--- title: "We have forgotten about utility functions in BO (whoops!)" date: 2026-01-27T00:00Z tags: - machine learning - bayesian optimization - opinion - _recent-highlight has_math: true --- Bayesian decision theory is one of the best justifications for BO- particularly for myopic acquisition functions like expected improvement. However, these acquisition functions are only "optimal" if one's utility function is $u(y) = y$ (the identity function). Have BO researchers (and BO users) basically _forgotten_ to swap this out for "real" utility functions in practice? In this post I argue that we have basically overlooked this detail (to our own detriment). It's not _too_ hard to fix, but unfortunately the $u(y) = y$ assumption is actually quite deeply embedded, and completely removing it will make things more complicated. Ultimately, despite the difficulty, I think we should do it anyway. ## Primer: how the utility assumption ends up in expected improvement I will use the _expected improvement_ (EI) acquisition function as a placeholder for utility-based acquisition functions in general, and present a quick derivation. A better and more thorough derivation can be found in §5.2 of "Bayesian Optimization" by Garnett.[^bayesoptbook] [^bayesoptbook]: Website here: . **Problem setting.** We are maximizing a noiseless function $y = f(x)$. We have an evaluation history $\{{(x_1, y_1), \ldots, (x_n, y_n)\}}$. We can query 1 more x location: what should we do? **Utility Functions** the answer depends on what we want. There is an element of risk/reward trade-off (eg should we do a risky evaluation which might be very large or a modest point which we are more confident about), and also some ambiguity about how the previous solutions should affect our decisions. EI starts by making an innocent assumption: we only care about the _single best point_ we evaluate. This means there is an incumbent best point $$y^* = \max_{i\leq1\leq n} y_i$$ and, if our final evaluation returns $y_{n+1} < y^\*$ then that's ok, we will just go with $y^*$. Otherwise we will go with $y_{n+1}$. We can only _gain_ from the final evaluation, not lose anything. **Bayesian decision theory.** Next, Bayesian decision theory says that we should make this decision by maximizing _expected utility_, which maps each outcome through a utility function $u$ that says how "good" the outcome is. Assuming $u(y_1, \ldots, y_n, y_{n+1})=\max_i y_i$ and doing the math, you get an expected utility $$ E_{y_{n+1}} \left[ \max\left(0, y_{n+1}-y^* \right)\right]$$ which is the expected amount of improvement (hence the name expected improvement). **Summary.** The utility function appears when Bayesian decision theory is invoked, and the utility of a _set_ of solutions is just the maximum value in the set. ## What's wrong with this utility function? Bayesian decision theory states that preferences over uncertain outcomes can be cast as maximizing expected utility of _some_ utility function: ie for any given set of preferences there must exist a utility function. It _does not_ say that if you pick a utility function and maximize expected utility you will be acting according to your preferences. Therefore, the utility function fundamentally must be an _input_ to the problem, not something that can be arbitrarily set. Digging into the assumptions behind expected utility a bit further,[^expectedutilitypost] the utility function's values basically play 2 roles: [^expectedutilitypost]: See [this post](/blog/2025-11-02-expected-utility/) for more details on these assuptions. 1. It _orders_ the outcomes (saying which outcomes are better and worse, and which are equivalent). 2. It defines the user's _risk reward_ over outcomes. Assuming $u(A) < u(B) < u(C)$ it assumes the user would be equally ok with receiving B with 100% chance or taking the gamble "A with probability p, otherwise C" where $p = \left(u(C) - u(B)\right) / \left(u(C) - u(A) \right)$. $u(y) = y$ obviously correctly orders outcomes in the case of maximization (where higher is always better), but who knows what the user's risk-reward trade-off actually is? The bet with probability p is a highly specific assumption that seems highly unlikely to hold for users in general. To make it a bit more concrete, let's assume $y^* = 1$ (ie best score found so far is 1). If presented with the options: 1. 1.5 (100% probability) 2. 0.5 (50% probability), 2.1 (50% probability) 3. 0.9 (90% probability), 7 (10% probability) Expected improvement under the standard utility function would choose option 3 because it has the highest expected improvement, even though it is the riskiest option.[^EIvalues] To give a stylized real-world example of where this is might be unreasonable: in drug discovery there are often competitor drugs, and one's drug needs to be _better_ in order to get approved. If the competitor drug has a potency of 1, then _matching_ them doesn't succeed: you need to be better. I bet many decision makers would prefer option 1 in this case: it gets you "over the line" with certainty. Their utility function would probably be flat before $y=1$, then increase. [^EIvalues]: EI values are 1: 0.5 2: 0.55 3: 0.6. ## How did this utility function come to be used everywhere? I'm not sure- and don't really have time to go to the literature to find out. My guess is just a by-product of how papers are written. Academic papers aren't really "users" of the algorithms so they don't have strong preferences that would form the basis for a utility function. At the same time they need to make _some_ choice to run experiments, and a choice like $u(y) = y$ seems more "neutral" than something like $u(y) = \min\left(y, 3\sqrt{y} -1\right)$, plus it allows EI to be computed analytically, so authors just go with it. Authors of other papers then reimplement the method and use the implementation in the original paper with $u(y)=y$. Over time, the original utility function gets forgotten and users concentrate on the case that is actually implemented and studied. My overall point is that using $u(y)=y$ was probably _not_ a deliberate choice or statement about how BO methods should be used in general, it was just a simple design choice that stuck around. ## Warped GPs are not the answer A plausible-sounding workaround is the following: instead of creating a surrogate model of the unknown function f(x), create a surrogate model of $g(x)=u(f(x))$ and run BO with this. After all, $g(x)$ is effectively just another black-box function. This model, commonly called a _warped GP_[^snelson2003], might seem to be the ideal solution: it uses the utility function without needing to adjust the underlying BO algorithm. Problem solved? [^snelson2003]: Original paper: [Warped Gaussian Processes, NeurIPS 2003](https://proceedings.neurips.cc/paper/2003/hash/6b5754d737784b51ec5075c0dc437bf0-Abstract.html). Unfortunately I think not. The main issue I foresee is that real-world utility functions u might violate a lot of assumptions commonly made about surrogate models: for example, differentiability, continuity, or stationarity. For example, consider the drug discovery example I gave earlier about finding molecules that bind better than a competitor's molecule. A lot of molecules with binding ≤ 1 will have a utility score of exactly 0, with a sudden jump in g once binding exceeds 1. This is probably hard to fit a model to: it turns what might be a smooth relationship between structure and binding into a discrete jump, and probably masks the learning signal because a lot of improvements in binding (eg 0.7 -> 0.8 -> 0.9) will just look like a constant zero. It's also probably harder to fit a good noise model in utility space. An actual binding measurement probably has (somewhat) Gaussian noise of a similar magnitude for most binding strengths. However, mapped through a utility function to give noise on g, the noise would be highly non-Gaussian and therefore harder to model. A secondary issue is that such a model goes against the decision-theoretic roots of BO, which fundamentally posits that outcomes and preferences are conceptually separate. I think this separation is one of BO's biggest strengths, especially when compared to other methods (like generative models) which mix preferences and beliefs together in a confusing way (and are thus a bit harder to tune and use). Of course we can abandon principled appeals to theory, it's not _impossible_ to overcome these modelling difficulties. In this sense warped GPs _could_ work. The main point I want to convey is that they are a difficult choice that comes with a lot of "hidden" drawbacks, and therefore we (the BO community) shouldn't just accept them as the sole solution and move on. ## Properly supporting more general utility functions might be hard If we stick to the principles of Bayesian decision theory, the "right" way to handle utility functions is as an _input_ to the BO loop. A surrogate model is constructed, and the acquisition function should be computed on the _utilities_ of the output, not the raw outputs themselves. Unfortunately this introduces a lot of potential difficulties. To start, what restrictions should exist on u, if any? If u is itself a black box function then something like numerical integration would be necessary to evaluate the acquisition function at any point. That adds a lot of computation cost, and that's just for a _pointwise_ estimation, not even the _gradient_ $\nabla_x \alpha(x)$. I think we can't totally dismiss this worst-case scenario, but intuitively it feels like most users should be happy with _analytic_ utility functions, possibly even ones which are continuous, and maybe even differentiable (almost everywhere). This includes functions like: - $u(y) = y$ - $u(y) = \sqrt{y}$ - $u(y) = \min\left(y, 3\sqrt{y}-1\right)$ This feels like a pretty broad class of functions for users to express their preferences with, and one which is more amenable to BO. Acquisition values may still need to be computed with numerical integration, but I bet this could be partially cached or done analytically (at least with Gaussian posteriors). You should even be able to compute acquisition function gradients using the analytic derivative of u. However, even in this more optimistic case BO software would look a lot more complicated: essentially every acquisition function would have custom computational requirements which need to be specified by the user (because having BO researchers provide out of the box utility functions sort of defeats the point of the user providing this as an input). ## Summary: this is tough but worth it Given how hard it would be to retrofit user-provided utility functions onto BO, should we (the BO research community) just not do it? While the lazy option is certainly tempting, I think at least _some_ people in the BO community should work on this. Here is what I think pursuing this could lead to: - A way for the user to input their risk/reward preferences, giving them more direct control over the explore/exploit behaviours of BO algorithms (something which many users would like). - A more principled starting point for cost-aware BO, which usually needs to assume a value-cost trade off. - Automatically the cases of maximization, minimization, and targeting a specific value get handled in a unified way (via the utility function). No more confusing the EI equations for maximization vs minimization! - A utility-based framework naturally generalizes to multi-objective optimization (the utility function is basically a kind of scalarization). Overall I think there is potentially for at least one good JMLR-style paper here, or a NeurIPS/ICML/ICLR paper introducing a software library supporting arbitrary utility functions for BO. Plus, there are some acquisition functions without a clear grounding in Bayesian decision theory (eg UCB), and some theoretical work putting utility functions in would be welcome.[^ucb] If any of this sounds interesting, feel free to get in touch with me. [^ucb]: Eg for UCB I imagine the equivalent notion would be an upper bound on the _utility_ (rather than an upper bound on the function), but I'm not sure if all the desirable properties of UCB would transfer to this case. **Final thought**: remember, in Bayesian ML we do things not because they are easy, but because they are _right_.