Search Results for author: Manuela Veloso

Found 70 papers, 11 papers with code

Six Levels of Privacy: A Framework for Financial Synthetic Data

no code implementations20 Mar 2024 Tucker Balch, Vamsi K. Potluru, Deepak Paramanand, Manuela Veloso

In addition to the benefits it provides, such as improved financial modeling and better testing procedures, it poses privacy risks as well.

Synthetic Data Generation

Intelligent Execution through Plan Analysis

no code implementations18 Mar 2024 Daniel Borrajo, Manuela Veloso

Intelligent robots need to generate and execute plans.

REFRESH: Responsible and Efficient Feature Reselection Guided by SHAP Values

no code implementations13 Mar 2024 Shubham Sharma, Sanghamitra Dutta, Emanuele Albini, Freddy Lecue, Daniele Magazzeni, Manuela Veloso

In this paper, we introduce the problem of feature \emph{reselection}, so that features can be selected with respect to secondary model performance characteristics efficiently even after a feature selection process has been done with respect to a primary objective.

Fairness feature selection

On Computing Plans with Uniform Action Costs

no code implementations15 Feb 2024 Alberto Pozanco, Daniel Borrajo, Manuela Veloso

In many real-world planning applications, agents might be interested in finding plans whose actions have costs that are as uniform as possible.

Fair Coresets via Optimal Transport

no code implementations9 Nov 2023 Zikai Xiong, Niccolò Dalmasso, Shubham Sharma, Freddy Lecue, Daniele Magazzeni, Vamsi K. Potluru, Tucker Balch, Manuela Veloso

In this work, we present fair Wasserstein coresets (FWC), a novel coreset approach which generates fair synthetic representative samples along with sample-level weights to be used in downstream learning tasks.

Clustering Decision Making +1

FairWASP: Fast and Optimal Fair Wasserstein Pre-processing

no code implementations31 Oct 2023 Zikai Xiong, Niccolò Dalmasso, Alan Mishler, Vamsi K. Potluru, Tucker Balch, Manuela Veloso

FairWASP can therefore be used to construct datasets which can be fed into any classification method, not just methods which accept sample weights.


XSkill: Cross Embodiment Skill Discovery

1 code implementation19 Jul 2023 Mengda Xu, Zhenjia Xu, Cheng Chi, Manuela Veloso, Shuran Song

Human demonstration videos are a widely available data source for robot learning and an intuitive user interface for expressing desired behavior.

Imitation Learning Robot Manipulation

Accelerating Cutting-Plane Algorithms via Reinforcement Learning Surrogates

no code implementations17 Jul 2023 Kyle Mana, Fernando Acero, Stephen Mak, Parisa Zehtabi, Michael Cashmore, Daniele Magazzeni, Manuela Veloso

Discrete optimization belongs to the set of $\mathcal{NP}$-hard problems, spanning fields such as mixed-integer programming and combinatorial optimization.

Combinatorial Optimization Management +2

Differentially Private Synthetic Data Using KD-Trees

no code implementations19 Jun 2023 Eleonora Kreačić, Navid Nouri, Vamsi K. Potluru, Tucker Balch, Manuela Veloso

Creation of a synthetic dataset that faithfully represents the data distribution and simultaneously preserves privacy is a major research challenge.

Synthetic Data Generation

Financial Time Series Forecasting using CNN and Transformer

no code implementations11 Apr 2023 Zhen Zeng, Rachneet Kaur, Suchetha Siddagangappa, Saba Rahimi, Tucker Balch, Manuela Veloso

In our experiments, we demonstrated the success of the proposed method in comparison to commonly adopted statistical and deep learning methods on forecasting intraday stock price change of S&P 500 constituents.

Decision Making Time Series +1

Fast Learning of Multidimensional Hawkes Processes via Frank-Wolfe

no code implementations12 Dec 2022 Renbo Zhao, Niccolò Dalmasso, Mohsen Ghassemi, Vamsi K. Potluru, Tucker Balch, Manuela Veloso

Hawkes processes have recently risen to the forefront of tools when it comes to modeling and generating sequential events data.


Learn to explain yourself, when you can: Equipping Concept Bottleneck Models with the ability to abstain on their concept predictions

no code implementations21 Nov 2022 Joshua Lockhart, Daniele Magazzeni, Manuela Veloso

The Concept Bottleneck Models (CBMs) of Koh et al. [2020] provide a means to ensure that a neural network based classifier bases its predictions solely on human understandable concepts.

Towards learning to explain with concept bottleneck models: mitigating information leakage

no code implementations7 Nov 2022 Joshua Lockhart, Nicolas Marchesotti, Daniele Magazzeni, Manuela Veloso

Concept bottleneck models perform classification by first predicting which of a list of human provided concepts are true about a datapoint.

ASPiRe:Adaptive Skill Priors for Reinforcement Learning

no code implementations30 Sep 2022 Mengda Xu, Manuela Veloso, Shuran Song

We introduce ASPiRe (Adaptive Skill Prior for RL), a new approach that leverages prior experience to accelerate reinforcement learning.

reinforcement-learning Reinforcement Learning (RL)

Online Learning for Mixture of Multivariate Hawkes Processes

no code implementations16 Aug 2022 Mohsen Ghassemi, Niccolò Dalmasso, Simran Lamba, Vamsi K. Potluru, Sameena Shah, Tucker Balch, Manuela Veloso

Online learning of Hawkes processes has received increasing attention in the last couple of years especially for modeling a network of actors.

Differentially Private Learning of Hawkes Processes

no code implementations27 Jul 2022 Mohsen Ghassemi, Eleonora Kreačić, Niccolò Dalmasso, Vamsi K. Potluru, Tucker Balch, Manuela Veloso

Hawkes processes have recently gained increasing attention from the machine learning community for their versatility in modeling event sequence data.

A Survey of Explainable Reinforcement Learning

no code implementations17 Feb 2022 Stephanie Milani, Nicholay Topin, Manuela Veloso, Fei Fang

In this survey, we propose a novel taxonomy for organizing the XRL literature that prioritizes the RL setting.

Decision Making reinforcement-learning +1

Structure and Semantics Preserving Document Representations

no code implementations11 Jan 2022 Natraj Raman, Sameena Shah, Manuela Veloso

Retrieving relevant documents from a corpus is typically based on the semantic similarity between the document content and query text.

Metric Learning Retrieval +2

Synthetic Document Generator for Annotation-free Layout Recognition

no code implementations11 Nov 2021 Natraj Raman, Sameena Shah, Manuela Veloso

Analyzing the layout of a document to identify headers, sections, tables, figures etc.

Parameterized Explanations for Investor / Company Matching

no code implementations27 Oct 2021 Simerjot Kaur, Ivan Brugere, Andrea Stefanucci, Armineh Nourbakhsh, Sameena Shah, Manuela Veloso

We compare the performance of our system with human generated recommendations and demonstrate the ability of our algorithm to perform extremely well on this task.

Decision Making Explainable Recommendation +2

How Robust are Limit Order Book Representations under Data Perturbation?

1 code implementation10 Oct 2021 Yufei Wu, Mahmoud Mahfouz, Daniele Magazzeni, Manuela Veloso

The success of machine learning models in the financial domain is highly reliant on the quality of the data representation.

Towards Robust Representation of Limit Orders Books for Deep Learning Models

no code implementations10 Oct 2021 Yufei Wu, Mahmoud Mahfouz, Daniele Magazzeni, Manuela Veloso

The success of deep learning-based limit order book forecasting models is highly dependent on the quality and the robustness of the input data representation.

BIG-bench Machine Learning

Tradeoffs in Streaming Binary Classification under Limited Inspection Resources

no code implementations5 Oct 2021 Parisa Hassanzadeh, Danial Dervovic, Samuel Assefa, Prashant Reddy, Manuela Veloso

Institutions are increasingly relying on machine learning models to identify and alert on abnormal events, such as fraud, cyber attacks and system failures.

Binary Classification Classification +1

Learning to Classify and Imitate Trading Agents in Continuous Double Auction Markets

no code implementations4 Oct 2021 Mahmoud Mahfouz, Tucker Balch, Manuela Veloso, Danilo Mandic

Continuous double auctions such as the limit order book employed by exchanges are widely used in practice to match buyers and sellers of a variety of financial instruments.

Behavioural cloning

Advising Agent for Service-Providing Live-Chat Operators

no code implementations9 May 2021 Aviram Aviv, Yaniv Oshrat, Samuel A. Assefa, Tobi Mustapha, Daniel Borrajo, Manuela Veloso, Sarit Kraus

Call centers, in which human operators attend clients using textual chat, are very common in modern e-commerce.

Iterative Bounding MDPs: Learning Interpretable Policies via Non-Interpretable Methods

no code implementations25 Feb 2021 Nicholay Topin, Stephanie Milani, Fei Fang, Manuela Veloso

Because of this decision tree equivalence, any function approximator can be used during training, including a neural network, while yielding a decision tree policy for the base MDP.

reinforcement-learning Reinforcement Learning (RL)

Deep Video Prediction for Time Series Forecasting

no code implementations24 Feb 2021 Zhen Zeng, Tucker Balch, Manuela Veloso

In this paper, we propose to approach economic time series forecasting of multiple financial assets in a novel way via video prediction.

Decision Making Time Series +2

Visual Time Series Forecasting: An Image-driven Approach

no code implementations18 Nov 2020 Srijan Sood, Zhen Zeng, Naftali Cohen, Tucker Balch, Manuela Veloso

In this work, we leverage advances in deep learning to extend the field of time series forecasting to a visual setting.

Quantization Time Series +1

Simulating and classifying behavior in adversarial environments based on action-state traces: an application to money laundering

no code implementations3 Nov 2020 Daniel Borrajo, Manuela Veloso, Sameena Shah

One of the key characteristics of these applications is the wide range of strategies that an adversary may choose as they adapt their strategy dynamically to sustain benefits and evade authorities.

Robust Document Representations using Latent Topics and Metadata

no code implementations23 Oct 2020 Natraj Raman, Armineh Nourbakhsh, Sameena Shah, Manuela Veloso

Task specific fine-tuning of a pre-trained neural language model using a custom softmax output layer is the de facto approach of late when dealing with document classification problems.

Document Classification Language Modelling

SURF: Improving classifiers in production by learning from busy and noisy end users

no code implementations12 Oct 2020 Joshua Lockhart, Samuel Assefa, Ayham Alajdad, Andrew Alexander, Tucker Balch, Manuela Veloso

We show that conventional crowdsourcing algorithms struggle in this user feedback setting, and present a new algorithm, SURF, that can cope with this non-response ambiguity.

Paying down metadata debt: learning the representation of concepts using topic models

no code implementations9 Oct 2020 Jiahao Chen, Manuela Veloso

We introduce a data management problem called metadata debt, to identify the mapping between data concepts and their logical representations.

Management Topic Models

DocuBot : Generating financial reports using natural language interactions

no code implementations2 Oct 2020 Vineeth Ravi, Selim Amrouni, Andrea Stefanucci, Armineh Nourbakhsh, Prashant Reddy, Manuela Veloso

Digital reports are often created based on tedious manual analysis as well as visualization of the underlying trends and characteristics of data.

Calibration of Shared Equilibria in General Sum Partially Observable Markov Games

no code implementations NeurIPS 2020 Nelson Vadori, Sumitra Ganesh, Prashant Reddy, Manuela Veloso

Training multi-agent systems (MAS) to achieve realistic equilibria gives us a useful tool to understand and model real-world systems.

Guaranteeing Reproducibility in Deep Learning Competitions

no code implementations12 May 2020 Brandon Houghton, Stephanie Milani, Nicholay Topin, William Guss, Katja Hofmann, Diego Perez-Liebana, Manuela Veloso, Ruslan Salakhutdinov

To encourage the development of methods with reproducible and robust training behavior, we propose a challenge paradigm where competitors are evaluated directly on the performance of their learning procedures rather than pre-trained agents.

Some people aren't worth listening to: periodically retraining classifiers with feedback from a team of end users

no code implementations27 Apr 2020 Joshua Lockhart, Samuel Assefa, Tucker Balch, Manuela Veloso

Document classification is ubiquitous in a business setting, but often the end users of a classifier are engaged in an ongoing feedback-retrain loop with the team that maintain it.

Document Classification

Bayesian Consensus: Consensus Estimates from Miscalibrated Instruments under Heteroscedastic Noise

no code implementations14 Apr 2020 Chirag Nagpal, Robert E. Tillman, Prashant Reddy, Manuela Veloso

We consider the problem of aggregating predictions or measurements from a set of human forecasters, models, sensors or other instruments which may be subject to bias or miscalibration and random heteroscedastic noise.

Bayesian Inference

Heuristics for Link Prediction in Multiplex Networks

no code implementations9 Apr 2020 Robert E. Tillman, Vamsi K. Potluru, Jiahao Chen, Prashant Reddy, Manuela Veloso

Through experiments with simulated and real world scientific collaboration, transportation and global trade networks, we demonstrate that the proposed heuristics show increased performance with the richness of connection type correlation structure and significantly outperform their baseline heuristics for ordinary networks with a single connection type.

Link Prediction Vocal Bursts Type Prediction

Get Real: Realism Metrics for Robust Limit Order Book Market Simulations

no code implementations10 Dec 2019 Svitlana Vyetrenko, David Byrd, Nick Petosa, Mahmoud Mahfouz, Danial Dervovic, Manuela Veloso, Tucker Hybinette Balch

Machine learning (especially reinforcement learning) methods for trading are increasingly reliant on simulation for agent training and testing.

On the Importance of Opponent Modeling in Auction Markets

no code implementations28 Nov 2019 Mahmoud Mahfouz, Angelos Filos, Cyrine Chtourou, Joshua Lockhart, Samuel Assefa, Manuela Veloso, Danilo Mandic, Tucker Balch

The dynamics of financial markets are driven by the interactions between participants, as well as the trading mechanisms and regulatory frameworks that govern these interactions.

Playing Games in the Dark: An approach for cross-modality transfer in reinforcement learning

1 code implementation28 Nov 2019 Rui Silva, Miguel Vasco, Francisco S. Melo, Ana Paiva, Manuela Veloso

In this work we explore the use of latent representations obtained from multiple input sensory modalities (such as images or sounds) in allowing an agent to learn and exploit policies over different subsets of input modalities.

OpenAI Gym reinforcement-learning +1

Reinforcement Learning for Market Making in a Multi-agent Dealer Market

1 code implementation14 Nov 2019 Sumitra Ganesh, Nelson Vadori, Mengda Xu, Hua Zheng, Prashant Reddy, Manuela Veloso

Market makers play an important role in providing liquidity to markets by continuously quoting prices at which they are willing to buy and sell, and managing inventory risk.

reinforcement-learning Reinforcement Learning (RL)

Leveraging Multimodal Haptic Sensory Data for Robust Cutting

no code implementations27 Sep 2019 Kevin Zhang, Mohit Sharma, Manuela Veloso, Oliver Kroemer

In this paper, we propose using vibrations and force-torque feedback from the interactions to adapt the slicing motions and monitor for contact events.

Trading via Image Classification

1 code implementation23 Jul 2019 Naftali Cohen, Tucker Balch, Manuela Veloso

The art of systematic financial trading evolved with an array of approaches, ranging from simple strategies to complex algorithms all relying, primary, on aspects of time-series analysis.

Classification General Classification +4

The Effect of Visual Design in Image Classification

no code implementations22 Jul 2019 Naftali Cohen, Tucker Balch, Manuela Veloso

In this study, we examine whether binary decisions are better to be decided based on the numeric or the visual representation of the same data.

Classification Feature Engineering +2

Conservative Q-Improvement: Reinforcement Learning for an Interpretable Decision-Tree Policy

1 code implementation2 Jul 2019 Aaron M. Roth, Nicholay Topin, Pooyan Jamshidi, Manuela Veloso

There is a growing desire in the field of reinforcement learning (and machine learning in general) to move from black-box models toward more "interpretable AI."

reinforcement-learning Reinforcement Learning (RL)

Generation of Policy-Level Explanations for Reinforcement Learning

no code implementations28 May 2019 Nicholay Topin, Manuela Veloso

Though reinforcement learning has greatly benefited from the incorporation of neural networks, the inability to verify the correctness of such systems limits their use.

reinforcement-learning Reinforcement Learning (RL)

The MineRL 2019 Competition on Sample Efficient Reinforcement Learning using Human Priors

1 code implementation22 Apr 2019 William H. Guss, Cayden Codel, Katja Hofmann, Brandon Houghton, Noboru Kuno, Stephanie Milani, Sharada Mohanty, Diego Perez Liebana, Ruslan Salakhutdinov, Nicholay Topin, Manuela Veloso, Phillip Wang

To that end, we introduce: (1) the Minecraft ObtainDiamond task, a sequential decision making environment requiring long-term planning, hierarchical control, and efficient exploration methods; and (2) the MineRL-v0 dataset, a large-scale collection of over 60 million state-action pairs of human demonstrations that can be resimulated into embodied trajectories with arbitrary modifications to game state and visuals.

Decision Making Efficient Exploration +2

The Impact of Humanoid Affect Expression on Human Behavior in a Game-Theoretic Setting

1 code implementation10 Jun 2018 Aaron M. Roth, Umang Bhatt, Tamara Amin, Afsaneh Doryab, Fei Fang, Manuela Veloso

In this pilot study, we investigate (1) in what way a robot can express a certain mood to influence a human's decision making behavioral model; (2) how and to what extent the human will be influenced in a game theoretic setting.

Decision Making

Understanding Convolutional Networks with APPLE : Automatic Patch Pattern Labeling for Explanation

no code implementations11 Feb 2018 Sandeep Konam, Ian Quah, Stephanie Rosenthal, Manuela Veloso

With the success of deep learning, recent efforts have been focused on analyzing how learned networks make their classifications.

Classification General Classification

What Can This Robot Do? Learning from Appearance and Experiments

no code implementations15 Dec 2017 Ashwin Khadke, Manuela Veloso

We present an approach to make the learner build a model of the subject at a task based on the latter's appearance and refine it by experimentation.

UAV and Service Robot Coordination for Indoor Object Search Tasks

no code implementations26 Sep 2017 Sandeep Konam, Stephanie Rosenthal, Manuela Veloso

In this paper, we propose the concept of coordination between CoBot and the Parrot ARDrone 2. 0 to perform service-based object search tasks, in which CoBot localizes and navigates to the general search areas carrying the ARDrone and the ARDrone searches locally for objects.


Interactive Policy Learning through Confidence-Based Autonomy

no code implementations15 Jan 2014 Sonia Chernova, Manuela Veloso

We present Confidence-Based Autonomy (CBA), an interactive algorithm for policy learning from demonstration.

Trajectory-Based Short-Sighted Probabilistic Planning

no code implementations NeurIPS 2012 Felipe Trevizan, Manuela Veloso

In order to compute a solution for a probabilistic planning problem, planners need to manage the uncertainty associated with the different paths from the initial state to a goal state.

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