at the Johns Hopkins University (JHU), affiliated with the Center
for Language and Speech Processing (CLSP). He is a Bloomberg Data Science
PhD Fellow and the 2020 recipient of the Frederick Jelinek Fellowship.  He
develops machine learning methods for real-world problems -- especially
probabilistic models for both structured and unstructured data, along with
efficient algorithms for training and inference, with applications in event
sequence modeling and natural language processing. His papers have appeared
at NeurIPS, ICML, NAACL, and AAAI.


*Host: *Matthew Walter <mwalter at ttic.edu>


Mary C. Marre
Faculty Administrative Support
*Toyota Technological Institute*
*6045 S. Kenwood Avenue*
*Room 517*
*Chicago, IL  60637*
*p:(773) 834-1757*
*f: (773) 357-6970*
*mmarre at ttic.edu <mmarre at ttic.edu>*


On Wed, Feb 24, 2021 at 10:31 AM Mary Marre <mmarre at ttic.edu> wrote:

> *When:*      Wednesday, February 24th at* 11:10 am CT*
>
>
>
> *Where:*     Zoom Virtual Talk (*register in advance here
> <https://uchicagogroup.zoom.us/webinar/register/WN_qJjjpBu2QTeaVuNFFUuANA>*
> )
>
>
> *Who: *       Hongyuan Mei, Johns Hopkins University
>
>
> *Title: * Probabilistic Modeling for Event Sequences
>
> *Abstract:* Suppose we are monitoring discrete events in real time.  Can
> we predict what events will occur in the future, and when? For example, can
> we probabilistically predict a patient's prognosis, eventual diagnosis, and
> treatment cost based on their symptoms and treatments so far? What will an
> online customer buy in future? What will a social media user share, like,
> or comment on? What workload will a computer system receive over the next 5
> minutes?
>
> This talk will present the neural Hawkes process (NHP), a flexible
> probabilistic model that supports such reasoning.  I will sketch methods
> for estimating its parameters (via MLE and NCE), sampling predictions of
> the future (via rejection sampling), and imputing past events that we have
> missed (via particle smoothing).
>
> I'll then show how to scale the NHP (or neural sequential models in
> general) to real-world domains that involve many event types.  We begin
> with a temporal deductive database that tracks how relevant facts including
> the possible event types change over time. We take the system state to be a
> collection of vector-space embeddings of these facts, and derive a deep
> recurrent dynamic neural architecture from the temporal Datalog program
> that specifies the temporal database.  We call this method ``neural Datalog
> through time.''
>
> I'll also sketch a few future research directions, including embedding the
> NHP model within a reinforcement learner to discover causal structure and
> learn intervention policies that can improve future outcomes.
>
> This work was done with Jason Eisner and other collaborators including
> Guanghui Qin, Tom Wan, and Minjie Xu.
>
> *Bio:* Hongyuan Mei is a Ph.D. student in the Department of Computer
> Science at the Johns Hopkins University (JHU), affiliated with the Center
> for Language and Speech Processing (CLSP). He is a Bloomberg Data Science
> PhD Fellow and the 2020 recipient of the Frederick Jelinek Fellowship.  He
> develops machine learning methods for real-world problems -- especially
> probabilistic models for both structured and unstructured data, along with
> efficient algorithms for training and inference, with applications in event
> sequence modeling and natural language processing. His papers have appeared
> at NeurIPS, ICML, NAACL, and AAAI.
>
>
> *Host: *Matthew Walter <mwalter at ttic.edu>
>
>
> Mary C. Marre
> Faculty Administrative Support
> *Toyota Technological Institute*
> *6045 S. Kenwood Avenue*
> *Room 517*
> *Chicago, IL  60637*
> *p:(773) 834-1757*
> *f: (773) 357-6970*
> *mmarre at ttic.edu <mmarre at ttic.edu>*
>
>
> On Tue, Feb 23, 2021 at 3:30 PM Mary Marre <mmarre at ttic.edu> wrote:
>
>> *When:*      Wednesday, February 24th at* 11:10 am CT*
>>
>>
>>
>> *Where:*     Zoom Virtual Talk (*register in advance here
>> <https://uchicagogroup.zoom.us/webinar/register/WN_qJjjpBu2QTeaVuNFFUuANA>*
>> )
>>
>>
>> *Who: *       Hongyuan Mei, Johns Hopkins University
>>
>>
>> *Title: * Probabilistic Modeling for Event Sequences
>>
>> *Abstract:* Suppose we are monitoring discrete events in real time.  Can
>> we predict what events will occur in the future, and when? For example, can
>> we probabilistically predict a patient's prognosis, eventual diagnosis, and
>> treatment cost based on their symptoms and treatments so far? What will an
>> online customer buy in future? What will a social media user share, like,
>> or comment on? What workload will a computer system receive over the next 5
>> minutes?
>>
>> This talk will present the neural Hawkes process (NHP), a flexible
>> probabilistic model that supports such reasoning.  I will sketch methods
>> for estimating its parameters (via MLE and NCE), sampling predictions of
>> the future (via rejection sampling), and imputing past events that we have
>> missed (via particle smoothing).
>>
>> I'll then show how to scale the NHP (or neural sequential models in
>> general) to real-world domains that involve many event types.  We begin
>> with a temporal deductive database that tracks how relevant facts including
>> the possible event types change over time. We take the system state to be a
>> collection of vector-space embeddings of these facts, and derive a deep
>> recurrent dynamic neural architecture from the temporal Datalog program
>> that specifies the temporal database.  We call this method ``neural Datalog
>> through time.''
>>
>> I'll also sketch a few future research directions, including embedding
>> the NHP model within a reinforcement learner to discover causal structure
>> and learn intervention policies that can improve future outcomes.
>>
>> This work was done with Jason Eisner and other collaborators including
>> Guanghui Qin, Tom Wan, and Minjie Xu.
>>
>> *Bio:* Hongyuan Mei is a Ph.D. student in the Department of Computer
>> Science at the Johns Hopkins University (JHU), affiliated with the Center
>> for Language and Speech Processing (CLSP). He is a Bloomberg Data Science
>> PhD Fellow and the 2020 recipient of the Frederick Jelinek Fellowship.  He
>> develops machine learning methods for real-world problems -- especially
>> probabilistic models for both structured and unstructured data, along with
>> efficient algorithms for training and inference, with applications in event
>> sequence modeling and natural language processing. His papers have appeared
>> at NeurIPS, ICML, NAACL, and AAAI.
>>
>>
>> *Host: *Matthew Walter <mwalter at ttic.edu>
>>
>>
>> Mary C. Marre
>> Faculty Administrative Support
>> *Toyota Technological Institute*
>> *6045 S. Kenwood Avenue*
>> *Room 517*
>> *Chicago, IL  60637*
>> *p:(773) 834-1757*
>> *f: (773) 357-6970*
>> *mmarre at ttic.edu <mmarre at ttic.edu>*
>>
>>
>> On Thu, Feb 18, 2021 at 9:03 PM Mary Marre <mmarre at ttic.edu> wrote:
>>
>>> *When:*      Wednesday, February 24th at* 11:10 am CT*
>>>
>>>
>>>
>>> *Where:*     Zoom Virtual Talk (*register in advance here
>>> <https://uchicagogroup.zoom.us/webinar/register/WN_qJjjpBu2QTeaVuNFFUuANA>*
>>> )
>>>
>>>
>>> *Who: *       Hongyuan Mei, Johns Hopkins University
>>>
>>>
>>> *Title: * Probabilistic Modeling for Event Sequences
>>>
>>> *Abstract:* Suppose we are monitoring discrete events in real time.
>>> Can we predict what events will occur in the future, and when? For example,
>>> can we probabilistically predict a patient's prognosis, eventual diagnosis,
>>> and treatment cost based on their symptoms and treatments so far? What will
>>> an online customer buy in future? What will a social media user share,
>>> like, or comment on? What workload will a computer system receive over the
>>> next 5 minutes?
>>>
>>> This talk will present the neural Hawkes process (NHP), a flexible
>>> probabilistic model that supports such reasoning.  I will sketch methods
>>> for estimating its parameters (via MLE and NCE), sampling predictions of
>>> the future (via rejection sampling), and imputing past events that we have
>>> missed (via particle smoothing).
>>>
>>> I'll then show how to scale the NHP (or neural sequential models in
>>> general) to real-world domains that involve many event types.  We begin
>>> with a temporal deductive database that tracks how relevant facts including
>>> the possible event types change over time. We take the system state to be a
>>> collection of vector-space embeddings of these facts, and derive a deep
>>> recurrent dynamic neural architecture from the temporal Datalog program
>>> that specifies the temporal database.  We call this method ``neural Datalog
>>> through time.''
>>>
>>> I'll also sketch a few future research directions, including embedding
>>> the NHP model within a reinforcement learner to discover causal structure
>>> and learn intervention policies that can improve future outcomes.
>>>
>>> This work was done with Jason Eisner and other collaborators including
>>> Guanghui Qin, Tom Wan, and Minjie Xu.
>>>
>>> *Bio:* Hongyuan Mei is a Ph.D. student in the Department of Computer
>>> Science at the Johns Hopkins University (JHU), affiliated with the Center
>>> for Language and Speech Processing (CLSP). He is a Bloomberg Data Science
>>> PhD Fellow and the 2020 recipient of the Frederick Jelinek Fellowship.  He
>>> develops machine learning methods for real-world problems -- especially
>>> probabilistic models for both structured and unstructured data, along with
>>> efficient algorithms for training and inference, with applications in event
>>> sequence modeling and natural language processing. His papers have appeared
>>> at NeurIPS, ICML, NAACL, and AAAI.
>>>
>>>
>>> *Host: *Matthew Walter <mwalter at ttic.edu>
>>>
>>>
>>>
>>>
>>> Mary C. Marre
>>> Faculty Administrative Support
>>> *Toyota Technological Institute*
>>> *6045 S. Kenwood Avenue*
>>> *Room 517*
>>> *Chicago, IL  60637*
>>> *p:(773) 834-1757*
>>> *f: (773) 357-6970*
>>> *mmarre at ttic.edu <mmarre at ttic.edu>*
>>>
>>
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