Search Results for author: Guillermo Cecchi

Found 20 papers, 9 papers with code

Neural Topic Modeling of Psychotherapy Sessions

no code implementations13 Apr 2022 Baihan Lin, Djallel Bouneffouf, Guillermo Cecchi, Ravi Tejwani

In this work, we compare different neural topic modeling methods in learning the topical propensities of different psychiatric conditions from the psychotherapy session transcripts parsed from speech recordings.

Time Series

Deep Annotation of Therapeutic Working Alliance in Psychotherapy

no code implementations12 Apr 2022 Baihan Lin, Guillermo Cecchi, Djallel Bouneffouf

The therapeutic working alliance is an important predictor of the outcome of the psychotherapy treatment.

Predicting human decision making in psychological tasks with recurrent neural networks

1 code implementation22 Oct 2020 Baihan Lin, Djallel Bouneffouf, Guillermo Cecchi

Unlike traditional time series, the action sequences of human decision making usually involve many cognitive processes such as beliefs, desires, intentions, and theory of mind, i. e., what others are thinking.

Decision Making Time Series

Online Learning in Iterated Prisoner's Dilemma to Mimic Human Behavior

1 code implementation9 Jun 2020 Baihan Lin, Djallel Bouneffouf, Guillermo Cecchi

We propose to study online learning algorithm behavior in the Iterated Prisoner's Dilemma (IPD) game, where we explored the full spectrum of reinforcement learning agents: multi-armed bandits, contextual bandits and reinforcement learning.

Multi-Armed Bandits online learning +1

Unified Models of Human Behavioral Agents in Bandits, Contextual Bandits and RL

1 code implementation10 May 2020 Baihan Lin, Guillermo Cecchi, Djallel Bouneffouf, Jenna Reinen, Irina Rish

Artificial behavioral agents are often evaluated based on their consistent behaviors and performance to take sequential actions in an environment to maximize some notion of cumulative reward.

Decision Making Multi-Armed Bandits

Nearly-Unsupervised Hashcode Representations for Biomedical Relation Extraction

no code implementations IJCNLP 2019 Sahil Garg, Aram Galstyan, Greg Ver Steeg, Guillermo Cecchi

Recently, kernelized locality sensitive hashcodes have been successfully employed as representations of natural language text, especially showing high relevance to biomedical relation extraction tasks.

Relation Extraction

Reinforcement Learning Models of Human Behavior: Reward Processing in Mental Disorders

no code implementations NeurIPS Workshop Neuro_AI 2019 Baihan Lin, Guillermo Cecchi, Djallel Bouneffouf, Jenna Reinen, Irina Rish

Drawing an inspiration from behavioral studies of human decision making, we propose here a general parametric framework for a reinforcement learning problem, which extends the standard Q-learning approach to incorporate a two-stream framework of reward processing with biases biologically associated with several neurological and psychiatric conditions, including Parkinson's and Alzheimer's diseases, attention-deficit/hyperactivity disorder (ADHD), addiction, and chronic pain.

Decision Making Q-Learning +2

Nearly-Unsupervised Hashcode Representations for Relation Extraction

no code implementations9 Sep 2019 Sahil Garg, Aram Galstyan, Greg Ver Steeg, Guillermo Cecchi

Recently, kernelized locality sensitive hashcodes have been successfully employed as representations of natural language text, especially showing high relevance to biomedical relation extraction tasks.

Relation Extraction

Split Q Learning: Reinforcement Learning with Two-Stream Rewards

1 code implementation21 Jun 2019 Baihan Lin, Djallel Bouneffouf, Guillermo Cecchi

Drawing an inspiration from behavioral studies of human decision making, we propose here a general parametric framework for a reinforcement learning problem, which extends the standard Q-learning approach to incorporate a two-stream framework of reward processing with biases biologically associated with several neurological and psychiatric conditions, including Parkinson's and Alzheimer's diseases, attention-deficit/hyperactivity disorder (ADHD), addiction, and chronic pain.

Decision Making Q-Learning +2

A Story of Two Streams: Reinforcement Learning Models from Human Behavior and Neuropsychiatry

1 code implementation21 Jun 2019 Baihan Lin, Guillermo Cecchi, Djallel Bouneffouf, Jenna Reinen, Irina Rish

Drawing an inspiration from behavioral studies of human decision making, we propose here a more general and flexible parametric framework for reinforcement learning that extends standard Q-learning to a two-stream model for processing positive and negative rewards, and allows to incorporate a wide range of reward-processing biases -- an important component of human decision making which can help us better understand a wide spectrum of multi-agent interactions in complex real-world socioeconomic systems, as well as various neuropsychiatric conditions associated with disruptions in normal reward processing.

Decision Making Q-Learning +2

Modeling Psychotherapy Dialogues with Kernelized Hashcode Representations: A Nonparametric Information-Theoretic Approach

no code implementations26 Apr 2018 Sahil Garg, Irina Rish, Guillermo Cecchi, Palash Goyal, Sarik Ghazarian, Shuyang Gao, Greg Ver Steeg, Aram Galstyan

We also derive a novel lower bound on mutual information, used as a model-selection criterion favoring representations with better alignment between the utterances of participants in a collaborative dialogue setting, as well as higher predictability of the generated responses.

Dialogue Generation Model Selection

Contextual Bandit with Adaptive Feature Extraction

1 code implementation3 Feb 2018 Baihan Lin, Djallel Bouneffouf, Guillermo Cecchi, Irina Rish

Our experiments on a variety of datasets, and both in stationary and non-stationary environments of several kinds demonstrate clear advantages of the proposed adaptive representation learning over the standard contextual bandit based on "raw" input contexts.

Decision Making Online Clustering +1

Kernelized Hashcode Representations for Relation Extraction

1 code implementation10 Nov 2017 Sahil Garg, Aram Galstyan, Greg Ver Steeg, Irina Rish, Guillermo Cecchi, Shuyang Gao

Here we propose to use random subspaces of KLSH codes for efficiently constructing an explicit representation of NLP structures suitable for general classification methods.

General Classification Relation Extraction

Using Automated Metaphor Identification to Aid in Detection and Prediction of First-Episode Schizophrenia

no code implementations EMNLP 2017 E. Dar{\'\i}o Guti{\'e}rrez, Guillermo Cecchi, Cheryl Corcoran, Philip Corlett

The diagnosis of serious mental health conditions such as schizophrenia is based on the judgment of clinicians whose training takes several years, and cannot be easily formalized into objective measures.

Sentiment Analysis

Efficient Data Representation by Selecting Prototypes with Importance Weights

3 code implementations5 Jul 2017 Karthik S. Gurumoorthy, Amit Dhurandhar, Guillermo Cecchi, Charu Aggarwal

Prototypical examples that best summarizes and compactly represents an underlying complex data distribution communicate meaningful insights to humans in domains where simple explanations are hard to extract.

Neurogenesis-Inspired Dictionary Learning: Online Model Adaption in a Changing World

1 code implementation22 Jan 2017 Sahil Garg, Irina Rish, Guillermo Cecchi, Aurelie Lozano

In this paper, we focus on online representation learning in non-stationary environments which may require continuous adaptation of model architecture.

Dictionary Learning Hippocampus +3

The ontogeny of discourse structure mimics the development of literature

no code implementations27 Dec 2016 Natalia Bezerra Mota, Sylvia Pinheiro, Mariano Sigman, Diego Fernandez Slezak, Guillermo Cecchi, Mauro Copelli, Sidarta Ribeiro

In literature, monotonic asymptotic changes over time were remarkable: While lexical diversity, long-range recurrence and graph size increased away from near-randomness, short-range recurrence declined, from above to below random levels.

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