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Found 15 papers, 10 papers with code
AutoBencher: Creating Salient, Novel, Difficult Datasets for Language Models
In this paper, we present three desiderata for a good benchmark for language models: (i) salience (e. g., knowledge about World War II is more salient than a random day in history), (ii) novelty (i. e., the benchmark reveals new trends in model rankings not shown by previous benchmarks), and (iii) difficulty (i. e., the benchmark should be difficult for existing models, leaving headroom for future improvement).
Simple Embodied Language Learning as a Byproduct of Meta-Reinforcement Learning
Specifically, we design an office navigation environment, where the agent's goal is to find a particular office, and office locations differ in different buildings (i. e., tasks).
A Survey of Meta-Reinforcement Learning
Meta-RL is most commonly studied in a problem setting where, given a distribution of tasks, the goal is to learn a policy that is capable of adapting to any new task from the task distribution with as little data as possible.
Learning Options via Compression
Identifying statistical regularities in solutions to some tasks in multi-task reinforcement learning can accelerate the learning of new tasks.
Giving Feedback on Interactive Student Programs with Meta-Exploration
However, teaching and giving feedback on such software is time-consuming -- standard approaches require instructors to manually grade student-implemented interactive programs.
Analyzing a Caching Model
Machine Learning has been successfully applied in systems applications such as memory prefetching and caching, where learned models have been shown to outperform heuristics.
Just Train Twice: Improving Group Robustness without Training Group Information
Standard training via empirical risk minimization (ERM) can produce models that achieve high accuracy on average but low accuracy on certain groups, especially in the presence of spurious correlations between the input and label.
Ranked #1 on
Out-of-Distribution Generalization
on ImageNet-W
Decoupling Exploration and Exploitation for Meta-Reinforcement Learning without Sacrifices
Learning a new task often requires both exploring to gather task-relevant information and exploiting this information to solve the task.
Learning Abstract Models for Strategic Exploration and Fast Reward Transfer
Model-based reinforcement learning (RL) is appealing because (i) it enables planning and thus more strategic exploration, and (ii) by decoupling dynamics from rewards, it enables fast transfer to new reward functions.
An Imitation Learning Approach for Cache Replacement
While directly applying Belady's is infeasible since the future is unknown, we train a policy conditioned only on past accesses that accurately approximates Belady's even on diverse and complex access patterns, and call this approach Parrot.
Explore then Execute: Adapting without Rewards via Factorized Meta-Reinforcement Learning
In principle, meta-reinforcement learning approaches can exploit this shared structure, but in practice, they fail to adapt to new environments when adaptation requires targeted exploration (e. g., exploring the cabinets to find ingredients in a new kitchen).
Learning Abstract Models for Long-Horizon Exploration
In our approach, a manager maintains an abstract MDP over a subset of the abstract states, which grows monotonically through targeted exploration (possible due to the abstract MDP).
Mapping Natural Language Commands to Web Elements
The web provides a rich, open-domain environment with textual, structural, and spatial properties.
Reinforcement Learning on Web Interfaces Using Workflow-Guided Exploration
Reinforcement learning (RL) agents improve through trial-and-error, but when reward is sparse and the agent cannot discover successful action sequences, learning stagnates.
From Language to Programs: Bridging Reinforcement Learning and Maximum Marginal Likelihood
Our goal is to learn a semantic parser that maps natural language utterances into executable programs when only indirect supervision is available: examples are labeled with the correct execution result, but not the program itself.
