no code implementations • NAACL (DaSH) 2021 • Natraj Raman, Sameena Shah, Tucker Balch, Manuela Veloso
Information visualization is critical to analytical reasoning and knowledge discovery.
no code implementations • 29 May 2024 • Tom Bewley, Salim I. Amoukou, Saumitra Mishra, Daniele Magazzeni, Manuela Veloso
We introduce T-CREx, a novel model-agnostic method for local and global counterfactual explanation (CE), which summarises recourse options for both individuals and groups in the form of human-readable rules.
no code implementations • 25 Mar 2024 • Fernando Acero, Parisa Zehtabi, Nicolas Marchesotti, Michael Cashmore, Daniele Magazzeni, Manuela Veloso
Portfolio optimization involves determining the optimal allocation of portfolio assets in order to maximize a given investment objective.
no code implementations • 20 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.
no code implementations • 18 Mar 2024 • Daniel Borrajo, Manuela Veloso
Intelligent robots need to generate and execute plans.
no code implementations • 17 Mar 2024 • Zhen Zeng, William Watson, Nicole Cho, Saba Rahimi, Shayleen Reynolds, Tucker Balch, Manuela Veloso
FlowMind further simplifies user interaction by presenting high-level descriptions of auto-generated workflows, enabling users to inspect and provide feedback effectively.
no code implementations • 17 Mar 2024 • Zhen Zeng, Rachneet Kaur, Suchetha Siddagangappa, Tucker Balch, Manuela Veloso
Time series forecasting plays a crucial role in decision-making across various domains, but it presents significant challenges.
no code implementations • 13 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.
no code implementations • 15 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.
no code implementations • 29 Dec 2023 • Vamsi K. Potluru, Daniel Borrajo, Andrea Coletta, Niccolò Dalmasso, Yousef El-Laham, Elizabeth Fons, Mohsen Ghassemi, Sriram Gopalakrishnan, Vikesh Gosai, Eleonora Kreačić, Ganapathy Mani, Saheed Obitayo, Deepak Paramanand, Natraj Raman, Mikhail Solonin, Srijan Sood, Svitlana Vyetrenko, Haibei Zhu, Manuela Veloso, Tucker Balch
Synthetic data has made tremendous strides in various commercial settings including finance, healthcare, and virtual reality.
no code implementations • 9 Nov 2023 • Zikai Xiong, Niccolò Dalmasso, Shubham Sharma, Freddy Lecue, Daniele Magazzeni, Vamsi K. Potluru, Tucker Balch, Manuela Veloso
Data distillation and coresets have emerged as popular approaches to generate a smaller representative set of samples for downstream learning tasks to handle large-scale datasets.
no code implementations • 31 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.
no code implementations • 28 Sep 2023 • Tom Bamford, Andrea Coletta, Elizabeth Fons, Sriram Gopalakrishnan, Svitlana Vyetrenko, Tucker Balch, Manuela Veloso
Moreover, the required storage, computational time, and retrieval complexity to search in the time-series space are often non-trivial.
no code implementations • 22 Aug 2023 • Saba Rahimi, Tucker Balch, Manuela Veloso
The GPT-3 model achieved a 96% passing score on a set of 50 sample driving knowledge test questions.
1 code implementation • 19 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.
no code implementations • 17 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.
no code implementations • 19 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.
no code implementations • 11 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.
no code implementations • 12 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.
no code implementations • 21 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.
no code implementations • 7 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.
no code implementations • 13 Oct 2022 • Nelson Vadori, Leo Ardon, Sumitra Ganesh, Thomas Spooner, Selim Amrouni, Jared Vann, Mengda Xu, Zeyu Zheng, Tucker Balch, Manuela Veloso
We study a game between liquidity provider and liquidity taker agents interacting in an over-the-counter market, for which the typical example is foreign exchange.
Multi-agent Reinforcement Learning reinforcement-learning +1
no code implementations • 30 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.
no code implementations • 16 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.
no code implementations • 27 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.
no code implementations • 17 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.
no code implementations • 11 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.
no code implementations • 6 Dec 2021 • Alexander Lavin, David Krakauer, Hector Zenil, Justin Gottschlich, Tim Mattson, Johann Brehmer, Anima Anandkumar, Sanjay Choudry, Kamil Rocki, Atılım Güneş Baydin, Carina Prunkl, Brooks Paige, Olexandr Isayev, Erik Peterson, Peter L. McMahon, Jakob Macke, Kyle Cranmer, Jiaxin Zhang, Haruko Wainwright, Adi Hanuka, Manuela Veloso, Samuel Assefa, Stephan Zheng, Avi Pfeffer
We present the "Nine Motifs of Simulation Intelligence", a roadmap for the development and integration of the essential algorithms necessary for a merger of scientific computing, scientific simulation, and artificial intelligence.
no code implementations • 11 Nov 2021 • Natraj Raman, Sameena Shah, Manuela Veloso
Analyzing the layout of a document to identify headers, sections, tables, figures etc.
1 code implementation • 27 Oct 2021 • Selim Amrouni, Aymeric Moulin, Jared Vann, Svitlana Vyetrenko, Tucker Balch, Manuela Veloso
We introduce a general technique to wrap a DEMAS simulator into the Gym framework.
no code implementations • 27 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.
no code implementations • 10 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.
1 code implementation • 10 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.
no code implementations • 5 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.
no code implementations • 4 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.
no code implementations • 2 Jul 2021 • Naftali Cohen, Srijan Sood, Zhen Zeng, Tucker Balch, Manuela Veloso
In this work, we address time-series forecasting as a computer vision task.
no code implementations • 9 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.
no code implementations • 25 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.
no code implementations • 24 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.
no code implementations • 18 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.
no code implementations • 3 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.
no code implementations • 3 Nov 2020 • Daniel Borrajo, Manuela Veloso
Financial institutions mostly deal with people.
no code implementations • 23 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.
no code implementations • 12 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.
no code implementations • 9 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.
no code implementations • 2 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.
no code implementations • 23 Jun 2020 • Nelson Vadori, Sumitra Ganesh, Prashant Reddy, Manuela Veloso
We introduce a novel framework to account for sensitivity to rewards uncertainty in sequential decision-making problems.
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.
no code implementations • 12 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.
no code implementations • 27 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.
no code implementations • 14 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.
no code implementations • 9 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.
no code implementations • 10 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.
no code implementations • 28 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.
1 code implementation • 28 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.
1 code implementation • 14 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.
no code implementations • 27 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.
1 code implementation • 29 Jul 2019 • William H. Guss, Brandon Houghton, Nicholay Topin, Phillip Wang, Cayden Codel, Manuela Veloso, Ruslan Salakhutdinov
Therefore, we introduce a comprehensive, large-scale, simulator-paired dataset of human demonstrations: MineRL.
2 code implementations • 23 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.
no code implementations • 22 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.
1 code implementation • 2 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."
no code implementations • 28 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.
1 code implementation • 22 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.
1 code implementation • 10 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.
no code implementations • 11 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.
no code implementations • 15 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.
no code implementations • 26 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.
1 code implementation • 30 Jul 2017 • Benjamin J. Lengerich, Sandeep Konam, Eric P. Xing, Stephanie Rosenthal, Manuela Veloso
The predictive power of neural networks often costs model interpretability.
no code implementations • 30 May 2017 • Guan-Horng Liu, Avinash Siravuru, Sai Prabhakar, Manuela Veloso, George Kantor
Multisensory polices are known to enhance both state estimation and target tracking.
no code implementations • 27 Apr 2017 • Shichao Yang, Sandeep Konam, Chen Ma, Stephanie Rosenthal, Manuela Veloso, Sebastian Scherer
Second, predict the trajectory from the depth and normal.
no code implementations • 15 Jan 2014 • Sonia Chernova, Manuela Veloso
We present Confidence-Based Autonomy (CBA), an interactive algorithm for policy learning from demonstration.
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.