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Found 21 papers, 7 papers with code
The SMART approach to instance-optimal online learning
We devise an online learning algorithm -- titled Switching via Monotone Adapted Regret Traces (SMART) -- that adapts to the data and achieves regret that is instance optimal, i. e., simultaneously competitive on every input sequence compared to the performance of the follow-the-leader (FTL) policy and the worst case guarantee of any other input policy.
Artificial Replay: A Meta-Algorithm for Harnessing Historical Data in Bandits
How best to incorporate historical data to "warm start" bandit algorithms is an open question: naively initializing reward estimates using all historical samples can suffer from spurious data and imbalanced data coverage, leading to computational and storage issues $\unicode{x2014}$ particularly salient in continuous action spaces.
Adaptive Discretization in Online Reinforcement Learning
In this paper we provide a unified theoretical analysis of tree-based hierarchical partitioning methods for online reinforcement learning, providing model-free and model-based algorithms.
Real-Time Approximate Routing for Smart Transit Systems
We study real-time routing policies in smart transit systems, where the platform has a combination of cars and high-capacity vehicles (e. g., buses or shuttles) and seeks to serve a set of incoming trip requests.
Optimization and Control
Explainable AI for Robot Failures: Generating Explanations that Improve User Assistance in Fault Recovery
In order for error explanations to be meaningful, we investigate what types of information within a set of hand-scripted explanations are most helpful to non-experts for failure and solution identification.
Explainable AI for System Failures: Generating Explanations that Improve Human Assistance in Fault Recovery
With the growing capabilities of intelligent systems, the integration of artificial intelligence (AI) and robots in everyday life is increasing.
Adaptive Discretization for Model-Based Reinforcement Learning
We introduce the technique of adaptive discretization to design an efficient model-based episodic reinforcement learning algorithm in large (potentially continuous) state-action spaces.
Model-based Reinforcement Learning
reinforcement-learning
+1
Adaptive Discretization for Episodic Reinforcement Learning in Metric Spaces
We present an efficient algorithm for model-free episodic reinforcement learning on large (potentially continuous) state-action spaces.
Hierarchical Transfer Learning for Multi-label Text Classification
Compared to binary classifiers trained from scratch, our HTrans approach results in significant improvements of 1{\%} on micro-F1 and 3{\%} on macro-F1 on the RCV1 dataset.
Online Allocation and Pricing: Constant Regret via Bellman Inequalities
We develop a framework for designing simple and efficient policies for a family of online allocation and pricing problems, that includes online packing, budget-constrained probing, dynamic pricing, and online contextual bandits with knapsacks.
The Bayesian Prophet: A Low-Regret Framework for Online Decision Making
We develop a new framework for designing online policies given access to an oracle providing statistical information about an offline benchmark.
Summarizing Situational and Topical Information During Crises
The use of microblogging platforms such as Twitter during crises has become widespread.
Generating Abstractive Summaries from Meeting Transcripts
The most informative and well-formed sub-graph obtained by integer linear programming (ILP) is selected to generate a one-sentence summary for each topic segment.
Multi-document abstractive summarization using ILP based multi-sentence compression
The sentences in the most important document are aligned to sentences in other documents to generate clusters of similar sentences.
Abstractive Meeting Summarization UsingDependency Graph Fusion
Automatic summarization techniques on meeting conversations developed so far have been primarily extractive, resulting in poor summaries.
Unbounded Human Learning: Optimal Scheduling for Spaced Repetition
Second, we use this memory model to develop a stochastic model for spaced repetition systems.
Fast Bidirectional Probability Estimation in Markov Models
We develop a new bidirectional algorithm for estimating Markov chain multi-step transition probabilities: given a Markov chain, we want to estimate the probability of hitting a given target state in $\ell$ steps after starting from a given source distribution.
Personalized PageRank Estimation and Search: A Bidirectional Approach
First, for the problem of estimating Personalized PageRank (PPR) from a source distribution to a target node, we present a new bidirectional estimator with simple yet strong guarantees on correctness and performance, and 3x to 8x speedup over existing estimators in experiments on a diverse set of networks.
Online Collaborative-Filtering on Graphs
We consider this problem under a simple natural model, wherein the number of items and the number of item-views are of the same order, and an `access-graph' constrains which user is allowed to see which item.
The Price of Privacy in Untrusted Recommendation Engines
In the information-rich regime, where each user rates at least a constant fraction of items, a spectral clustering approach is shown to achieve a sample-complexity lower bound derived from a simple information-theoretic argument based on Fano's inequality.
