mtybilly/Instruct2DS huggingface dataset:
Learning Queueing Policies for Organ Transplantation Allocation using Interpretable Counterfactual Survival Analysis
Authors: Jeroen Berrevoets, Ahmed Alaa, Zhaozhi Qian, James Jordon, Alexander E. S. Gimson, Mihaela van der Schaar
Published in: Proceedings of the 38th International Conference on Machine Learning (Main Conference Track), 2021

Abstract: Organ transplantation is often the last resort for treating end-stage illnesses, but managing transplant wait-lists is challenging because of organ scarcity and the complexity of assessing donor-recipient compatibility. In this paper, we develop a data-driven model for (real-time) organ allocation using observational data for transplant outcomes. Our model integrates a queuing-theoretic framework with unsupervised learning to cluster the organs into “organ types”, and then construct priority queues (associated with each organ type) wherein incoming patients are assigned. To reason about organ allocations, the model uses synthetic controls to infer a patient’s survival outcomes under counterfactual allocations to the different organ types– the model is trained end-to-end to optimise the trade-off between patient waiting time and expected survival time. The usage of synthetic controls enable patient-level interpretations of allocation decisions that can be presented and understood by clinicians. We test our model on multiple data sets, and show that it outperforms other organ-allocation policies in terms of added life-years, and death count. Furthermore, we introduce a novel organ-allocation simulator to accurately test new policies.
Learning from Biased Data: A Semi-Parametric Approach
Authors: Patrice Bertail, Stephan Clémençon, Yannick Guyonvarch, Nathan Noiry
Published in: Proceedings of the 38th International Conference on Machine Learning (Main Conference Track), 2021

Abstract: We consider risk minimization problems where the (source) distribution $P_S$ of the training observations $Z_1, łdots, Z_n$ differs from the (target) distribution $P_T$ involved in the risk that one seeks to minimize. Under the natural assumption that $P_S$ dominates $P_T$, \textiti.e. $P_T< \! \!<P_S$, we develop a semi-parametric framework in the situation where we \textitdo not observe any sample from $P_T$, but rather have access to some auxiliary information at the target population scale. More precisely, assuming that the Radon-Nikodym derivative $dP_T/dP_S(z)$ belongs to a parametric class $\g(z,α), αın \mathcalA\$ and that some (generalized) moments of $P_T$ are available to the learner, we propose a two-step learning procedure to perform the risk minimization task. We first select $\hatα$ so as to match the moment constraints as closely as possible and then reweight each (biased) training observation $Z_i$ by $g(Z_i,\hatα)$ in the final Empirical Risk Minimization (ERM) algorithm. We establish a $O_ℙ(1/\sqrtn)$ generalization bound proving that, remarkably, the solution to the weighted ERM problem thus constructed achieves a learning rate of the same order as that attained in absence of any sampling bias. Beyond these theoretical guarantees, numerical results providing strong empirical evidence of the relevance of the approach promoted in this article are displayed.
Is Space-Time Attention All You Need for Video Understanding?
Authors: Gedas Bertasius, Heng Wang, Lorenzo Torresani
Published in: Proceedings of the 38th International Conference on Machine Learning (Main Conference Track), 2021

Abstract: We present a convolution-free approach to video classification built exclusively on self-attention over space and time. Our method, named “TimeSformer,” adapts the standard Transformer architecture to video by enabling spatiotemporal feature learning directly from a sequence of frame-level patches. Our experimental study compares different self-attention schemes and suggests that “divided attention,” where temporal attention and spatial attention are separately applied within each block, leads to the best video classification accuracy among the design choices considered. Despite the radically new design, TimeSformer achieves state-of-the-art results on several action recognition benchmarks, including the best reported accuracy on Kinetics-400 and Kinetics-600. Finally, compared to 3D convolutional networks, our model is faster to train, it can achieve dramatically higher test efficiency (at a small drop in accuracy), and it can also be applied to much longer video clips (over one minute long). Code and models are available at: https://github.com/facebookresearch/TimeSformer.
Confidence Scores Make Instance-dependent Label-noise Learning Possible
Authors: Antonin Berthon, Bo Han, Gang Niu, Tongliang Liu, Masashi Sugiyama
Published in: Proceedings of the 38th International Conference on Machine Learning (Main Conference Track), 2021

Abstract: In learning with noisy labels, for every instance, its label can randomly walk to other classes following a transition distribution which is named a noise model. Well-studied noise models are all instance-independent, namely, the transition depends only on the original label but not the instance itself, and thus they are less practical in the wild. Fortunately, methods based on instance-dependent noise have been studied, but most of them have to rely on strong assumptions on the noise models. To alleviate this issue, we introduce confidence-scored instance-dependent noise (CSIDN), where each instance-label pair is equipped with a confidence score. We find that with the help of confidence scores, the transition distribution of each instance can be approximately estimated. Similarly to the powerful forward correction for instance-independent noise, we propose a novel instance-level forward correction for CSIDN. We demonstrate the utility and effectiveness of our method through multiple experiments on datasets with synthetic label noise and real-world unknown noise.
Size-Invariant Graph Representations for Graph Classification Extrapolations
Authors: Beatrice Bevilacqua, Yangze Zhou, Bruno Ribeiro
Published in: Proceedings of the 38th International Conference on Machine Learning (Main Conference Track), 2021

Abstract: In general, graph representation learning methods assume that the train and test data come from the same distribution. In this work we consider an underexplored area of an otherwise rapidly developing field of graph representation learning: The task of out-of-distribution (OOD) graph classification, where train and test data have different distributions, with test data unavailable during training. Our work shows it is possible to use a causal model to learn approximately invariant representations that better extrapolate between train and test data. Finally, we conclude with synthetic and real-world dataset experiments showcasing the benefits of representations that are invariant to train/test distribution shifts.
Principal Bit Analysis: Autoencoding with Schur-Concave Loss
Authors: Sourbh Bhadane, Aaron B Wagner, Jayadev Acharya
Published in: Proceedings of the 38th International Conference on Machine Learning (Main Conference Track), 2021

Abstract: We consider a linear autoencoder in which the latent variables are quantized, or corrupted by noise, and the constraint is Schur-concave in the set of latent variances. Although finding the optimal encoder/decoder pair for this setup is a nonconvex optimization problem, we show that decomposing the source into its principal components is optimal. If the constraint is strictly Schur-concave and the empirical covariance matrix has only simple eigenvalues, then any optimal encoder/decoder must decompose the source in this way. As one application, we consider a strictly Schur-concave constraint that estimates the number of bits needed to represent the latent variables under fixed-rate encoding, a setup that we call \emphPrincipal Bit Analysis (PBA). This yields a practical, general-purpose, fixed-rate compressor that outperforms existing algorithms. As a second application, we show that a prototypical autoencoder-based variable-rate compressor is guaranteed to decompose the source into its principal components.
Lower Bounds on Cross-Entropy Loss in the Presence of Test-time Adversaries
Authors: Arjun Nitin Bhagoji, Daniel Cullina, Vikash Sehwag, Prateek Mittal
Published in: Proceedings of the 38th International Conference on Machine Learning (Main Conference Track), 2021

Abstract: Understanding the fundamental limits of robust supervised learning has emerged as a problem of immense interest, from both practical and theoretical standpoints. In particular, it is critical to determine classifier-agnostic bounds on the training loss to establish when learning is possible. In this paper, we determine optimal lower bounds on the cross-entropy loss in the presence of test-time adversaries, along with the corresponding optimal classification outputs. Our formulation of the bound as a solution to an optimization problem is general enough to encompass any loss function depending on soft classifier outputs. We also propose and provide a proof of correctness for a bespoke algorithm to compute this lower bound efficiently, allowing us to determine lower bounds for multiple practical datasets of interest. We use our lower bounds as a diagnostic tool to determine the effectiveness of current robust training methods and find a gap from optimality at larger budgets. Finally, we investigate the possibility of using of optimal classification outputs as soft labels to empirically improve robust training.
Additive Error Guarantees for Weighted Low Rank Approximation
Authors: Aditya Bhaskara, Aravinda Kanchana Ruwanpathirana, Maheshakya Wijewardena
Published in: Proceedings of the 38th International Conference on Machine Learning (Main Conference Track), 2021

Abstract: Low-rank approximation is a classic tool in data analysis, where the goal is to approximate a matrix $A$ with a low-rank matrix $L$ so as to minimize the error $\normA - L_F^2$. However in many applications, approximating some entries is more important than others, which leads to the weighted low rank approximation problem. However, the addition of weights makes the low-rank approximation problem intractable. Thus many works have obtained efficient algorithms under additional structural assumptions on the weight matrix (such as low rank, and appropriate block structure). We study a natural greedy algorithm for weighted low rank approximation and develop a simple condition under which it yields bi-criteria approximation up to a small additive factor in the error. The algorithm involves iteratively computing the top singular vector of an appropriately varying matrix, and is thus easy to implement at scale. Our methods also allow us to study the problem of low rank approximation under $\ell_p$ norm error.
Sample Complexity of Robust Linear Classification on Separated Data
Authors: Robi Bhattacharjee, Somesh Jha, Kamalika Chaudhuri
Published in: Proceedings of the 38th International Conference on Machine Learning (Main Conference Track), 2021

Abstract: We consider the sample complexity of learning with adversarial robustness. Most prior theoretical results for this problem have considered a setting where different classes in the data are close together or overlapping. We consider, in contrast, the well-separated case where there exists a classifier with perfect accuracy and robustness, and show that the sample complexity narrates an entirely different story. Specifically, for linear classifiers, we show a large class of well-separated distributions where the expected robust loss of any algorithm is at least $Ømega(\fracdn)$, whereas the max margin algorithm has expected standard loss $O(\frac1n)$. This shows a gap in the standard and robust losses that cannot be obtained via prior techniques. Additionally, we present an algorithm that, given an instance where the robustness radius is much smaller than the gap between the classes, gives a solution with expected robust loss is $O(\frac1n)$. This shows that for very well-separated data, convergence rates of $O(\frac1n)$ are achievable, which is not the case otherwise. Our results apply to robustness measured in any $\ell_p$ norm with $p > 1$ (including $p = ınfty$).
Finding k in Latent $k-$ polytope
Authors: Chiranjib Bhattacharyya, Ravindran Kannan, Amit Kumar
Published in: Proceedings of the 38th International Conference on Machine Learning (Main Conference Track), 2021

Abstract: The recently introduced Latent $k-$ Polytope($ŁkP$) encompasses several stochastic Mixed Membership models including Topic Models. The problem of finding $k$, the number of extreme points of $ŁkP$, is a fundamental challenge and includes several important open problems such as determination of number of components in Ad-mixtures. This paper addresses this challenge by introducing Interpolative Convex Rank(\INR) of a matrix defined as the minimum number of its columns whose convex hull is within Hausdorff distance $ɛ$ of the convex hull of all columns. The first important contribution of this paper is to show that under \emphstandard assumptions $k$ equals the \INR of a \emphsubset smoothed data matrix defined from Data generated from an $ŁkP$. The second important contribution of the paper is a polynomial time algorithm for finding $k$ under standard assumptions. An immediate corollary is the first polynomial time algorithm for finding the \emphinner dimension in Non-negative matrix factorisation(NMF) with assumptions which are qualitatively different than existing ones such as \emphSeparability. %An immediate corollary is the first polynomial time algorithm for finding the \emphinner dimension in Non-negative matrix factorisation(NMF) with assumptions considerably weaker than \emphSeparability.
Non-Autoregressive Electron Redistribution Modeling for Reaction Prediction
Authors: Hangrui Bi, Hengyi Wang, Chence Shi, Connor Coley, Jian Tang, Hongyu Guo
Published in: Proceedings of the 38th International Conference on Machine Learning (Main Conference Track), 2021

Abstract: Reliably predicting the products of chemical reactions presents a fundamental challenge in synthetic chemistry. Existing machine learning approaches typically produce a reaction product by sequentially forming its subparts or intermediate molecules. Such autoregressive methods, however, not only require a pre-defined order for the incremental construction but preclude the use of parallel decoding for efficient computation. To address these issues, we devise a non-autoregressive learning paradigm that predicts reaction in one shot. Leveraging the fact that chemical reactions can be described as a redistribution of electrons in molecules, we formulate a reaction as an arbitrary electron flow and predict it with a novel multi-pointer decoding network. Experiments on the USPTO-MIT dataset show that our approach has established a new state-of-the-art top-1 accuracy and achieves at least 27 times inference speedup over the state-of-the-art methods. Also, our predictions are easier for chemists to interpret owing to predicting the electron flows.
TempoRL: Learning When to Act
Authors: André Biedenkapp, Raghu Rajan, Frank Hutter, Marius Lindauer
Published in: Proceedings of the 38th International Conference on Machine Learning (Main Conference Track), 2021

Abstract: Reinforcement learning is a powerful approach to learn behaviour through interactions with an environment. However, behaviours are usually learned in a purely reactive fashion, where an appropriate action is selected based on an observation. In this form, it is challenging to learn when it is necessary to execute new decisions. This makes learning inefficient especially in environments that need various degrees of fine and coarse control. To address this, we propose a proactive setting in which the agent not only selects an action in a state but also for how long to commit to that action. Our TempoRL approach introduces skip connections between states and learns a skip-policy for repeating the same action along these skips. We demonstrate the effectiveness of TempoRL on a variety of traditional and deep RL environments, showing that our approach is capable of learning successful policies up to an order of magnitude faster than vanilla Q-learning.
Follow-the-Regularized-Leader Routes to Chaos in Routing Games
Authors: Jakub Bielawski, Thiparat Chotibut, Fryderyk Falniowski, Grzegorz Kosiorowski, Michał Misiurewicz, Georgios Piliouras
Published in: Proceedings of the 38th International Conference on Machine Learning (Main Conference Track), 2021

Abstract: We study the emergence of chaotic behavior of Follow-the-Regularized Leader (FoReL) dynamics in games. We focus on the effects of increasing the population size or the scale of costs in congestion games, and generalize recent results on unstable, chaotic behaviors in the Multiplicative Weights Update dynamics to a much larger class of FoReL dynamics. We establish that, even in simple linear non-atomic congestion games with two parallel links and \emphany fixed learning rate, unless the game is fully symmetric, increasing the population size or the scale of costs causes learning dynamics to becomes unstable and eventually chaotic, in the sense of Li-Yorke and positive topological entropy. Furthermore, we prove the existence of novel non-standard phenomena such as the coexistence of stable Nash equilibria and chaos in the same game. We also observe the simultaneous creation of a chaotic attractor as another chaotic attractor gets destroyed. Lastly, although FoReL dynamics can be strange and non-equilibrating, we prove that the time average still converges to an \emphexact equilibrium for any choice of learning rate and any scale of costs.
Neural Symbolic Regression that scales
Authors: Luca Biggio, Tommaso Bendinelli, Alexander Neitz, Aurelien Lucchi, Giambattista Parascandolo
Published in: Proceedings of the 38th International Conference on Machine Learning (Main Conference Track), 2021

Abstract: Symbolic equations are at the core of scientific discovery. The task of discovering the underlying equation from a set of input-output pairs is called symbolic regression. Traditionally, symbolic regression methods use hand-designed strategies that do not improve with experience. In this paper, we introduce the first symbolic regression method that leverages large scale pre-training. We procedurally generate an unbounded set of equations, and simultaneously pre-train a Transformer to predict the symbolic equation from a corresponding set of input-output-pairs. At test time, we query the model on a new set of points and use its output to guide the search for the equation. We show empirically that this approach can re-discover a set of well-known physical equations, and that it improves over time with more data and compute.
Model Distillation for Revenue Optimization: Interpretable Personalized Pricing
Authors: Max Biggs, Wei Sun, Markus Ettl
Published in: Proceedings of the 38th International Conference on Machine Learning (Main Conference Track), 2021

Abstract: Data-driven pricing strategies are becoming increasingly common, where customers are offered a personalized price based on features that are predictive of their valuation of a product. It is desirable for this pricing policy to be simple and interpretable, so it can be verified, checked for fairness, and easily implemented. However, efforts to incorporate machine learning into a pricing framework often lead to complex pricing policies that are not interpretable, resulting in slow adoption in practice. We present a novel, customized, prescriptive tree-based algorithm that distills knowledge from a complex black-box machine learning algorithm, segments customers with similar valuations and prescribes prices in such a way that maximizes revenue while maintaining interpretability. We quantify the regret of a resulting policy and demonstrate its efficacy in applications with both synthetic and real-world datasets.
Scalable Normalizing Flows for Permutation Invariant Densities
Authors: Marin Biloš, Stephan Günnemann
Published in: Proceedings of the 38th International Conference on Machine Learning (Main Conference Track), 2021

Abstract: Modeling sets is an important problem in machine learning since this type of data can be found in many domains. A promising approach defines a family of permutation invariant densities with continuous normalizing flows. This allows us to maximize the likelihood directly and sample new realizations with ease. In this work, we demonstrate how calculating the trace, a crucial step in this method, raises issues that occur both during training and inference, limiting its practicality. We propose an alternative way of defining permutation equivariant transformations that give closed form trace. This leads not only to improvements while training, but also to better final performance. We demonstrate the benefits of our approach on point processes and general set modeling.
Online Learning for Load Balancing of Unknown Monotone Resource Allocation Games
Authors: Ilai Bistritz, Nicholas Bambos
Published in: Proceedings of the 38th International Conference on Machine Learning (Main Conference Track), 2021

Abstract: Consider N players that each uses a mixture of K resources. Each of the players’ reward functions includes a linear pricing term for each resource that is controlled by the game manager. We assume that the game is strongly monotone, so if each player runs gradient descent, the dynamics converge to a unique Nash equilibrium (NE). Unfortunately, this NE can be inefficient since the total load on a given resource can be very high. In principle, we can control the total loads by tuning the coefficients of the pricing terms. However, finding pricing coefficients that balance the loads requires knowing the players’ reward functions and their action sets. Obtaining this game structure information is infeasible in a large-scale network and violates the users’ privacy. To overcome this, we propose a simple algorithm that learns to shift the NE of the game to meet the total load constraints by adjusting the pricing coefficients in an online manner. Our algorithm only requires the total load per resource as feedback and does not need to know the reward functions or the action sets. We prove that our algorithm guarantees convergence in L2 to a NE that meets target total load constraints. Simulations show the effectiveness of our approach when applied to smart grid demand-side management or power control in wireless networks.
Low-Precision Reinforcement Learning: Running Soft Actor-Critic in Half Precision
Authors: Johan Björck, Xiangyu Chen, Christopher De Sa, Carla P Gomes, Kilian Weinberger
Published in: Proceedings of the 38th International Conference on Machine Learning (Main Conference Track), 2021

Abstract: Low-precision training has become a popular approach to reduce compute requirements, memory footprint, and energy consumption in supervised learning. In contrast, this promising approach has not yet enjoyed similarly widespread adoption within the reinforcement learning (RL) community, partly because RL agents can be notoriously hard to train even in full precision. In this paper we consider continuous control with the state-of-the-art SAC agent and demonstrate that a naïve adaptation of low-precision methods from supervised learning fails. We propose a set of six modifications, all straightforward to implement, that leaves the underlying agent and its hyperparameters unchanged but improves the numerical stability dramatically. The resulting modified SAC agent has lower memory and compute requirements while matching full-precision rewards, demonstrating that low-precision training can substantially accelerate state-of-the-art RL without parameter tuning.
Multiplying Matrices Without Multiplying
Authors: Davis Blalock, John Guttag
Published in: Proceedings of the 38th International Conference on Machine Learning (Main Conference Track), 2021

Abstract: Multiplying matrices is among the most fundamental and most computationally demanding operations in machine learning and scientific computing. Consequently, the task of efficiently approximating matrix products has received significant attention. We introduce a learning-based algorithm for this task that greatly outperforms existing methods. Experiments using hundreds of matrices from diverse domains show that it often runs 10x faster than alternatives at a given level of error, as well as 100x faster than exact matrix multiplication. In the common case that one matrix is known ahead of time, our method also has the interesting property that it requires zero multiply-adds. These results suggest that a mixture of hashing, averaging, and byte shuffling—the core operations of our method—could be a more promising building block for machine learning than the sparsified, factorized, and/or scalar quantized matrix products that have recently been the focus of substantial research and hardware investment.
One for One, or All for All: Equilibria and Optimality of Collaboration in Federated Learning
Authors: Avrim Blum, Nika Haghtalab, Richard Lanas Phillips, Han Shao
Published in: Proceedings of the 38th International Conference on Machine Learning (Main Conference Track), 2021

Abstract: In recent years, federated learning has been embraced as an approach for bringing about collaboration across large populations of learning agents. However, little is known about how collaboration protocols should take agents’ incentives into account when allocating individual resources for communal learning in order to maintain such collaborations. Inspired by game theoretic notions, this paper introduces a framework for incentive-aware learning and data sharing in federated learning. Our stable and envy-free equilibria capture notions of collaboration in the presence of agents interested in meeting their learning objectives while keeping their own sample collection burden low. For example, in an envy-free equilibrium, no agent would wish to swap their sampling burden with any other agent and in a stable equilibrium, no agent would wish to unilaterally reduce their sampling burden. In addition to formalizing this framework, our contributions include characterizing the structural properties of such equilibria, proving when they exist, and showing how they can be computed. Furthermore, we compare the sample complexity of incentive-aware collaboration with that of optimal collaboration when one ignores agents’ incentives.
Black-box density function estimation using recursive partitioning
Authors: Erik Bodin, Zhenwen Dai, Neill Campbell, Carl Henrik Ek
Published in: Proceedings of the 38th International Conference on Machine Learning (Main Conference Track), 2021

Abstract: We present a novel approach to Bayesian inference and general Bayesian computation that is defined through a sequential decision loop. Our method defines a recursive partitioning of the sample space. It neither relies on gradients nor requires any problem-specific tuning, and is asymptotically exact for any density function with a bounded domain. The output is an approximation to the whole density function including the normalisation constant, via partitions organised in efficient data structures. Such approximations may be used for evidence estimation or fast posterior sampling, but also as building blocks to treat a larger class of estimation problems. The algorithm shows competitive performance to recent state-of-the-art methods on synthetic and real-world problems including parameter inference for gravitational-wave physics.
Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks
Authors: Cristian Bodnar, Fabrizio Frasca, Yuguang Wang, Nina Otter, Guido F Montufar, Pietro Lió, Michael Bronstein
Published in: Proceedings of the 38th International Conference on Machine Learning (Main Conference Track), 2021

Abstract: The pairwise interaction paradigm of graph machine learning has predominantly governed the modelling of relational systems. However, graphs alone cannot capture the multi-level interactions present in many complex systems and the expressive power of such schemes was proven to be limited. To overcome these limitations, we propose Message Passing Simplicial Networks (MPSNs), a class of models that perform message passing on simplicial complexes (SCs). To theoretically analyse the expressivity of our model we introduce a Simplicial Weisfeiler-Lehman (SWL) colouring procedure for distinguishing non-isomorphic SCs. We relate the power of SWL to the problem of distinguishing non-isomorphic graphs and show that SWL and MPSNs are strictly more powerful than the WL test and not less powerful than the 3-WL test. We deepen the analysis by comparing our model with traditional graph neural networks (GNNs) with ReLU activations in terms of the number of linear regions of the functions they can represent. We empirically support our theoretical claims by showing that MPSNs can distinguish challenging strongly regular graphs for which GNNs fail and, when equipped with orientation equivariant layers, they can improve classification accuracy in oriented SCs compared to a GNN baseline.
The Hintons in your Neural Network: a Quantum Field Theory View of Deep Learning
Authors: Roberto Bondesan, Max Welling
Published in: Proceedings of the 38th International Conference on Machine Learning (Main Conference Track), 2021

Abstract: In this work we develop a quantum field theory formalism for deep learning, where input signals are encoded in Gaussian states, a generalization of Gaussian processes which encode the agent’s uncertainty about the input signal. We show how to represent linear and non-linear layers as unitary quantum gates, and interpret the fundamental excitations of the quantum model as particles, dubbed “Hintons”. On top of opening a new perspective and techniques for studying neural networks, the quantum formulation is well suited for optical quantum computing, and provides quantum deformations of neural networks that can be run efficiently on those devices. Finally, we discuss a semi-classical limit of the quantum deformed models which is amenable to classical simulation.
Offline Contextual Bandits with Overparameterized Models
Authors: David Brandfonbrener, William Whitney, Rajesh Ranganath, Joan Bruna
Published in: Proceedings of the 38th International Conference on Machine Learning (Main Conference Track), 2021

Abstract: Recent results in supervised learning suggest that while overparameterized models have the capacity to overfit, they in fact generalize quite well. We ask whether the same phenomenon occurs for offline contextual bandits. Our results are mixed. Value-based algorithms benefit from the same generalization behavior as overparameterized supervised learning, but policy-based algorithms do not. We show that this discrepancy is due to the \emphaction-stability of their objectives. An objective is action-stable if there exists a prediction (action-value vector or action distribution) which is optimal no matter which action is observed. While value-based objectives are action-stable, policy-based objectives are unstable. We formally prove upper bounds on the regret of overparameterized value-based learning and lower bounds on the regret for policy-based algorithms. In our experiments with large neural networks, this gap between action-stable value-based objectives and unstable policy-based objectives leads to significant performance differences.
High-Performance Large-Scale Image Recognition Without Normalization
Authors: Andy Brock, Soham De, Samuel L Smith, Karen Simonyan
Published in: Proceedings of the 38th International Conference on Machine Learning (Main Conference Track), 2021

Abstract: Batch normalization is a key component of most image classification models, but it has many undesirable properties stemming from its dependence on the batch size and interactions between examples. Although recent work has succeeded in training deep ResNets without normalization layers, these models do not match the test accuracies of the best batch-normalized networks, and are often unstable for large learning rates or strong data augmentations. In this work, we develop an adaptive gradient clipping technique which overcomes these instabilities, and design a significantly improved class of Normalizer-Free ResNets. Our smaller models match the test accuracy of an EfficientNet-B7 on ImageNet while being up to 8.7x faster to train, and our largest models attain a new state-of-the-art top-1 accuracy of 86.5%. In addition, Normalizer-Free models attain significantly better performance than their batch-normalized counterparts when fine-tuning on ImageNet after large-scale pre-training on a dataset of 300 million labeled images, with our best models obtaining an accuracy of 89.2%.
Evaluating the Implicit Midpoint Integrator for Riemannian Hamiltonian Monte Carlo
Authors: James Brofos, Roy R Lederman
Published in: Proceedings of the 38th International Conference on Machine Learning (Main Conference Track), 2021

Abstract: Riemannian manifold Hamiltonian Monte Carlo is traditionally carried out using the generalized leapfrog integrator. However, this integrator is not the only choice and other integrators yielding valid Markov chain transition operators may be considered. In this work, we examine the implicit midpoint integrator as an alternative to the generalized leapfrog integrator. We discuss advantages and disadvantages of the implicit midpoint integrator for Hamiltonian Monte Carlo, its theoretical properties, and an empirical assessment of the critical attributes of such an integrator for Hamiltonian Monte Carlo: energy conservation, volume preservation, and reversibility. Empirically, we find that while leapfrog iterations are faster, the implicit midpoint integrator has better energy conservation, leading to higher acceptance rates, as well as better conservation of volume and better reversibility, arguably yielding a more accurate sampling procedure.
Reinforcement Learning of Implicit and Explicit Control Flow Instructions
Authors: Ethan Brooks, Janarthanan Rajendran, Richard L Lewis, Satinder Singh
Published in: Proceedings of the 38th International Conference on Machine Learning (Main Conference Track), 2021

Abstract: Learning to flexibly follow task instructions in dynamic environments poses interesting challenges for reinforcement learning agents. We focus here on the problem of learning control flow that deviates from a strict step-by-step execution of instructions—that is, control flow that may skip forward over parts of the instructions or return backward to previously completed or skipped steps. Demand for such flexible control arises in two fundamental ways: explicitly when control is specified in the instructions themselves (such as conditional branching and looping) and implicitly when stochastic environment dynamics require re-completion of instructions whose effects have been perturbed, or opportunistic skipping of instructions whose effects are already present. We formulate an attention-based architecture that meets these challenges by learning, from task reward only, to flexibly attend to and condition behavior on an internal encoding of the instructions. We test the architecture’s ability to learn both explicit and implicit control in two illustrative domains—one inspired by Minecraft and the other by StarCraft—and show that the architecture exhibits zero-shot generalization to novel instructions of length greater than those in a training set, at a performance level unmatched by three baseline recurrent architectures and one ablation architecture.
Machine Unlearning for Random Forests
Authors: Jonathan Brophy, Daniel Lowd
Published in: Proceedings of the 38th International Conference on Machine Learning (Main Conference Track), 2021

Abstract: Responding to user data deletion requests, removing noisy examples, or deleting corrupted training data are just a few reasons for wanting to delete instances from a machine learning (ML) model. However, efficiently removing this data from an ML model is generally difficult. In this paper, we introduce data removal-enabled (DaRE) forests, a variant of random forests that enables the removal of training data with minimal retraining. Model updates for each DaRE tree in the forest are exact, meaning that removing instances from a DaRE model yields exactly the same model as retraining from scratch on updated data. DaRE trees use randomness and caching to make data deletion efficient. The upper levels of DaRE trees use random nodes, which choose split attributes and thresholds uniformly at random. These nodes rarely require updates because they only minimally depend on the data. At the lower levels, splits are chosen to greedily optimize a split criterion such as Gini index or mutual information. DaRE trees cache statistics at each node and training data at each leaf, so that only the necessary subtrees are updated as data is removed. For numerical attributes, greedy nodes optimize over a random subset of thresholds, so that they can maintain statistics while approximating the optimal threshold. By adjusting the number of thresholds considered for greedy nodes, and the number of random nodes, DaRE trees can trade off between more accurate predictions and more efficient updates. In experiments on 13 real-world datasets and one synthetic dataset, we find DaRE forests delete data orders of magnitude faster than retraining from scratch while sacrificing little to no predictive power.
Value Alignment Verification
Authors: Daniel S Brown, Jordan Schneider, Anca Dragan, Scott Niekum
Published in: Proceedings of the 38th International Conference on Machine Learning (Main Conference Track), 2021

Abstract: As humans interact with autonomous agents to perform increasingly complicated, potentially risky tasks, it is important to be able to efficiently evaluate an agent’s performance and correctness. In this paper we formalize and theoretically analyze the problem of efficient value alignment verification: how to efficiently test whether the behavior of another agent is aligned with a human’s values? The goal is to construct a kind of "driver’s test" that a human can give to any agent which will verify value alignment via a minimal number of queries. We study alignment verification problems with both idealized humans that have an explicit reward function as well as problems where they have implicit values. We analyze verification of exact value alignment for rational agents, propose and test heuristics for value alignment verification in gridworlds and a continuous autonomous driving domain, and prove that there exist sufficient conditions such that we can verify epsilon-alignment in any environment via a constant-query-complexity alignment test.
Model-Free and Model-Based Policy Evaluation when Causality is Uncertain
Authors: David A Bruns-Smith
Published in: Proceedings of the 38th International Conference on Machine Learning (Main Conference Track), 2021

Abstract: When decision-makers can directly intervene, policy evaluation algorithms give valid causal estimates. In off-policy evaluation (OPE), there may exist unobserved variables that both impact the dynamics and are used by the unknown behavior policy. These “confounders” will introduce spurious correlations and naive estimates for a new policy will be biased. We develop worst-case bounds to assess sensitivity to these unobserved confounders in finite horizons when confounders are drawn iid each period. We demonstrate that a model-based approach with robust MDPs gives sharper lower bounds by exploiting domain knowledge about the dynamics. Finally, we show that when unobserved confounders are persistent over time, OPE is far more difficult and existing techniques produce extremely conservative bounds.
Narrow Margins: Classification, Margins and Fat Tails
Authors: Francois Buet-Golfouse
Published in: Proceedings of the 38th International Conference on Machine Learning (Main Conference Track), 2021

Abstract: It is well-known that, for separable data, the regularised two-class logistic regression or support vector machine re-normalised estimate converges to the maximal margin classifier as the regularisation hyper-parameter $łambda$ goes to 0. The fact that different loss functions may lead to the same solution is of theoretical and practical relevance as margin maximisation allows more straightforward considerations in terms of generalisation and geometric interpretation. We investigate the case where this convergence property is not guaranteed to hold and show that it can be fully characterised by the distribution of error terms in the latent variable interpretation of linear classifiers. In particular, if errors follow a regularly varying distribution, then the regularised and re-normalised estimate does not converge to the maximal margin classifier. This shows that classification with fat tails has a qualitatively different behaviour, which should be taken into account when considering real-life data.
Differentially Private Correlation Clustering
Authors: Mark Bun, Marek Elias, Janardhan Kulkarni
Published in: Proceedings of the 38th International Conference on Machine Learning (Main Conference Track), 2021

Abstract: Correlation clustering is a widely used technique in unsupervised machine learning. Motivated by applications where individual privacy is a concern, we initiate the study of differentially private correlation clustering. We propose an algorithm that achieves subquadratic additive error compared to the optimal cost. In contrast, straightforward adaptations of existing non-private algorithms all lead to a trivial quadratic error. Finally, we give a lower bound showing that any pure differentially private algorithm for correlation clustering requires additive error $Ømega$(n).
Disambiguation of Weak Supervision leading to Exponential Convergence rates
Authors: Vivien A Cabannnes, Francis Bach, Alessandro Rudi
Published in: Proceedings of the 38th International Conference on Machine Learning (Main Conference Track), 2021

Abstract: Machine learning approached through supervised learning requires expensive annotation of data. This motivates weakly supervised learning, where data are annotated with incomplete yet discriminative information. In this paper, we focus on partial labelling, an instance of weak supervision where, from a given input, we are given a set of potential targets. We review a disambiguation principle to recover full supervision from weak supervision, and propose an empirical disambiguation algorithm. We prove exponential convergence rates of our algorithm under classical learnability assumptions, and we illustrate the usefulness of our method on practical examples.
Finite mixture models do not reliably learn the number of components
Authors: Diana Cai, Trevor Campbell, Tamara Broderick
Published in: Proceedings of the 38th International Conference on Machine Learning (Main Conference Track), 2021

Abstract: Scientists and engineers are often interested in learning the number of subpopulations (or components) present in a data set. A common suggestion is to use a finite mixture model (FMM) with a prior on the number of components. Past work has shown the resulting FMM component-count posterior is consistent; that is, the posterior concentrates on the true, generating number of components. But consistency requires the assumption that the component likelihoods are perfectly specified, which is unrealistic in practice. In this paper, we add rigor to data-analysis folk wisdom by proving that under even the slightest model misspecification, the FMM component-count posterior diverges: the posterior probability of any particular finite number of components converges to 0 in the limit of infinite data. Contrary to intuition, posterior-density consistency is not sufficient to establish this result. We develop novel sufficient conditions that are more realistic and easily checkable than those common in the asymptotics literature. We illustrate practical consequences of our theory on simulated and real data.
A Theory of Label Propagation for Subpopulation Shift
Authors: Tianle Cai, Ruiqi Gao, Jason Lee, Qi Lei
Published in: Proceedings of the 38th International Conference on Machine Learning (Main Conference Track), 2021

Abstract: One of the central problems in machine learning is domain adaptation. Different from past theoretical works, we consider a new model of subpopulation shift in the input or representation space. In this work, we propose a provably effective framework based on label propagation by using an input consistency loss. In our analysis we used a simple but realistic “expansion” assumption, which has been proposed in i̧tetwei2021theoretical. It turns out that based on a teacher classifier on the source domain, the learned classifier can not only propagate to the target domain but also improve upon the teacher. By leveraging existing generalization bounds, we also obtain end-to-end finite-sample guarantees on deep neural networks. In addition, we extend our theoretical framework to a more general setting of source-to-target transfer based on an additional unlabeled dataset, which can be easily applied to various learning scenarios. Inspired by our theory, we adapt consistency-based semi-supervised learning methods to domain adaptation settings and gain significant improvements.
Lenient Regret and Good-Action Identification in Gaussian Process Bandits
Authors: Xu Cai, Selwyn Gomes, Jonathan Scarlett
Published in: Proceedings of the 38th International Conference on Machine Learning (Main Conference Track), 2021

Abstract: In this paper, we study the problem of Gaussian process (GP) bandits under relaxed optimization criteria stating that any function value above a certain threshold is “good enough”. On the theoretical side, we study various \em lenient regret notions in which all near-optimal actions incur zero penalty, and provide upper bounds on the lenient regret for GP-UCB and an elimination algorithm, circumventing the usual $O(\sqrtT)$ term (with time horizon $T$) resulting from zooming extremely close towards the function maximum. In addition, we complement these upper bounds with algorithm-independent lower bounds. On the practical side, we consider the problem of finding a single “good action” according to a known pre-specified threshold, and introduce several good-action identification algorithms that exploit knowledge of the threshold. We experimentally find that such algorithms can typically find a good action faster than standard optimization-based approaches.
A Zeroth-Order Block Coordinate Descent Algorithm for Huge-Scale Black-Box Optimization
Authors: Hanqin Cai, Yuchen Lou, Daniel Mckenzie, Wotao Yin
Published in: Proceedings of the 38th International Conference on Machine Learning (Main Conference Track), 2021

Abstract: We consider the zeroth-order optimization problem in the huge-scale setting, where the dimension of the problem is so large that performing even basic vector operations on the decision variables is infeasible. In this paper, we propose a novel algorithm, coined ZO-BCD, that exhibits favorable overall query complexity and has a much smaller per-iteration computational complexity. In addition, we discuss how the memory footprint of ZO-BCD can be reduced even further by the clever use of circulant measurement matrices. As an application of our new method, we propose the idea of crafting adversarial attacks on neural network based classifiers in a wavelet domain, which can result in problem dimensions of over one million. In particular, we show that crafting adversarial examples to audio classifiers in a wavelet domain can achieve the state-of-the-art attack success rate of 97.9% with significantly less distortion.