Alon Cohen
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We present the OMG-CMDP! algorithm for regret minimization in adversarial Contextual MDPs. The algorithm operates under the minimal assumptions of realizable function class and access to online least squares and log loss regression oracles. Our algorithm is efficient (assuming efficient online regression oracles), simple and robust to approximation errors. It enjoys an
$\widetilde{O}(H^{2.5} \sqrt{ T|S||A| (
} \linebreak[1]
\overline{ \mathcal{R}(\mathcal{O})
+ H \log(\delta^{-1}) )})$ regret guarantee,
with $T$ being the number of episodes, $S$ the state space, $A$ the action space, $H$ the horizon and $\mathcal{R}(\mathcal{O}) = \mathcal{R}(\mathcal{O}_{\mathrm{sq}}^\mathcal{F}) + \mathcal{R}(\mathcal{O}_{\mathrm{log}}^\mathcal{P})$
is the sum of the regression oracles' regret, used to approximate the context-dependent rewards and dynamics, respectively. To the best of our knowledge, our algorithm is the first efficient and rate optimal regret minimization algorithm for adversarial CMDPs which operates under the minimal and standard assumption of online function approximation. Our technique relies on standard convex optimization algorithms, and we show that it is robust to approximation errors.
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Building a Clinically-Focused Problem List From Medical Notes
Birju Patel
Cathy Cheung
Liwen Xu
Peter Clardy
Rachana Fellinger
LOUHI 2022: The 13th International Workshop on Health Text Mining and Information Analysis (2022)
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Clinical notes often contain vital information not observed in other structured data, but their unstructured nature can lead to critical patient-related information being lost. To make sure this valuable information is utilized for patient care, algorithms that summarize notes into a problem list are often proposed. Focusing on identifying medically-relevant entities in the free-form text, these solutions are often detached from a canonical ontology and do not allow downstream use of the detected text-spans. As a solution, we present here a system for generating a canonical problem list from medical notes, consisting of two major stages. At the first stage, annotation, we use a transformer model to detect all clinical conditions which are mentioned in a single note. These clinical conditions are then grounded to a predefined ontology, and are linked to spans in the text. At the second stage, summarization, we aggregate over the set of clinical conditions detected on all of the patient's note, and produce a concise patient summary that organizes their important conditions.
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Shared computational principles for language processing in humans and deep language models
Ariel Goldstein
Zaid Zada
Eliav Buchnik
Amy Price
Bobbi Aubrey
Samuel A. Nastase
Harshvardhan Gazula
Gina Choe
Aditi Rao
Catherine Kim
Colton Casto
Lora Fanda
Werner Doyle
Daniel Friedman
Patricia Dugan
Lucia Melloni
Roi Reichart
Sasha Devore
Adeen Flinker
Liat Hasenfratz
Omer Levy,
Kenneth A. Norman
Orrin Devinsky
Uri Hasson
Nature Neuroscience (2022)
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Departing from traditional linguistic models, advances in deep learning have resulted in a new type of predictive (autoregressive) deep language models (DLMs). Using a self-supervised next-word prediction task, these models generate appropriate linguistic responses in a given context. In the current study, nine participants listened to a 30-min podcast while their brain responses were recorded using electrocorticography (ECoG). We provide empirical evidence that the human brain and autoregressive DLMs share three fundamental computational principles as they process the same natural narrative: (1) both are engaged in continuous next-word prediction before word onset; (2) both match their pre-onset predictions to the incoming word to calculate post-onset surprise; (3) both rely on contextual embeddings to represent words in natural contexts. Together, our findings suggest that autoregressive DLMs provide a new and biologically feasible computational framework for studying the neural basis of language.
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We consider stochastic optimization with delayed gradients where, at each time step~$t$, the algorithm makes an update using a stale stochastic gradient from step $t - d_t$ for some arbitrary delay $d_t$.
This setting abstracts asynchronous distributed optimization where a central server receives gradient updates computed by worker machines. These machines can experience computation and communication loads that might vary significantly over time.
In the general non-convex smooth optimization setting, we give a simple and efficient algorithm that requires $O( \sigma^2/\epsilon^4 + \tau/\epsilon^2 )$ steps for finding an $\epsilon$-stationary point $x$, where $\tau$ is the \emph{average} delay $\smash{\frac{1}{T}\sum_{t=1}^T d_t}$ and $\sigma^2$ is the variance of the stochastic gradients.
This improves over previous work, which showed that stochastic gradient decent achieves the same rate but with respect to the \emph{maximal} delay $\max_{t} d_t$, that can be significantly larger than the average delay especially in heterogeneous distributed systems.
Our experiments demonstrate the efficacy and robustness of our algorithm in cases where the delay distribution is skewed or heavy-tailed.
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Learning and Evaluating a Differentially Private Pre-trained Language Model
Shlomo Hoory
Avichai Tendler
Findings of the Association for Computational Linguistics: EMNLP 2021, Association for Computational Linguistics, Punta Cana, Dominican Republic, pp. 1178-1189
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Contextual language models have led to significantly better results on a plethora of language understanding tasks, especially when pre-trained on the same data as the downstream task. While this additional pre-training usually improves performance, it often leads to information leakage and therefore risks the privacy of individuals mentioned in the training data. One method to guarantee the privacy of such individuals is to train a differentially private model, but this usually comes at the expense of model performance. Moreover, it is hard to tell given a privacy parameter $\epsilon$ what was the effect on the trained representation and whether it maintained relevant information while improving privacy. To improve privacy and guide future practitioners and researchers, we demonstrate here how to train a differentially private pre-trained language model (i.e., BERT) with a privacy guarantee of $\epsilon=0.5$ with only a small degradation in performance. We experiment on a dataset of clinical notes with a model trained on an entity extraction (EE) task on and compare it to a similar model trained without differential privacy. Finally, we present a series of experiments showing how to interpret the differentially private representation and understand the information lost and maintained in this process.
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We study online learning of finite-horizon Markov Decision Processes (MDPs) with adversarially changing loss functions and unknown dynamics.
In each episode, the learner observes a trajectory realized by her policy chosen for this episode. In addition, the learner suffers and observes the loss accumulated along the trajectory which we call aggregate bandit feedback. The learner, however, never observes any additional information about the loss; in particular, the individual losses suffered along the trajectory.
Our main result is a computationally-efficient algorithm with \sqrt{K} regret for this setting, where K is the number of episodes.
We efficiently reduce \emph{Online MDPs with Aggregate Bandit Feedback} to a novel setting: Distorted Linear Bandits (DLB). This setting is a robust generalization of linear bandits in which selected actions are adversarially perturbed.
We give a computationally-efficient online learning algorithm for DLB and prove a \sqrt{T} regret bound, where T is the number of time steps.
Our algorithm is based on a schedule of increasing learning rates used in Online Mirror Descent with a self-concordant barrier regularization.
We use the DLB algorithm to derive our main result of \sqrt{K} regret.
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We study the Stochastic Shortest Path (SSP) problem in which an agent has to reach a goal state in minimum total expected cost.
In the learning formulation of the problem, the agent has no prior knowledge about the costs and dynamics of the model.
She repeatedly interacts with the model for $K$ episodes, and has to minimize her regret.
In this work we show that the minimax regret for this setting is $\widetilde O(\sqrt{ (B_\star^2 + B_\star) |S| |A| K})$ where $B_\star$ is a bound on the expected cost of the optimal policy from any state, $S$ is the state space, and $A$ is the action space.
This matches the $\Omega (\sqrt{ B_\star^2 |S| |A| K})$ lower bound of \citet{rosenberg2020near} for $B_\star \ge 1$, and improves their regret bound by a factor of $\sqrt{|S|}$.
For $B_\star < 1$ we prove a matching lower bound of $\Omega (\sqrt{ B_\star |S| |A| K})$.
Our algorithm is based on a novel reduction from SSP to finite-horizon MDPs.
To that end, we provide an algorithm for the finite-horizon setting whose leading term in the regret depends polynomially on the expected cost of the optimal policy and only logarithmically on the horizon.
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We consider the applications of the Frank-Wolfe (FW) algorithm for Apprenticeship Learning (AL). In this setting, there is a Markov Decision Process (MDP), but the reward function is not given explicitly. Instead, there is an expert that acts according to some policy, and the goal is to find a policy whose feature expectations are closest to those of the expert policy. We formulate this problem as finding the projection of the feature expectations of the expert on the feature expectations polytope -- the convex hull of the feature expectations of all the deterministic policies in the MDP. We show that this formulation is equivalent to the AL objective and that solving this problem using the FW algorithm is equivalent to the most known AL algorithm, the projection method of Abbeel and Ng (2004).
This insight allows us to analyze AL with tools from the convex optimization literature and to derive tighter bounds on AL. Specifically, we show that a variation of the FW method that is based on taking ``away steps" achieves a linear rate of convergence when applied to AL. We also show experimentally that this version outperforms the FW baseline. To the best of our knowledge, this is the first work that shows linear convergence rates for AL.
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We derive and analyze learning algorithms for policy evaluation, policy gradient and apprenticeship learning for the average reward criteria. Existing algorithms explicitly require an upper bound on the mixing time. In contrast, we build on ideas from Markov-chain theory and derive sampling algorithms that do not require such an upper bound. For these algorithms, we provide theoretical bounds on their sample-complexity and running time.
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Near-optimal Regret Bounds for Stochastic Shortest Path
Aviv Rosenberg
International Conference on Machine Learning (ICML) 2020 (2020)
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Stochastic shortest path (SSP) is a well-known problem in planning and control, in which an agent has to reach a goal state in minimum total expected cost.
In the learning formulation of the problem, the agent is unaware of the environment dynamics (i.e., the transition function) and has to repeatedly play for a given number of episodes while learning the problem’s optimal solution.
Unlike other well-studied models in reinforcement learning (RL), the length of an episode is not predetermined (or bounded) and is influenced by the agent’s actions.
Recently, Tarbouriech et al. (2019) studied this problem in the context of regret minimization, and provided an algorithm whose regret bound is inversely proportional to the square root of the minimum instantaneous cost.
In this work we remove this dependence on the minimum cost—we give an algorithm that guarantees a regret bound of $O(B S \sqrt{A K})$ , where B is an upper bound on the expected cost of the optimal policy, S is the number of states, A is the number of actions and K is the total number of episodes.
We additionally show that any learning algorithm must have at least $\Omega(B \sqrt{S A K})$ regret in the worst case.
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Imagine a large firm with multiple departments that plans a large recruitment. Candidates arrive one by-one, and for each candidate the firm decides, based on her data (CV, skills, experience, etc), whether to summon her for an interview. The firm wants to recruit the best candidates while minimizing the number of interviews. We model such scenarios as a matching problem between items (candidates) and categories (departments): the items arrive one-by-one in an online manner, and upon processing each item the algorithm decides, based on its value and the categories it can be matched with, whether to retain or discard it (this decision is irrevocable). The goal is to retain as few items as possible while guaranteeing that the set of retained items contains an optimal matching.
We consider two variants of this problem: (i) in the first variant it is assumed that the n items are drawn independently from an unknown distribution D. (ii) In the second variant it is assumed that before the process starts, the algorithm has an access to a training set of n items drawn independently from the same unknown distribution (e.g. data of candidates from previous recruitment seasons). We give tight bounds on the minimum possible number of retained items in each of these variants. These results demonstrate that one can retain exponentially less items in the second variant (with the training set).
Our algorithms and analysis utilize ideas and techniques from statistical learning theory and from
discrete algorithms.
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We present the first computationally-efficient algorithm with $\tO(\sqrt{T})$ regret for learning in Linear Quadratic Control systems with unknown linear dynamics and known quadratic costs.
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Planning and Learning with Stochastic Action Sets
Martin Mladenov
Proceedings of the Twenty-seventh International Joint Conference on Artificial Intelligence (IJCAI-18), Stockholm (2018), pp. 4674-4682
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This is an extended version of the paper Planning and Learning with Stochastic Action Sets that appeared in the Proceedings of the Twenty-seventh International Joint Conference on Artificial Intelligence (IJCAI-18), pp.4674-4682, Stockholm (2018).
In many practical uses of reinforcement learning (RL) the set of actions available
at a given state is a random variable, with realizations governed by an exogenous
stochastic process. Somewhat surprisingly, the foundations for such sequential
decision processes have been unaddressed. In this work, we formalize and
investigate MDPs with stochastic action sets (SAS-MDPs) to provide these foundations.
We show that optimal policies and value functions in this model have a
structure that admits a compact representation. From an RL perspective, we show
that Q-learning with sampled action sets is sound. In model-based settings, we
consider two important special cases: when individual actions are available with
independent probabilities; and a sampling-based model for unknown distributions.
We develop poly-time value and policy iteration methods for both cases; and in the
first, we offer a poly-time linear programming solution.
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We study the problem of controlling linear time-invariant systems with known noisy dynamics and adversarially chosen quadratic losses. We present the first efficient online learning algorithms in this setting that guarantee O(√T) regret under mild assumptions, where T is the time horizon. Our algorithms rely on a novel SDP relaxation for the steady-state distribution of the system. Crucially, and in contrast to previously proposed relaxations, the feasible solutions of our SDP all correspond to strongly stable'' policies that mix exponentially fast to a steady state.
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