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08/20/2023
LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models
The advent of large language models (LLMs) and their adoption by the leg...
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08/16/2023
Freedom of Speech and AI Output
Is the output of generative AI entitled to First Amendment protection? W...
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08/09/2023
Where's the Liability in Harmful AI Speech?
Generative AI, in particular text-based "foundation models" (large model...
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05/03/2023
Cheaply Evaluating Inference Efficiency Metrics for Autoregressive Transformer APIs
Large language models (LLMs) power many state-of-the-art systems in natu...
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03/28/2023
Foundation Models and Fair Use
Existing foundation models are trained on copyrighted material. Deployin...
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11/27/2022
Self-Destructing Models: Increasing the Costs of Harmful Dual Uses in Foundation Models
A growing ecosystem of large, open-source foundation models has reduced ...
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11/16/2022
Holistic Evaluation of Language Models
Language models (LMs) are becoming the foundation for almost all major l...
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10/04/2022
Text Characterization Toolkit
In NLP, models are usually evaluated by reporting single-number performa...
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08/24/2022
Entropy Regularization for Population Estimation
Entropy regularization is known to improve exploration in sequential dec...
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07/01/2022
Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open-Source Legal Dataset
One concern with the rise of large language models lies with their poten...
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05/04/2022
Data Governance in the Age of Large-Scale Data-Driven Language Technology
The recent emergence and adoption of Machine Learning technology, and sp...
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04/25/2022
Integrating Reward Maximization and Population Estimation: Sequential Decision-Making for Internal Revenue Service Audit Selection
We introduce a new setting, optimize-and-estimate structured bandits. He...
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12/13/2021
Beyond Ads: Sequential Decision-Making Algorithms in Public Policy
We explore the promises and challenges of employing sequential decision-...
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04/18/2021
When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset
While self-supervised learning has made rapid advances in natural langua...
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03/10/2021
An Information-Theoretic Perspective on Credit Assignment in Reinforcement Learning
How do we formalize the challenge of credit assignment in reinforcement ...
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10/13/2020
With Little Power Comes Great Responsibility
Despite its importance to experimental design, statistical power (the pr...
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07/21/2020
Ideas for Improving the Field of Machine Learning: Summarizing Discussion from the NeurIPS 2019 Retrospectives Workshop
This report documents ideas for improving the field of machine learning,...
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07/06/2020
TDprop: Does Jacobi Preconditioning Help Temporal Difference Learning?
We investigate whether Jacobi preconditioning, accounting for the bootst...
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04/15/2020
Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims
With the recent wave of progress in artificial intelligence (AI) has com...
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01/31/2020
Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning
Accurate reporting of energy and carbon usage is essential for understan...
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02/05/2019
Separating value functions across time-scales
In many finite horizon episodic reinforcement learning (RL) settings, it...
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12/03/2018
Distilling Information from a Flood: A Possibility for the Use of Meta-Analysis and Systematic Review in Machine Learning Research
The current flood of information in all areas of machine learning resear...
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11/30/2018
An Introduction to Deep Reinforcement Learning
Deep reinforcement learning is the combination of reinforcement learning...
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11/07/2018
The RLLChatbot: a solution to the ConvAI Challenge
Current conversational systems can follow simple commands and answer bas...
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11/04/2018
Adversarial Gain
Adversarial examples can be defined as inputs to a model which induce a ...
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10/05/2018
Where Did My Optimum Go?: An Empirical Analysis of Gradient Descent Optimization in Policy Gradient Methods
Recent analyses of certain gradient descent optimization methods have sh...
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05/09/2018
Reward Estimation for Variance Reduction in Deep Reinforcement Learning
In reinforcement learning (RL), stochastic environments can make learnin...
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12/11/2017
Learning Robust Dialog Policies in Noisy Environments
Modern virtual personal assistants provide a convenient interface for co...
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11/24/2017
Ethical Challenges in Data-Driven Dialogue Systems
The use of dialogue systems as a medium for human-machine interaction is...
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09/25/2017
Underwater Multi-Robot Convoying using Visual Tracking by Detection
We present a robust multi-robot convoying approach that relies on visual...
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09/21/2017
Cost Adaptation for Robust Decentralized Swarm Behaviour
The multi-agent swarm system is a robust paradigm which can drive effici...
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09/19/2017
Deep Reinforcement Learning that Matters
In recent years, significant progress has been made in solving challengi...
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08/14/2017
Benchmark Environments for Multitask Learning in Continuous Domains
As demand drives systems to generalize to various domains and problems, ...
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12/17/2015